From fbd88746263fec0f72f2f01bcc382e88e902ab50 Mon Sep 17 00:00:00 2001
From: Ramesh Sampath <rameshsampath@google.com>
Date: Fri, 19 Apr 2024 04:39:34 -0700
Subject: [PATCH 01/47] Update keras import to fix build error.

`from keras import ops` or `import keras` are valid, but `import keras.ops` is no longer valid since ops is not a module in keras namespace, but points to the public API.

PiperOrigin-RevId: 626326912
---
 lit_nlp/examples/models/instrumented_keras_lms.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/lit_nlp/examples/models/instrumented_keras_lms.py b/lit_nlp/examples/models/instrumented_keras_lms.py
index 44236311..f9b4cbcd 100644
--- a/lit_nlp/examples/models/instrumented_keras_lms.py
+++ b/lit_nlp/examples/models/instrumented_keras_lms.py
@@ -6,8 +6,7 @@
 from typing import Sequence
 
 from absl import logging
-import keras.backend
-import keras.ops
+import keras
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
 from lit_nlp.lib import utils as lit_utils

From 483082dcb0beb39795c0fc093fe93036bb6a274c Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Fri, 26 Apr 2024 13:12:40 -0700
Subject: [PATCH 02/47] LIT: Refactor DataService reactions.

PiperOrigin-RevId: 628490534
---
 lit_nlp/client/services/data_service.ts | 90 ++++++++++++++-----------
 1 file changed, 50 insertions(+), 40 deletions(-)

diff --git a/lit_nlp/client/services/data_service.ts b/lit_nlp/client/services/data_service.ts
index 114fae3d..354702bf 100644
--- a/lit_nlp/client/services/data_service.ts
+++ b/lit_nlp/client/services/data_service.ts
@@ -95,40 +95,39 @@ export class DataService extends LitService {
       this.columnData.clear();
     });
 
-    // Run classification interpreter when the inputs or margins change.
-    const getClassificationInputs = () =>
-        [this.appState.currentInputData, this.appState.currentModels,
-         this.classificationService.allMarginSettings];
-    reaction(getClassificationInputs, () => {
-      if (this.appState.currentInputData == null ||
-          this.appState.currentInputData.length === 0 ||
-          this.appState.currentModels.length === 0 ||
-          !this.settingsService.isValidCurrentDataAndModels) {
-        return;
-      }
-      for (const model of this.appState.currentModels) {
-        this.runClassification(model, this.appState.currentInputData);
-      }
-    }, {fireImmediately: true});
-
-    // Run other prediction interpreters when necessary.
-    const getPredictionInputs =
-        () => [this.appState.currentInputData, this.appState.currentModels];
-    reaction(getPredictionInputs, () => {
-      if (this.appState.currentInputData == null ||
-          this.appState.currentInputData.length === 0 ||
-          this.appState.currentModels.length === 0 ||
-          !this.settingsService.isDatasetValidForModels(
-              this.appState.currentDataset, this.appState.currentModels)) {
-        return;
-      }
-      for (const model of this.appState.currentModels) {
-        this.runGeneratedTextPreds(model, this.appState.currentInputData);
-        this.runRegression(model, this.appState.currentInputData);
-        this.runScalarPreds(model, this.appState.currentInputData);
-        this.runMultiLabelPreds(model, this.appState.currentInputData);
-      }
-    }, {fireImmediately: true});
+    // Fetch predicitons when the current dataset or model changes.
+    reaction(
+      () => [
+        this.appState.currentInputData, this.appState.currentModels
+      ] as const,
+      ([currentInputData, currentModels]) => {
+        if (!currentInputData?.length ||
+            !currentModels.length ||
+            !this.settingsService.isValidCurrentDataAndModels) {
+          return;
+        }
+        for (const model of currentModels) {
+          this.runClassification(model, currentInputData);
+          this.runGeneratedTextPreds(model, currentInputData);
+          this.runMultiLabelPreds(model, currentInputData);
+          this.runRegression(model, currentInputData);
+          this.runScalarPreds(model, currentInputData);
+        }
+      },
+      {fireImmediately: true}
+    );
+
+    // Additonally, run the classification interpreter when the margins change.
+    reaction(
+      () => this.classificationService.allMarginSettings,
+      () => {
+        if (!this.settingsService.isValidCurrentDataAndModels) {return;}
+        for (const model of this.appState.currentModels) {
+          this.runClassification(model, this.appState.currentInputData);
+        }
+      },
+      {fireImmediately: true}
+    );
 
     this.appState.addNewDatapointsCallback(async (newDatapoints) =>
       this.setValuesForNewDatapoints(newDatapoints));
@@ -403,9 +402,14 @@ export class DataService extends LitService {
    */
   @action
   addColumn(
-      columnVals: ColumnData, key: string, name: string, dataType: LitType,
-      source: Source, getValueFn: ValueFn = () => null,
-      colorRange?: ColorRange) {
+      columnVals: ColumnData,
+      key: string,
+      name: string,
+      dataType: LitType,
+      source: Source,
+      getValueFn: ValueFn = () => null,
+      colorRange?: ColorRange
+  ) {
     if (!this.columnHeaders.has(name)) {
       this.columnHeaders.set(
           name, {dataType, source, name, key, getValueFn, colorRange});
@@ -426,9 +430,15 @@ export class DataService extends LitService {
    */
   @action
   addColumnFromList(
-      values: ValueType[], data: IndexedInput[], key: string, name: string,
-      dataType: LitType, source: Source, getValueFn: ValueFn = () => null,
-      colorRange?: ColorRange) {
+      values: ValueType[],
+      data: IndexedInput[],
+      key: string,
+      name: string,
+      dataType: LitType,
+      source: Source,
+      getValueFn: ValueFn = () => null,
+      colorRange?: ColorRange
+  ) {
     if (values.length !== data.length) {
       throw new Error(`Attempted to add data column ${
           name} with incorrect number of values.`);

From a5265a4feeb701b878986f79665d5fdf9ddc244c Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Fri, 26 Apr 2024 13:27:39 -0700
Subject: [PATCH 03/47] LIT: Add warm_start option to LitWidget

PiperOrigin-RevId: 628494490
---
 lit_nlp/notebook.py | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/lit_nlp/notebook.py b/lit_nlp/notebook.py
index e3996529..aae2eb9a 100644
--- a/lit_nlp/notebook.py
+++ b/lit_nlp/notebook.py
@@ -95,13 +95,16 @@ def _encode(v):
 class LitWidget(object):
   """Class for using LIT inside notebooks."""
 
-  def __init__(self,
-               *args,
-               height=1000,
-               render=False,
-               proxy_url=None,
-               layouts: Optional[layout.LitComponentLayouts] = None,
-               **kw):
+  def __init__(
+      self,
+      *args,
+      height=1000,
+      render=False,
+      proxy_url=None,
+      layouts: Optional[layout.LitComponentLayouts] = None,
+      warm_start: bool = False,
+      **kw,
+  ):
     """Start LIT server and optionally render the UI immediately.
 
     Args:
@@ -111,15 +114,16 @@ def __init__(self,
         to False.
       proxy_url: Optional proxy URL, if using in a notebook with a server proxy.
         Defaults to None.
-      layouts: Optional custom UI layouts. TODO(lit-dev): support simple module
-        lists here as well.
+      layouts: Optional custom UI layouts.
+      warm_start: If true, run predictions for every model on every compatible
+        dataset before returning a renderable widget.
       **kw: Keyword arguments for the LitApp.
     """
     app_flags = dict(server_config.get_flags())
     app_flags['server_type'] = 'notebook'
     app_flags['host'] = 'localhost'
     app_flags['port'] = None
-    app_flags['warm_start'] = 1
+    app_flags['warm_start'] = 1 if warm_start else 0
     app_flags['warm_start_progress_indicator'] = progress_indicator
     app_flags['sync_state'] = True
 

From 4fb3bde897c68fdeb3bd829f6e5a88223bc131a4 Mon Sep 17 00:00:00 2001
From: Ian Tenney <iftenney@google.com>
Date: Tue, 30 Apr 2024 17:09:01 -0700
Subject: [PATCH 04/47] Pretty-printing of Model objects

PiperOrigin-RevId: 629571604
---
 lit_nlp/api/model.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/lit_nlp/api/model.py b/lit_nlp/api/model.py
index ce034a20..49a68649 100644
--- a/lit_nlp/api/model.py
+++ b/lit_nlp/api/model.py
@@ -89,6 +89,18 @@ def description(self) -> str:
     """
     return inspect.getdoc(self) or ''
 
+  def __str__(self) -> str:
+    classname = self.__class__.__module__ + '.' + self.__class__.__qualname__
+    indented_description = '  ' + self.description().replace('\n', '\n  ')
+    return f'{classname}(...):\n{indented_description}'
+
+  def _repr_pretty_(self, p, cycle):
+    """Pretty-printing for IPython environments, both notebooks and repl."""
+    if not cycle:
+      p.text(str(self))
+    else:
+      p.text('...')
+
   @classmethod
   def init_spec(cls) -> Optional[Spec]:
     """Attempts to infer a Spec describing a Model's constructor parameters.

From 675ca2de21b68dc62e4909c80a2cd57d8ee8b601 Mon Sep 17 00:00:00 2001
From: Bin Du <dubin@google.com>
Date: Fri, 10 May 2024 14:56:23 -0700
Subject: [PATCH 05/47] Add the helper function in Keras setup to clean up
 special tokens in GTP2 and SentencePiece tokenizer.

PiperOrigin-RevId: 632611251
---
 .../examples/models/instrumented_keras_lms.py | 33 +++++++++++++++----
 1 file changed, 26 insertions(+), 7 deletions(-)

diff --git a/lit_nlp/examples/models/instrumented_keras_lms.py b/lit_nlp/examples/models/instrumented_keras_lms.py
index f9b4cbcd..b3bdb41c 100644
--- a/lit_nlp/examples/models/instrumented_keras_lms.py
+++ b/lit_nlp/examples/models/instrumented_keras_lms.py
@@ -10,7 +10,6 @@
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
 from lit_nlp.lib import utils as lit_utils
-import numpy as np
 
 
 # pylint: disable=g-import-not-at-top
@@ -87,10 +86,6 @@ def __init__(
     self.max_length = max_length
     self.dynamic_sequence_length = dynamic_sequence_length
 
-    self.ids_to_tokens = np.vectorize(
-        self.model.preprocessor.tokenizer.id_to_token
-    )
-
     # map ids: <tf.int>[batch_size, num_tokens]
     # to embs: <tf.float>[batch_size, num_tokens, emb_dim]
     self.embedder = self.model.backbone.token_embedding
@@ -141,6 +136,30 @@ def encode_inputs(self, texts: Sequence[str]):
     # Actually trim the input tensors.
     return {k: v[:, :longest_sequence] for k, v in encoded_inputs.items()}
 
+  def clean_subword_token(self, tok: str) -> str:
+    """Clean up special subword token from the tokenizers if necessary.
+
+    Args:
+      tok: the token to clean up.
+    Returns:
+      The replaced token if the provided token matches the special subword token
+      below; otherwise, the original token is returned.
+    """
+    # For GPT2 tokenizer.
+    tok = tok.replace("Ċ", "\n")  # newlines
+    tok = tok.replace("Ġ", "▁")  # start of word -> magic underscore
+    # For SentencePiece Tokenizer.
+    tok = tok.replace("<0x0A>", "\n")  # newlines
+    return tok
+
+  def ids_to_clean_tokens(self, ids: Sequence[int]) -> Sequence[str]:
+    return [
+        self.clean_subword_token(
+            self.model.preprocessor.tokenizer.id_to_token(id)
+        )
+        for id in ids
+    ]
+
   @classmethod
   def from_loaded(cls, existing: "_KerasBaseModel", *args, **kw):
     """Share weights and underlying Keras model with another instance."""
@@ -419,7 +438,7 @@ def _postprocess(self, preds):
     """Post-process single-example preds. Operates on numpy arrays."""
     mask = preds.pop("padding_mask").astype(bool)
     ids = preds.pop("input_ids")[mask]
-    preds[FieldNames.TOKENS] = self.ids_to_tokens(ids)
+    preds[FieldNames.TOKENS] = self.ids_to_clean_tokens(ids)
     for key in lit_utils.find_spec_keys(
         self.output_spec(), lit_types.TokenScores
     ):
@@ -479,7 +498,7 @@ def _postprocess(self, preds):
     # rather than acting as a boolean mask.
     mask = preds.pop("padding_mask").astype(bool)
     ids = preds.pop("token_ids")[mask]
-    preds[FieldNames.TOKENS] = self.ids_to_tokens(ids)
+    preds[FieldNames.TOKENS] = self.ids_to_clean_tokens(ids)
     return preds
 
   def predict_minibatch(self, inputs):

From b16059fbd0320d411298009c0226489e1f548a69 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 22 May 2024 13:35:37 -0700
Subject: [PATCH 06/47] Rename lm_salience_module to sequence_salience_module

PiperOrigin-RevId: 636277468
---
 lit_nlp/api/layout.py                         | 22 +++++++-------
 ...odule.css => sequence_salience_module.css} |  0
 ..._module.ts => sequence_salience_module.ts} | 30 +++++++++----------
 lit_nlp/examples/lm_salience_demo.py          | 21 ++++++++-----
 requirements_core.txt                         |  2 +-
 5 files changed, 41 insertions(+), 34 deletions(-)
 rename lit_nlp/client/modules/{lm_salience_module.css => sequence_salience_module.css} (100%)
 rename lit_nlp/client/modules/{lm_salience_module.ts => sequence_salience_module.ts} (97%)

diff --git a/lit_nlp/api/layout.py b/lit_nlp/api/layout.py
index 55e41e6d..be67215e 100644
--- a/lit_nlp/api/layout.py
+++ b/lit_nlp/api/layout.py
@@ -29,19 +29,15 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   Entries should map the TypeScript class name to the HTML element name,
   as declared in HTMLElementTagNameMap in the .ts file defining each LitModule.
   """
-  AnnotatedTextModule = 'annotated-text-module'
+  # keep-sorted start
   AnnotatedTextGoldModule = 'annotated-text-gold-module'
+  AnnotatedTextModule = 'annotated-text-module'
   AttentionModule = 'attention-module'
   ClassificationModule = 'classification-module'
   ConfusionMatrixModule = 'confusion-matrix-module'
   CurvesModule = 'curves-module'
   DataTableModule = 'data-table-module'
-  SimpleDataTableModule = 'simple-data-table-module'
   DatapointEditorModule = 'datapoint-editor-module'
-  # Non-replicating version of Datapoint Editor
-  SingleDatapointEditorModule = 'single-datapoint-editor-module'
-  # Simplified, non-replicating version of Datapoint Editor
-  SimpleDatapointEditorModule = 'simple-datapoint-editor-module'
   DiveModule = 'dive-module'
   DocumentationModule = 'documentation-module'
   EmbeddingsModule = 'embeddings-module'
@@ -50,7 +46,7 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   GeneratedTextModule = 'generated-text-module'
   GeneratorModule = 'generator-module'
   LanguageModelPredictionModule = 'lm-prediction-module'
-  LMSalienceModule = 'lm-salience-module'
+  LegacySequenceSalienceModule = 'legacy-sequence-salience-module'
   MetricsModule = 'metrics-module'
   MultilabelModule = 'multilabel-module'
   PdpModule = 'pdp-module'
@@ -58,14 +54,20 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   SalienceClusteringModule = 'salience-clustering-module'
   SalienceMapModule = 'salience-map-module'
   ScalarModule = 'scalar-module'
-  LegacySequenceSalienceModule = 'legacy-sequence-salience-module'
+  SequenceSalienceModule = 'sequence-salience-module'
+  SimpleDataTableModule = 'simple-data-table-module'
+  # Simplified, non-replicating version of Datapoint Editor
+  SimpleDatapointEditorModule = 'simple-datapoint-editor-module'
+  # Non-replicating version of Datapoint Editor
+  SingleDatapointEditorModule = 'single-datapoint-editor-module'
   SpanGraphGoldModule = 'span-graph-gold-module'
-  SpanGraphModule = 'span-graph-module'
   SpanGraphGoldModuleVertical = 'span-graph-gold-module-vertical'
+  SpanGraphModule = 'span-graph-module'
   SpanGraphModuleVertical = 'span-graph-module-vertical'
   TCAVModule = 'tcav-module'
-  TrainingDataAttributionModule = 'tda-module'
   ThresholderModule = 'thresholder-module'
+  TrainingDataAttributionModule = 'tda-module'
+  # keep-sorted end
 
   def __call__(self, **kw):
     return ModuleConfig(self.value, **kw)
diff --git a/lit_nlp/client/modules/lm_salience_module.css b/lit_nlp/client/modules/sequence_salience_module.css
similarity index 100%
rename from lit_nlp/client/modules/lm_salience_module.css
rename to lit_nlp/client/modules/sequence_salience_module.css
diff --git a/lit_nlp/client/modules/lm_salience_module.ts b/lit_nlp/client/modules/sequence_salience_module.ts
similarity index 97%
rename from lit_nlp/client/modules/lm_salience_module.ts
rename to lit_nlp/client/modules/sequence_salience_module.ts
index 40f9a8e6..68f38450 100644
--- a/lit_nlp/client/modules/lm_salience_module.ts
+++ b/lit_nlp/client/modules/sequence_salience_module.ts
@@ -1,5 +1,5 @@
 /**
- * @fileoverview Custom viz module for causal LM salience.
+ * @fileoverview Custom viz module for sequence salience with causal LMs.
  */
 
 import '@material/mwc-icon';
@@ -26,7 +26,7 @@ import {cleanSpmText, groupTokensByRegexPrefix, groupTokensByRegexSeparator} fro
 import {type IndexedInput, type Preds, SCROLL_SYNC_CSS_CLASS, type Spec} from '../lib/types';
 import {cumSumArray, filterToKeys, findSpecKeys, groupAlike, makeModifiedInput, sumArray} from '../lib/utils';
 
-import {styles} from './lm_salience_module.css';
+import {styles} from './sequence_salience_module.css';
 
 /**
  * Max of absolute value
@@ -133,10 +133,10 @@ export class SingleExampleSingleModelModule extends LitModule {
 }
 
 /**
- * Custom styled version of <lit-token-chips> for rendering LM salience tokens.
+ * Custom styles for <lit-token-chips> to render sequence salience tokens.
  */
-@customElement('lm-salience-chips')
-class LMSalienceChips extends TextChips {
+@customElement('sequence-salience-chips')
+class SequenceSalienceChips extends TextChips {
   @property({type: Boolean}) underline = false;
 
   override holderClass() {
@@ -205,8 +205,8 @@ const CMAP_DEFAULT_RANGE = 0.4;
 const DEFAULT_CUSTOM_SEGMENTATION_REGEX = '\\n+';
 
 /** LIT module for model output. */
-@customElement('lm-salience-module')
-export class LMSalienceModule extends SingleExampleSingleModelModule {
+@customElement('sequence-salience-module')
+export class SequenceSalienceModule extends SingleExampleSingleModelModule {
   static override title = 'Sequence Salience';
   static override numCols = 6;  // 60% of screen width if DataTable on left
   static override duplicateAsRow = true;
@@ -215,9 +215,9 @@ export class LMSalienceModule extends SingleExampleSingleModelModule {
       model: string,
       selectionServiceIndex: number,
       shouldReact: number,
-      ) => html`<lm-salience-module model=${model} .shouldReact=${shouldReact}
+      ) => html`<sequence-salience-module model=${model} .shouldReact=${shouldReact}
                   selectionServiceIndex=${selectionServiceIndex}>
-                </lm-salience-module>`;
+                </sequence-salience-module>`;
 
   static override get styles() {
     return [sharedStyles, styles];
@@ -561,7 +561,7 @@ export class LMSalienceModule extends SingleExampleSingleModelModule {
     });
 
     // If selected example OR selected target string change.
-    // NOTE: you may see a console warning: "Element lm-salience-module
+    // NOTE: you may see a console warning: "Element sequence-salience-module
     // scheduled an update (generally because a property was set) after an
     // update completed, causing a new update to be scheduled."
     // This is okay here: this.modifiedData will be updated after
@@ -994,12 +994,12 @@ export class LMSalienceModule extends SingleExampleSingleModelModule {
     // prettier-ignore
     return html`
       <div class='chip-container'>
-        <lm-salience-chips
+        <sequence-salience-chips
           .tokensWithWeights=${segmentsWithWeights} .cmap=${this.cmap}
           ?dense=${this.denseView} ?vDense=${this.vDense}
           ?underline=${this.underline}
           ?preSpace=${this.denseView} breakNewlines>
-        </lm-salience-chips>
+        </sequence-salience-chips>
       </div>
     `;
   }
@@ -1081,7 +1081,7 @@ export class LMSalienceModule extends SingleExampleSingleModelModule {
 
 declare global {
   interface HTMLElementTagNameMap {
-    'lm-salience-chips': LMSalienceChips;
-    'lm-salience-module': LMSalienceModule;
+    'sequence-salience-chips': SequenceSalienceChips;
+    'sequence-salience-module': SequenceSalienceModule;
   }
-}
\ No newline at end of file
+}
diff --git a/lit_nlp/examples/lm_salience_demo.py b/lit_nlp/examples/lm_salience_demo.py
index 510d90e7..37a68d1f 100644
--- a/lit_nlp/examples/lm_salience_demo.py
+++ b/lit_nlp/examples/lm_salience_demo.py
@@ -54,7 +54,10 @@
 from lit_nlp import server_flags
 from lit_nlp.api import layout
 from lit_nlp.examples.datasets import lm as lm_data
-from lit_nlp.lib import file_cache
+
+# TODO(b/333698148): file_cache doesn't work well with certain HF and KerasNLP
+# preset names. Disabling until resolved.
+# from lit_nlp.lib import file_cache
 
 # NOTE: additional flags defined in server_flags.py
 
@@ -132,7 +135,7 @@
         "Editor": [modules.SingleDatapointEditorModule],
     },
     upper={  # if 'lower' not specified, this fills the right side
-        "Salience": [modules.LMSalienceModule],
+        "Salience": [modules.SequenceSalienceModule],
     },
     layoutSettings=layout.LayoutSettings(leftWidth=40),
     description="Left/right layout for language model salience.",
@@ -143,7 +146,7 @@
         "Editor": [modules.SimpleDatapointEditorModule],
     },
     lower={
-        "Salience": [modules.LMSalienceModule],
+        "Salience": [modules.SequenceSalienceModule],
     },
     layoutSettings=layout.LayoutSettings(
         hideToolbar=True,
@@ -162,7 +165,7 @@
         "Datapoint Generators": [modules.GeneratorModule],
     },
     lower={
-        "Salience": [modules.LMSalienceModule],
+        "Salience": [modules.SequenceSalienceModule],
         "Metrics": [modules.MetricsModule],
     },
     layoutSettings=layout.LayoutSettings(
@@ -279,10 +282,12 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
     model_name, path = model_string.split(":", 1)
     logging.info("Loading model '%s' from '%s'", model_name, path)
 
-    path = file_cache.cached_path(
-        path,
-        extract_compressed_file=path.endswith(".tar.gz"),
-    )
+    # TODO(b/333698148): file_cache doesn't work well with certain HF and
+    # KerasNLP preset names. Disabling until resolved.
+    # path = file_cache.cached_path(
+    #     path,
+    #     extract_compressed_file=path.endswith(".tar.gz"),
+    # )
 
     if _DL_FRAMEWORK.value == "kerasnlp":
       # pylint: disable=g-import-not-at-top
diff --git a/requirements_core.txt b/requirements_core.txt
index 2ceb6ed4..bacdd68a 100644
--- a/requirements_core.txt
+++ b/requirements_core.txt
@@ -21,7 +21,7 @@ filelock>=3.12.3
 google-cloud-translate>=3.11.1
 ipython>=7.34.0
 Levenshtein>=0.21.1
-matplotlib>=3.7.1
+matplotlib>=3.6.0,<3.9.0
 ml-collections>=0.1.1
 numpy>=1.24.1
 pandas>=2.0.3

From b3c120b22138fb03a712f11778197cf4966d0c3a Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Fri, 24 May 2024 13:28:10 -0700
Subject: [PATCH 07/47] LIT: Relax min Python version to 3.9

PiperOrigin-RevId: 637017601
---
 .github/workflows/ci.yml                          | 2 +-
 lit_nlp/api/dataset_test.py                       | 1 -
 lit_nlp/components/ablation_flip.py               | 2 +-
 lit_nlp/components/backtranslator.py              | 2 +-
 lit_nlp/components/classification_results_test.py | 5 -----
 lit_nlp/components/shap_explainer.py              | 5 ++---
 lit_nlp/components/tcav.py                        | 2 +-
 lit_nlp/lib/caching.py                            | 2 +-
 lit_nlp/lib/utils.py                              | 2 +-
 lit_nlp/lib/utils_test.py                         | 2 +-
 pyproject.toml                                    | 4 ++--
 requirements_core.txt                             | 2 +-
 12 files changed, 12 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 236359e5..270b0666 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,7 +33,7 @@ jobs:
     strategy:
       matrix:
         node-version: [18]
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11"]
     defaults:
       run:
         shell: bash -l {0}
diff --git a/lit_nlp/api/dataset_test.py b/lit_nlp/api/dataset_test.py
index 7463e28e..ab77f323 100644
--- a/lit_nlp/api/dataset_test.py
+++ b/lit_nlp/api/dataset_test.py
@@ -179,7 +179,6 @@ def test_init_from_examples_without_ids(self):
         examples=examples
     )
 
-    # TODO(b/266681945): Enabled zip(..., strict=true) once updated to Py3.10
     for indexed_example, example, original in zip(
         dataset.indexed_examples, dataset.examples, examples
     ):
diff --git a/lit_nlp/components/ablation_flip.py b/lit_nlp/components/ablation_flip.py
index f6d41e11..b83be51b 100644
--- a/lit_nlp/components/ablation_flip.py
+++ b/lit_nlp/components/ablation_flip.py
@@ -209,7 +209,7 @@ def _generate_leave_one_out_ablation_score(
           self._create_cf(example, input_spec, [(field, idx)])
           for idx in idxs
       ])
-      for idx, cf_output in zip(idxs, cf_outputs, strict=True):
+      for idx, cf_output in zip(idxs, cf_outputs):
         loo_score = cf_utils.prediction_difference(
             cf_output, orig_output, output_spec, pred_key
         )
diff --git a/lit_nlp/components/backtranslator.py b/lit_nlp/components/backtranslator.py
index 4da15647..96d8d97c 100644
--- a/lit_nlp/components/backtranslator.py
+++ b/lit_nlp/components/backtranslator.py
@@ -94,7 +94,7 @@ def generate_all(self,
     # Generate by substituting in each field.
     # TODO(lit-team): substitute on a combination of fields?
     for field_name, candidates in candidates_by_field.items():
-      for i, (inp, cands) in enumerate(zip(inputs, candidates, strict=True)):
+      for i, (inp, cands) in enumerate(zip(inputs, candidates)):
         for cand in cands:
           all_outputs[i].append(utils.make_modified_input(
               inp, {field_name: cand}, 'Backtranslator'
diff --git a/lit_nlp/components/classification_results_test.py b/lit_nlp/components/classification_results_test.py
index 64514115..27cdd985 100644
--- a/lit_nlp/components/classification_results_test.py
+++ b/lit_nlp/components/classification_results_test.py
@@ -53,7 +53,6 @@ def test_no_label(self):
         {'probas': dtypes.ClassificationResult([0.2, 0.8], '1', None)},
     ]
     self.assertListEqual(['probas'], list(results[0].keys()))
-    # TODO(b/266681945): Add strict=True to ensure equal lengths
     for result, expected in zip(results, expected_results):
       np.testing.assert_array_equal(
           expected['probas'].scores, result['probas'].scores
@@ -77,7 +76,6 @@ def test_no_margins(self):
         {'probas': dtypes.ClassificationResult([0.2, 0.8], '1', False)},
     ]
     self.assertListEqual(['probas'], list(results[0].keys()))
-    # TODO(b/266681945): Add strict=True to ensure equal lengths
     for result, expected in zip(results, expected_results):
       np.testing.assert_array_equal(
           expected['probas'].scores, result['probas'].scores
@@ -105,7 +103,6 @@ def test_single_margin(self):
         {'probas': dtypes.ClassificationResult([0.2, 0.8], '0', True)},
     ]
     self.assertListEqual(['probas'], list(results[0].keys()))
-    # TODO(b/266681945): Add strict=True to ensure equal lengths
     for result, expected in zip(results, expected_results):
       np.testing.assert_array_equal(
           expected['probas'].scores, result['probas'].scores
@@ -145,7 +142,6 @@ def test_faceted_margins_text(self):
         {'probas': dtypes.ClassificationResult([0.2, 0.8], '1', False)},
     ]
     self.assertListEqual(['probas'], list(results[0].keys()))
-    # TODO(b/266681945): Add strict=True to ensure equal lengths
     for result, expected in zip(results, expected_results):
       np.testing.assert_array_equal(
           expected['probas'].scores, result['probas'].scores
@@ -188,7 +184,6 @@ def test_faceted_margins_num(self):
         {'probas': dtypes.ClassificationResult([0.2, 0.8], '1', False)},
     ]
     self.assertListEqual(['probas'], list(results[0].keys()))
-    # TODO(b/266681945): Add strict=True to ensure equal lengths
     for result, expected in zip(results, expected_results):
       np.testing.assert_array_equal(
           expected['probas'].scores, result['probas'].scores
diff --git a/lit_nlp/components/shap_explainer.py b/lit_nlp/components/shap_explainer.py
index 4df68535..f4434fe4 100644
--- a/lit_nlp/components/shap_explainer.py
+++ b/lit_nlp/components/shap_explainer.py
@@ -163,8 +163,7 @@ def run(
 
     def prediction_fn(examples):
       dict_examples: list[JsonDict] = [
-          dict(zip(input_feats, feature_values, strict=True))
-          for feature_values in examples
+          dict(zip(input_feats, feature_values)) for feature_values in examples
       ]
 
       preds: list[Union[int, float]] = []
@@ -189,7 +188,7 @@ def prediction_fn(examples):
     explainer = shap.KernelExplainer(prediction_fn, background)
     shap_values_by_example = explainer.shap_values(inputs_to_use)
     salience = [
-        dict(zip(input_feats, example_shap_values, strict=True))
+        dict(zip(input_feats, example_shap_values))
         for example_shap_values in shap_values_by_example
     ]
     return [{'saliency': dtypes.FeatureSalience(s)} for s in salience]
diff --git a/lit_nlp/components/tcav.py b/lit_nlp/components/tcav.py
index 32b7a189..dacab22b 100644
--- a/lit_nlp/components/tcav.py
+++ b/lit_nlp/components/tcav.py
@@ -142,7 +142,7 @@ def get_predictions(
         t for t in target_fields if getattr(output_spec[t], 'parent')
     ]
     modified_inputs = []
-    for ex, preds in zip(inputs, predictions, strict=True):
+    for ex, preds in zip(inputs, predictions):
       overrides = {}
       for field in valid_target_fields:
         label_idx = np.argmax(preds[field])
diff --git a/lit_nlp/lib/caching.py b/lit_nlp/lib/caching.py
index ab42885f..169dfd6d 100644
--- a/lit_nlp/lib/caching.py
+++ b/lit_nlp/lib/caching.py
@@ -269,7 +269,7 @@ def fit_transform(self, inputs: Iterable[JsonDict]):
       )
     outputs = list(wrapped.fit_transform(inputs_as_list))
     with self._cache.lock:
-      for cache_key, output in zip(cache_keys, outputs, strict=True):
+      for cache_key, output in zip(cache_keys, outputs):
         self._cache.put(output, cache_key)
     return outputs
 
diff --git a/lit_nlp/lib/utils.py b/lit_nlp/lib/utils.py
index 4351465d..6f5ff7b1 100644
--- a/lit_nlp/lib/utils.py
+++ b/lit_nlp/lib/utils.py
@@ -203,7 +203,7 @@ def pad1d(
     min_len: int,
     pad_val: T,
     pad_left: bool = False,
-    max_len: int | None = None,
+    max_len: Optional[int] = None,
 ) -> list[T]:
   """Pad a list to the target length."""
   if pad_left:
diff --git a/lit_nlp/lib/utils_test.py b/lit_nlp/lib/utils_test.py
index 5646181f..52b3011d 100644
--- a/lit_nlp/lib/utils_test.py
+++ b/lit_nlp/lib/utils_test.py
@@ -293,7 +293,7 @@ def test_pad1d(
       pad_val: T,
       expected: list[T],
       pad_left: bool = False,
-      max_len: int | None = None,
+      max_len: Optional[int] = None,
   ):
     self.assertEqual(
         utils.pad1d(
diff --git a/pyproject.toml b/pyproject.toml
index 5bed29b2..b93fe7c5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,13 +11,13 @@ authors = [
 description = "🔥LIT: The Learning Interpretability Tool"
 readme = "README.md"
 license = { file = "LICENSE" }
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 # LINT.IfChange
 dependencies = [
   "absl-py>=1.4.0",
   "annoy>=1.17.3",
   "attrs>=22.1.0",
-  "etils[epath]>=1.7.0",
+  "etils[epath]>=1.5.0",
   "filelock>=3.12.3",
   "google-cloud-translate>=3.11.1",
   "ipython>=7.34.0",
diff --git a/requirements_core.txt b/requirements_core.txt
index bacdd68a..8863d139 100644
--- a/requirements_core.txt
+++ b/requirements_core.txt
@@ -16,7 +16,7 @@
 absl-py>=1.4.0
 annoy>=1.17.3
 attrs>=22.1.0
-etils[epath]>=1.7.0
+etils[epath]>=1.5.0
 filelock>=3.12.3
 google-cloud-translate>=3.11.1
 ipython>=7.34.0

From 5188c8c835328efcc9dff5a0a4cf4cd79fabe099 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 29 May 2024 11:21:19 -0700
Subject: [PATCH 08/47] Relaxing SHAP version.

PiperOrigin-RevId: 638351905
---
 pyproject.toml        | 2 +-
 requirements_core.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b93fe7c5..97e19939 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,7 +34,7 @@ dependencies = [
   "saliency>=0.1.3",
   "scikit-learn>=1.0.2",
   "scipy>=1.10.1",
-  "shap==0.42.0",
+  "shap>=0.42.0,<0.46.0",
   "six>=1.16.0",
   "termcolor>=2.3.0",
   "tqdm>=4.64.0",
diff --git a/requirements_core.txt b/requirements_core.txt
index 8863d139..c1bb2434 100644
--- a/requirements_core.txt
+++ b/requirements_core.txt
@@ -33,7 +33,7 @@ sacrebleu>=2.3.1
 saliency>=0.1.3
 scikit-learn>=1.0.2
 scipy>=1.10.1
-shap==0.42.0
+shap>=0.42.0,<0.46.0
 six>=1.16.0
 termcolor>=2.3.0
 tqdm>=4.64.0

From 15eccb1197366c925a5beff310fb5d7d369bde0c Mon Sep 17 00:00:00 2001
From: Ian Tenney <iftenney@google.com>
Date: Thu, 30 May 2024 09:43:37 -0700
Subject: [PATCH 09/47] Simplify file_cache logic and re-enable for sequence
 salience demo.

PiperOrigin-RevId: 638684231
---
 lit_nlp/examples/lm_salience_demo.py | 19 +++++++++----------
 lit_nlp/lib/file_cache.py            |  1 +
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/lit_nlp/examples/lm_salience_demo.py b/lit_nlp/examples/lm_salience_demo.py
index 37a68d1f..fb555bec 100644
--- a/lit_nlp/examples/lm_salience_demo.py
+++ b/lit_nlp/examples/lm_salience_demo.py
@@ -54,10 +54,7 @@
 from lit_nlp import server_flags
 from lit_nlp.api import layout
 from lit_nlp.examples.datasets import lm as lm_data
-
-# TODO(b/333698148): file_cache doesn't work well with certain HF and KerasNLP
-# preset names. Disabling until resolved.
-# from lit_nlp.lib import file_cache
+from lit_nlp.lib import file_cache
 
 # NOTE: additional flags defined in server_flags.py
 
@@ -282,12 +279,14 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
     model_name, path = model_string.split(":", 1)
     logging.info("Loading model '%s' from '%s'", model_name, path)
 
-    # TODO(b/333698148): file_cache doesn't work well with certain HF and
-    # KerasNLP preset names. Disabling until resolved.
-    # path = file_cache.cached_path(
-    #     path,
-    #     extract_compressed_file=path.endswith(".tar.gz"),
-    # )
+    # Limit scope of caching to archive files and remote paths, as some preset
+    # names like "google/gemma-1.1-7b-it" look like file paths but should not
+    # be handled as such.
+    if path.endswith(".tar.gz") or file_cache.is_remote(path):
+      path = file_cache.cached_path(
+          path,
+          extract_compressed_file=path.endswith(".tar.gz"),
+      )
 
     if _DL_FRAMEWORK.value == "kerasnlp":
       # pylint: disable=g-import-not-at-top
diff --git a/lit_nlp/lib/file_cache.py b/lit_nlp/lib/file_cache.py
index a8417ce0..88ab3934 100644
--- a/lit_nlp/lib/file_cache.py
+++ b/lit_nlp/lib/file_cache.py
@@ -217,6 +217,7 @@ def filename_fom_url(url: str, etag: Optional[str] = None) -> str:
 
 
 def is_remote(url_of_filepath: str) -> bool:
+  """Check if a path represents a remote URL or non-local file."""
   parsed = urllib_parse.urlparse(url_of_filepath)
   return parsed.scheme in ('http', 'https')
 

From f4c099082f0e89986aad162cc3cd0ac9bc2214c7 Mon Sep 17 00:00:00 2001
From: Googler <no-reply@google.com>
Date: Mon, 3 Jun 2024 12:29:54 -0700
Subject: [PATCH 10/47] No public description

PiperOrigin-RevId: 639874888
---
 lit_nlp/examples/blank_slate_demo.py                        | 4 ++--
 .../examples/{datasets/penguin_data.py => penguin/data.py}  | 0
 lit_nlp/examples/{penguin_demo.py => penguin/demo.py}       | 6 +++---
 .../examples/{models/penguin_model.py => penguin/model.py}  | 2 +-
 .../penguin_model_int_test.py => penguin/model_int_test.py} | 2 +-
 website/sphinx_src/components.md                            | 2 +-
 website/sphinx_src/demos.md                                 | 2 +-
 7 files changed, 9 insertions(+), 9 deletions(-)
 rename lit_nlp/examples/{datasets/penguin_data.py => penguin/data.py} (100%)
 rename lit_nlp/examples/{penguin_demo.py => penguin/demo.py} (93%)
 rename lit_nlp/examples/{models/penguin_model.py => penguin/model.py} (97%)
 rename lit_nlp/examples/{models/penguin_model_int_test.py => penguin/model_int_test.py} (93%)

diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index b64db1c0..9d235dcb 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -35,13 +35,13 @@
 from lit_nlp.examples.datasets import imagenette
 from lit_nlp.examples.datasets import lm
 from lit_nlp.examples.datasets import mt
-from lit_nlp.examples.datasets import penguin_data
 from lit_nlp.examples.datasets import summarization
 from lit_nlp.examples.models import glue_models
 from lit_nlp.examples.models import mobilenet
-from lit_nlp.examples.models import penguin_model
 from lit_nlp.examples.models import pretrained_lms
 from lit_nlp.examples.models import t5
+from lit_nlp.examples.penguin import data as penguin_data
+from lit_nlp.examples.penguin import model as penguin_model
 
 # NOTE: additional flags defined in server_flags.py
 
diff --git a/lit_nlp/examples/datasets/penguin_data.py b/lit_nlp/examples/penguin/data.py
similarity index 100%
rename from lit_nlp/examples/datasets/penguin_data.py
rename to lit_nlp/examples/penguin/data.py
diff --git a/lit_nlp/examples/penguin_demo.py b/lit_nlp/examples/penguin/demo.py
similarity index 93%
rename from lit_nlp/examples/penguin_demo.py
rename to lit_nlp/examples/penguin/demo.py
index 79cc077c..c48fd538 100644
--- a/lit_nlp/examples/penguin_demo.py
+++ b/lit_nlp/examples/penguin/demo.py
@@ -1,7 +1,7 @@
 """🐧 LIT demo for tabular data using penguin classification.
 
 To run:
-  python -m lit_nlp.examples.penguin_demo --port=5432
+  blaze run -c opt --config=cuda examples/penguin:demo -- --port=5432
 
 Then navigate to localhost:5432 to access the demo UI.
 """
@@ -17,8 +17,8 @@
 from lit_nlp import server_flags
 from lit_nlp.api import layout
 from lit_nlp.components import minimal_targeted_counterfactuals
-from lit_nlp.examples.datasets import penguin_data
-from lit_nlp.examples.models import penguin_model
+from lit_nlp.examples.penguin import data as penguin_data
+from lit_nlp.examples.penguin import model as penguin_model
 
 MODEL_PATH = 'https://storage.googleapis.com/what-if-tool-resources/lit-models/penguin.h5'  # pylint: disable=line-too-long
 
diff --git a/lit_nlp/examples/models/penguin_model.py b/lit_nlp/examples/penguin/model.py
similarity index 97%
rename from lit_nlp/examples/models/penguin_model.py
rename to lit_nlp/examples/penguin/model.py
index 783edc5f..6aec9c11 100644
--- a/lit_nlp/examples/models/penguin_model.py
+++ b/lit_nlp/examples/penguin/model.py
@@ -2,7 +2,7 @@
 
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
-from lit_nlp.examples.datasets import penguin_data
+from lit_nlp.examples.penguin import data as penguin_data
 from lit_nlp.lib import file_cache
 import numpy as np
 import tensorflow as tf
diff --git a/lit_nlp/examples/models/penguin_model_int_test.py b/lit_nlp/examples/penguin/model_int_test.py
similarity index 93%
rename from lit_nlp/examples/models/penguin_model_int_test.py
rename to lit_nlp/examples/penguin/model_int_test.py
index 7ebe86d7..c9dca404 100644
--- a/lit_nlp/examples/models/penguin_model_int_test.py
+++ b/lit_nlp/examples/penguin/model_int_test.py
@@ -1,7 +1,7 @@
 """Integration tests for penguin_model."""
 
 from absl.testing import absltest
-from lit_nlp.examples.models import penguin_model
+from lit_nlp.examples.penguin import model as penguin_model
 
 
 class PenguinModelIntTest(absltest.TestCase):
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index f2366d7c..19c4dde0 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -233,7 +233,7 @@ these cases, LIT validates model compatibility by checking that:
     (`SparseMultilabel`) field.
 
 For a demo using a penguin stats dataset/binary classification task, see
-[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py).
+[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py).
 
 ### Images
 
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 306d3139..dfc77b50 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -159,7 +159,7 @@ https://pair-code.github.io/lit/tutorials/coref
 ### Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \
-**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py)
+**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py)
 
 *   Binary classification on
     [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins).

From 6aa2eb64eddb8ca154401bfd6a039762bc374d6d Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 4 Jun 2024 10:05:10 -0700
Subject: [PATCH 11/47] Remove the image demo, models, dataset, and other
 related documentation from the LIT examples.

PiperOrigin-RevId: 640191591
---
 lit_nlp/examples/blank_slate_demo.py       |   15 -
 lit_nlp/examples/datasets/imagenette.py    |   34 -
 lit_nlp/examples/datasets/open_images.py   |   32 -
 lit_nlp/examples/image_demo.py             |  100 -
 lit_nlp/examples/models/imagenet_labels.py | 2019 --------------------
 lit_nlp/examples/models/mobilenet.py       |   78 -
 lit_nlp/examples/models/mobilenet_test.py  |  100 -
 website/sphinx_src/components.md           |   11 +-
 website/sphinx_src/demos.md                |   10 -
 website/sphinx_src/faq.md                  |    8 +-
 10 files changed, 6 insertions(+), 2401 deletions(-)
 delete mode 100644 lit_nlp/examples/datasets/imagenette.py
 delete mode 100644 lit_nlp/examples/datasets/open_images.py
 delete mode 100644 lit_nlp/examples/image_demo.py
 delete mode 100644 lit_nlp/examples/models/imagenet_labels.py
 delete mode 100644 lit_nlp/examples/models/mobilenet.py
 delete mode 100644 lit_nlp/examples/models/mobilenet_test.py

diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index 9d235dcb..6fbefd40 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -12,7 +12,6 @@
   left-to-right language model, with the Stanford Sentiment Treebank dataset,
   the IMDB reviews dataset, Billion Word Benchmark (lm1b) dataset and the option
   to load sentences from a flat text file.
-- MobileNet model, with the Imagenette TFDS dataset.
 
 To run:
   python -m lit_nlp.examples.blank_slate_demo --port=5432
@@ -32,12 +31,10 @@
 from lit_nlp import server_flags
 from lit_nlp.examples.datasets import classification
 from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.datasets import imagenette
 from lit_nlp.examples.datasets import lm
 from lit_nlp.examples.datasets import mt
 from lit_nlp.examples.datasets import summarization
 from lit_nlp.examples.models import glue_models
-from lit_nlp.examples.models import mobilenet
 from lit_nlp.examples.models import pretrained_lms
 from lit_nlp.examples.models import t5
 from lit_nlp.examples.penguin import data as penguin_data
@@ -111,12 +108,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       pretrained_lms.GPT2LanguageModel.init_spec(),
   )
 
-  # image model loaders.
-  model_loaders["image"] = (
-      mobilenet.MobileNet,
-      mobilenet.MobileNet.init_spec(),
-  )
-
   datasets = {}
   dataset_loaders: lit_app.DatasetLoadersMap = {}
 
@@ -156,12 +147,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       lm.BillionWordBenchmark.init_spec(),
   )
 
-  # image demo dataset loaders.
-  dataset_loaders["image"] = (
-      imagenette.ImagenetteDataset,
-      imagenette.ImagenetteDataset.init_spec(),
-  )
-
   # Start the LIT server. See server_flags.py for server options.
   lit_demo = dev_server.Server(
       models,
diff --git a/lit_nlp/examples/datasets/imagenette.py b/lit_nlp/examples/datasets/imagenette.py
deleted file mode 100644
index 7630709f..00000000
--- a/lit_nlp/examples/datasets/imagenette.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Imagenette dataset from tfds."""
-
-from lit_nlp.api import dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import image_utils
-from PIL import Image as PILImage
-import tensorflow_datasets as tfds
-
-
-class ImagenetteDataset(dataset.Dataset):
-  """Imagenette TFDS dataset.
-
-  See https://www.tensorflow.org/datasets/catalog/imagenette for details.
-  Images are at low quality and by default load 100 examples from the validation
-  set though this can be changed through the `split` parameter, as per TFDS
-  documentation.
-  """
-
-  def __init__(self, split: str = 'validation[:100]'):
-    tfds_examples = tfds.as_numpy(
-        tfds.load('imagenette/160px', split=split, download=True,
-                  try_gcs=True))
-    def convert_input(inp):
-      pil_image = PILImage.fromarray(inp['image'])
-      image_str = image_utils.convert_pil_to_image_str(pil_image)
-      return {'image': image_str}
-    self._examples = [convert_input(inp) for inp in tfds_examples]
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        'image': lit_types.ImageBytes(),
-    }
-
-
diff --git a/lit_nlp/examples/datasets/open_images.py b/lit_nlp/examples/datasets/open_images.py
deleted file mode 100644
index 71e0779a..00000000
--- a/lit_nlp/examples/datasets/open_images.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""Open Images dataset from tfds."""
-
-from lit_nlp.api import dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import image_utils
-from PIL import Image as PILImage
-import tensorflow_datasets as tfds
-
-
-class OpenImagesDataset(dataset.Dataset):
-  """OpenImages TFDS dataset.
-
-  See https://www.tensorflow.org/datasets/catalog/open_images_v4 for details.
-  Images are at 72 JPEG quality and by default load 100 examples from the test
-  set though this can be changed through the `split` parameter, as per TFDS
-  documentation.
-  """
-
-  def __init__(self, split: str = 'test[:100]'):
-    tfds_examples = tfds.as_numpy(
-        tfds.load('open_images_v4/200k', split=split, download=True,
-                  try_gcs=True))
-    def convert_input(inp):
-      pil_image = PILImage.fromarray(inp['image'])
-      image_str = image_utils.convert_pil_to_image_str(pil_image)
-      return {'image': image_str}
-    self._examples = [convert_input(inp) for inp in tfds_examples]
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        'image': lit_types.ImageBytes(),
-    }
diff --git a/lit_nlp/examples/image_demo.py b/lit_nlp/examples/image_demo.py
deleted file mode 100644
index 5d9d25a4..00000000
--- a/lit_nlp/examples/image_demo.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""LIT demo for image model.
-
-To run:
-  python -m lit_nlp.examples.image_demo --port=5432
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.components import classification_results
-from lit_nlp.components import image_gradient_maps
-from lit_nlp.examples.datasets import imagenette
-from lit_nlp.examples.models import mobilenet
-
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default('development_demo', True)
-FLAGS.set_default('warm_start', 1)
-FLAGS.set_default('page_title', 'LIT Image Demo')
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    'max_examples',
-    None,
-    (
-        'Maximum number of examples to load into LIT. '
-        'Set --max_examples=200 for a quick start.'
-    ),
-)
-
-
-def get_wsgi_app():
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default('server_type', 'external')
-  FLAGS.set_default('demo_mode', True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info('image_demo:get_wsgi_app() called with unused args: %s',
-                 unused)
-  return main([])
-
-# Custom frontend layout; see api/layout.py
-modules = layout.LitModuleName
-DEMO_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        'Main': [modules.DataTableModule, modules.DatapointEditorModule],
-    },
-    lower={
-        'Predictions': [modules.ClassificationModule, modules.ScalarModule],
-        'Explanations': [
-            modules.ClassificationModule,
-            modules.SalienceMapModule,
-        ],
-    },
-    description='Basic layout for image demo',
-)
-
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {'default': DEMO_LAYOUT}
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError('Too many command-line arguments.')
-
-  datasets = {'imagenette': imagenette.ImagenetteDataset()}
-  # Truncate datasets if --max_examples is set.
-  if _MAX_EXAMPLES.value is not None:
-    for name in datasets:
-      logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-      datasets[name] = datasets[name].slice[: _MAX_EXAMPLES.value]
-      logging.info('  truncated to %d examples', len(datasets[name]))
-
-  models = {'mobilenet': mobilenet.MobileNet()}
-  interpreters = {
-      'classification': classification_results.ClassificationInterpreter(),
-  } | image_gradient_maps.all_interpreters()
-
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      interpreters=interpreters,
-      generators={},
-      layouts={'default': DEMO_LAYOUT},
-      **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == '__main__':
-  app.run(main)
diff --git a/lit_nlp/examples/models/imagenet_labels.py b/lit_nlp/examples/models/imagenet_labels.py
deleted file mode 100644
index 14db6708..00000000
--- a/lit_nlp/examples/models/imagenet_labels.py
+++ /dev/null
@@ -1,2019 +0,0 @@
-"""Imagenet labels."""
-# pylint: disable=line-too-long
-IMAGENET_2012_LABELS = {
-    0:
-        'tench, Tinca tinca',
-    1:
-        'goldfish, Carassius auratus',
-    2:
-        'great white shark, white shark, man-eater, man-eating shark, '
-        'Carcharodon carcharias',
-    3:
-        'tiger shark, Galeocerdo cuvieri',
-    4:
-        'hammerhead, hammerhead shark',
-    5:
-        'electric ray, crampfish, numbfish, torpedo',
-    6:
-        'stingray',
-    7:
-        'cock',
-    8:
-        'hen',
-    9:
-        'ostrich, Struthio camelus',
-    10:
-        'brambling, Fringilla montifringilla',
-    11:
-        'goldfinch, Carduelis carduelis',
-    12:
-        'house finch, linnet, Carpodacus mexicanus',
-    13:
-        'junco, snowbird',
-    14:
-        'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
-    15:
-        'robin, American robin, Turdus migratorius',
-    16:
-        'bulbul',
-    17:
-        'jay',
-    18:
-        'magpie',
-    19:
-        'chickadee',
-    20:
-        'water ouzel, dipper',
-    21:
-        'kite',
-    22:
-        'bald eagle, American eagle, Haliaeetus leucocephalus',
-    23:
-        'vulture',
-    24:
-        'great grey owl, great gray owl, Strix nebulosa',
-    25:
-        'European fire salamander, Salamandra salamandra',
-    26:
-        'common newt, Triturus vulgaris',
-    27:
-        'eft',
-    28:
-        'spotted salamander, Ambystoma maculatum',
-    29:
-        'axolotl, mud puppy, Ambystoma mexicanum',
-    30:
-        'bullfrog, Rana catesbeiana',
-    31:
-        'tree frog, tree-frog',
-    32:
-        'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
-    33:
-        'loggerhead, loggerhead turtle, Caretta caretta',
-    34:
-        'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',
-    35:
-        'mud turtle',
-    36:
-        'terrapin',
-    37:
-        'box turtle, box tortoise',
-    38:
-        'banded gecko',
-    39:
-        'common iguana, iguana, Iguana iguana',
-    40:
-        'American chameleon, anole, Anolis carolinensis',
-    41:
-        'whiptail, whiptail lizard',
-    42:
-        'agama',
-    43:
-        'frilled lizard, Chlamydosaurus kingi',
-    44:
-        'alligator lizard',
-    45:
-        'Gila monster, Heloderma suspectum',
-    46:
-        'green lizard, Lacerta viridis',
-    47:
-        'African chameleon, Chamaeleo chamaeleon',
-    48:
-        'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus '
-        'komodoensis',
-    49:
-        'African crocodile, Nile crocodile, Crocodylus niloticus',
-    50:
-        'American alligator, Alligator mississipiensis',
-    51:
-        'triceratops',
-    52:
-        'thunder snake, worm snake, Carphophis amoenus',
-    53:
-        'ringneck snake, ring-necked snake, ring snake',
-    54:
-        'hognose snake, puff adder, sand viper',
-    55:
-        'green snake, grass snake',
-    56:
-        'king snake, kingsnake',
-    57:
-        'garter snake, grass snake',
-    58:
-        'water snake',
-    59:
-        'vine snake',
-    60:
-        'night snake, Hypsiglena torquata',
-    61:
-        'boa constrictor, Constrictor constrictor',
-    62:
-        'rock python, rock snake, Python sebae',
-    63:
-        'Indian cobra, Naja naja',
-    64:
-        'green mamba',
-    65:
-        'sea snake',
-    66:
-        'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
-    67:
-        'diamondback, diamondback rattlesnake, Crotalus adamanteus',
-    68:
-        'sidewinder, horned rattlesnake, Crotalus cerastes',
-    69:
-        'trilobite',
-    70:
-        'harvestman, daddy longlegs, Phalangium opilio',
-    71:
-        'scorpion',
-    72:
-        'black and gold garden spider, Argiope aurantia',
-    73:
-        'barn spider, Araneus cavaticus',
-    74:
-        'garden spider, Aranea diademata',
-    75:
-        'black widow, Latrodectus mactans',
-    76:
-        'tarantula',
-    77:
-        'wolf spider, hunting spider',
-    78:
-        'tick',
-    79:
-        'centipede',
-    80:
-        'black grouse',
-    81:
-        'ptarmigan',
-    82:
-        'ruffed grouse, partridge, Bonasa umbellus',
-    83:
-        'prairie chicken, prairie grouse, prairie fowl',
-    84:
-        'peacock',
-    85:
-        'quail',
-    86:
-        'partridge',
-    87:
-        'African grey, African gray, Psittacus erithacus',
-    88:
-        'macaw',
-    89:
-        'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
-    90:
-        'lorikeet',
-    91:
-        'coucal',
-    92:
-        'bee eater',
-    93:
-        'hornbill',
-    94:
-        'hummingbird',
-    95:
-        'jacamar',
-    96:
-        'toucan',
-    97:
-        'drake',
-    98:
-        'red-breasted merganser, Mergus serrator',  # NOTYPO
-    99:
-        'goose',
-    100:
-        'black swan, Cygnus atratus',
-    101:
-        'tusker',
-    102:
-        'echidna, spiny anteater, anteater',
-    103:
-        'platypus, duckbill, duckbilled platypus, duck-billed platypus, '
-        'Ornithorhynchus anatinus',
-    104:
-        'wallaby, brush kangaroo',
-    105:
-        'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',
-    106:
-        'wombat',
-    107:
-        'jellyfish',
-    108:
-        'sea anemone, anemone',
-    109:
-        'brain coral',
-    110:
-        'flatworm, platyhelminth',
-    111:
-        'nematode, nematode worm, roundworm',
-    112:
-        'conch',
-    113:
-        'snail',
-    114:
-        'slug',
-    115:
-        'sea slug, nudibranch',
-    116:
-        'chiton, coat-of-mail shell, sea cradle, polyplacophore',
-    117:
-        'chambered nautilus, pearly nautilus, nautilus',
-    118:
-        'Dungeness crab, Cancer magister',
-    119:
-        'rock crab, Cancer irroratus',
-    120:
-        'fiddler crab',
-    121:
-        'king crab, Alaska crab, Alaskan king crab, Alaska king crab, '
-        'Paralithodes camtschatica',
-    122:
-        'American lobster, Northern lobster, Maine lobster, Homarus americanus',
-    123:
-        'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',
-    124:
-        'crayfish, crawfish, crawdad, crawdaddy',
-    125:
-        'hermit crab',
-    126:
-        'isopod',
-    127:
-        'white stork, Ciconia ciconia',
-    128:
-        'black stork, Ciconia nigra',
-    129:
-        'spoonbill',
-    130:
-        'flamingo',
-    131:
-        'little blue heron, Egretta caerulea',
-    132:
-        'American egret, great white heron, Egretta albus',
-    133:
-        'bittern',
-    134:
-        'crane',
-    135:
-        'limpkin, Aramus pictus',
-    136:
-        'European gallinule, Porphyrio porphyrio',
-    137:
-        'American coot, marsh hen, mud hen, water hen, Fulica americana',
-    138:
-        'bustard',
-    139:
-        'ruddy turnstone, Arenaria interpres',
-    140:
-        'red-backed sandpiper, dunlin, Erolia alpina',
-    141:
-        'redshank, Tringa totanus',
-    142:
-        'dowitcher',
-    143:
-        'oystercatcher, oyster catcher',
-    144:
-        'pelican',
-    145:
-        'king penguin, Aptenodytes patagonica',
-    146:
-        'albatross, mollymawk',
-    147:
-        'grey whale, gray whale, devilfish, Eschrichtius gibbosus, '
-        'Eschrichtius robustus',
-    148:
-        'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
-    149:
-        'dugong, Dugong dugon',
-    150:
-        'sea lion',
-    151:
-        'Chihuahua',
-    152:
-        'Japanese spaniel',
-    153:
-        'Maltese dog, Maltese terrier, Maltese',
-    154:
-        'Pekinese, Pekingese, Peke',
-    155:
-        'Shih-Tzu',
-    156:
-        'Blenheim spaniel',
-    157:
-        'papillon',
-    158:
-        'toy terrier',
-    159:
-        'Rhodesian ridgeback',
-    160:
-        'Afghan hound, Afghan',
-    161:
-        'basset, basset hound',
-    162:
-        'beagle',
-    163:
-        'bloodhound, sleuthhound',
-    164:
-        'bluetick',
-    165:
-        'black-and-tan coonhound',
-    166:
-        'Walker hound, Walker foxhound',
-    167:
-        'English foxhound',
-    168:
-        'redbone',
-    169:
-        'borzoi, Russian wolfhound',
-    170:
-        'Irish wolfhound',
-    171:
-        'Italian greyhound',
-    172:
-        'whippet',
-    173:
-        'Ibizan hound, Ibizan Podenco',
-    174:
-        'Norwegian elkhound, elkhound',
-    175:
-        'otterhound, otter hound',
-    176:
-        'Saluki, gazelle hound',
-    177:
-        'Scottish deerhound, deerhound',
-    178:
-        'Weimaraner',
-    179:
-        'Staffordshire bullterrier, Staffordshire bull terrier',
-    180:
-        'American Staffordshire terrier, Staffordshire terrier, American pit '
-        'bull terrier, pit bull terrier',
-    181:
-        'Bedlington terrier',
-    182:
-        'Border terrier',
-    183:
-        'Kerry blue terrier',
-    184:
-        'Irish terrier',
-    185:
-        'Norfolk terrier',
-    186:
-        'Norwich terrier',
-    187:
-        'Yorkshire terrier',
-    188:
-        'wire-haired fox terrier',
-    189:
-        'Lakeland terrier',
-    190:
-        'Sealyham terrier, Sealyham',
-    191:
-        'Airedale, Airedale terrier',
-    192:
-        'cairn, cairn terrier',
-    193:
-        'Australian terrier',
-    194:
-        'Dandie Dinmont, Dandie Dinmont terrier',
-    195:
-        'Boston bull, Boston terrier',
-    196:
-        'miniature schnauzer',
-    197:
-        'giant schnauzer',
-    198:
-        'standard schnauzer',
-    199:
-        'Scotch terrier, Scottish terrier, Scottie',
-    200:
-        'Tibetan terrier, chrysanthemum dog',
-    201:
-        'silky terrier, Sydney silky',
-    202:
-        'soft-coated wheaten terrier',
-    203:
-        'West Highland white terrier',
-    204:
-        'Lhasa, Lhasa apso',
-    205:
-        'flat-coated retriever',
-    206:
-        'curly-coated retriever',
-    207:
-        'golden retriever',
-    208:
-        'Labrador retriever',
-    209:
-        'Chesapeake Bay retriever',
-    210:
-        'German short-haired pointer',
-    211:
-        'vizsla, Hungarian pointer',
-    212:
-        'English setter',
-    213:
-        'Irish setter, red setter',
-    214:
-        'Gordon setter',
-    215:
-        'Brittany spaniel',
-    216:
-        'clumber, clumber spaniel',
-    217:
-        'English springer, English springer spaniel',
-    218:
-        'Welsh springer spaniel',
-    219:
-        'cocker spaniel, English cocker spaniel, cocker',
-    220:
-        'Sussex spaniel',
-    221:
-        'Irish water spaniel',
-    222:
-        'kuvasz',
-    223:
-        'schipperke',
-    224:
-        'groenendael',
-    225:
-        'malinois',
-    226:
-        'briard',
-    227:
-        'kelpie',
-    228:
-        'komondor',
-    229:
-        'Old English sheepdog, bobtail',
-    230:
-        'Shetland sheepdog, Shetland sheep dog, Shetland',
-    231:
-        'collie',
-    232:
-        'Border collie',
-    233:
-        'Bouvier des Flandres, Bouviers des Flandres',
-    234:
-        'Rottweiler',
-    235:
-        'German shepherd, German shepherd dog, German police dog, alsatian',
-    236:
-        'Doberman, Doberman pinscher',
-    237:
-        'miniature pinscher',
-    238:
-        'Greater Swiss Mountain dog',
-    239:
-        'Bernese mountain dog',
-    240:
-        'Appenzeller',
-    241:
-        'EntleBucher',
-    242:
-        'boxer',
-    243:
-        'bull mastiff',
-    244:
-        'Tibetan mastiff',
-    245:
-        'French bulldog',
-    246:
-        'Great Dane',
-    247:
-        'Saint Bernard, St Bernard',
-    248:
-        'Eskimo dog, husky',
-    249:
-        'malamute, malemute, Alaskan malamute',
-    250:
-        'Siberian husky',
-    251:
-        'dalmatian, coach dog, carriage dog',
-    252:
-        'affenpinscher, monkey pinscher, monkey dog',
-    253:
-        'basenji',
-    254:
-        'pug, pug-dog',
-    255:
-        'Leonberg',
-    256:
-        'Newfoundland, Newfoundland dog',
-    257:
-        'Great Pyrenees',
-    258:
-        'Samoyed, Samoyede',
-    259:
-        'Pomeranian',
-    260:
-        'chow, chow chow',
-    261:
-        'keeshond',
-    262:
-        'Brabancon griffon',
-    263:
-        'Pembroke, Pembroke Welsh corgi',
-    264:
-        'Cardigan, Cardigan Welsh corgi',
-    265:
-        'toy poodle',
-    266:
-        'miniature poodle',
-    267:
-        'standard poodle',
-    268:
-        'Mexican hairless',
-    269:
-        'timber wolf, grey wolf, gray wolf, Canis lupus',
-    270:
-        'white wolf, Arctic wolf, Canis lupus tundrarum',
-    271:
-        'red wolf, maned wolf, Canis rufus, Canis niger',
-    272:
-        'coyote, prairie wolf, brush wolf, Canis latrans',
-    273:
-        'dingo, warrigal, warragal, Canis dingo',
-    274:
-        'dhole, Cuon alpinus',
-    275:
-        'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
-    276:
-        'hyena, hyaena',
-    277:
-        'red fox, Vulpes vulpes',
-    278:
-        'kit fox, Vulpes macrotis',
-    279:
-        'Arctic fox, white fox, Alopex lagopus',
-    280:
-        'grey fox, gray fox, Urocyon cinereoargenteus',
-    281:
-        'tabby, tabby cat',
-    282:
-        'tiger cat',
-    283:
-        'Persian cat',
-    284:
-        'Siamese cat, Siamese',
-    285:
-        'Egyptian cat',
-    286:
-        'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
-    287:
-        'lynx, catamount',
-    288:
-        'leopard, Panthera pardus',
-    289:
-        'snow leopard, ounce, Panthera uncia',
-    290:
-        'jaguar, panther, Panthera onca, Felis onca',
-    291:
-        'lion, king of beasts, Panthera leo',
-    292:
-        'tiger, Panthera tigris',
-    293:
-        'cheetah, chetah, Acinonyx jubatus',
-    294:
-        'brown bear, bruin, Ursus arctos',
-    295:
-        'American black bear, black bear, Ursus americanus, Euarctos americanus',
-    296:
-        'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
-    297:
-        'sloth bear, Melursus ursinus, Ursus ursinus',
-    298:
-        'mongoose',
-    299:
-        'meerkat, mierkat',
-    300:
-        'tiger beetle',
-    301:
-        'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
-    302:
-        'ground beetle, carabid beetle',
-    303:
-        'long-horned beetle, longicorn, longicorn beetle',
-    304:
-        'leaf beetle, chrysomelid',
-    305:
-        'dung beetle',
-    306:
-        'rhinoceros beetle',
-    307:
-        'weevil',
-    308:
-        'fly',
-    309:
-        'bee',
-    310:
-        'ant, emmet, pismire',
-    311:
-        'grasshopper, hopper',
-    312:
-        'cricket',
-    313:
-        'walking stick, walkingstick, stick insect',
-    314:
-        'cockroach, roach',
-    315:
-        'mantis, mantid',
-    316:
-        'cicada, cicala',
-    317:
-        'leafhopper',
-    318:
-        'lacewing, lacewing fly',
-    319:
-        "dragonfly, darning needle, devil's darning needle, sewing needle, "
-        'snake feeder, snake doctor, mosquito hawk, skeeter hawk',
-    320:
-        'damselfly',
-    321:
-        'admiral',
-    322:
-        'ringlet, ringlet butterfly',
-    323:
-        'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
-    324:
-        'cabbage butterfly',
-    325:
-        'sulphur butterfly, sulfur butterfly',
-    326:
-        'lycaenid, lycaenid butterfly',
-    327:
-        'starfish, sea star',
-    328:
-        'sea urchin',
-    329:
-        'sea cucumber, holothurian',
-    330:
-        'wood rabbit, cottontail, cottontail rabbit',
-    331:
-        'hare',
-    332:
-        'Angora, Angora rabbit',
-    333:
-        'hamster',
-    334:
-        'porcupine, hedgehog',
-    335:
-        'fox squirrel, eastern fox squirrel, Sciurus niger',
-    336:
-        'marmot',
-    337:
-        'beaver',
-    338:
-        'guinea pig, Cavia cobaya',
-    339:
-        'sorrel',
-    340:
-        'zebra',
-    341:
-        'hog, pig, grunter, squealer, Sus scrofa',
-    342:
-        'wild boar, boar, Sus scrofa',
-    343:
-        'warthog',
-    344:
-        'hippopotamus, hippo, river horse, Hippopotamus amphibius',
-    345:
-        'ox',
-    346:
-        'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
-    347:
-        'bison',
-    348:
-        'ram, tup',
-    349:
-        'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky '
-        'Mountain sheep, Ovis canadensis',
-    350:
-        'ibex, Capra ibex',
-    351:
-        'hartebeest',
-    352:
-        'impala, Aepyceros melampus',
-    353:
-        'gazelle',
-    354:
-        'Arabian camel, dromedary, Camelus dromedarius',
-    355:
-        'llama',
-    356:
-        'weasel',
-    357:
-        'mink',
-    358:
-        'polecat, fitch, foulmart, foumart, Mustela putorius',
-    359:
-        'black-footed ferret, ferret, Mustela nigripes',
-    360:
-        'otter',
-    361:
-        'skunk, polecat, wood pussy',
-    362:
-        'badger',
-    363:
-        'armadillo',
-    364:
-        'three-toed sloth, ai, Bradypus tridactylus',
-    365:
-        'orangutan, orang, orangutang, Pongo pygmaeus',
-    366:
-        'gorilla, Gorilla gorilla',
-    367:
-        'chimpanzee, chimp, Pan troglodytes',
-    368:
-        'gibbon, Hylobates lar',
-    369:
-        'siamang, Hylobates syndactylus, Symphalangus syndactylus',
-    370:
-        'guenon, guenon monkey',
-    371:
-        'patas, hussar monkey, Erythrocebus patas',
-    372:
-        'baboon',
-    373:
-        'macaque',
-    374:
-        'langur',
-    375:
-        'colobus, colobus monkey',
-    376:
-        'proboscis monkey, Nasalis larvatus',
-    377:
-        'marmoset',
-    378:
-        'capuchin, ringtail, Cebus capucinus',
-    379:
-        'howler monkey, howler',
-    380:
-        'titi, titi monkey',
-    381:
-        'spider monkey, Ateles geoffroyi',
-    382:
-        'squirrel monkey, Saimiri sciureus',
-    383:
-        'Madagascar cat, ring-tailed lemur, Lemur catta',
-    384:
-        'indri, indris, Indri indri, Indri brevicaudatus',
-    385:
-        'Indian elephant, Elephas maximus',
-    386:
-        'African elephant, Loxodonta africana',
-    387:
-        'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
-    388:
-        'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
-    389:
-        'barracouta, snoek',
-    390:
-        'eel',
-    391:
-        'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',
-    392:
-        'rock beauty, Holocanthus tricolor',
-    393:
-        'anemone fish',
-    394:
-        'sturgeon',
-    395:
-        'gar, garfish, garpike, billfish, Lepisosteus osseus',
-    396:
-        'lionfish',
-    397:
-        'puffer, pufferfish, blowfish, globefish',
-    398:
-        'abacus',
-    399:
-        'abaya',
-    400:
-        "academic gown, academic robe, judge's robe",
-    401:
-        'accordion, piano accordion, squeeze box',
-    402:
-        'acoustic guitar',
-    403:
-        'aircraft carrier, carrier, flattop, attack aircraft carrier',
-    404:
-        'airliner',
-    405:
-        'airship, dirigible',
-    406:
-        'altar',
-    407:
-        'ambulance',
-    408:
-        'amphibian, amphibious vehicle',
-    409:
-        'analog clock',
-    410:
-        'apiary, bee house',
-    411:
-        'apron',
-    412:
-        'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, '
-        'dustbin, trash barrel, trash bin',
-    413:
-        'assault rifle, assault gun',
-    414:
-        'backpack, back pack, knapsack, packsack, rucksack, haversack',
-    415:
-        'bakery, bakeshop, bakehouse',
-    416:
-        'balance beam, beam',
-    417:
-        'balloon',
-    418:
-        'ballpoint, ballpoint pen, ballpen, Biro',
-    419:
-        'Band Aid',
-    420:
-        'banjo',
-    421:
-        'bannister, banister, balustrade, balusters, handrail',
-    422:
-        'barbell',
-    423:
-        'barber chair',
-    424:
-        'barbershop',
-    425:
-        'barn',
-    426:
-        'barometer',
-    427:
-        'barrel, cask',
-    428:
-        'barrow, garden cart, lawn cart, wheelbarrow',
-    429:
-        'baseball',
-    430:
-        'basketball',
-    431:
-        'bassinet',
-    432:
-        'bassoon',
-    433:
-        'bathing cap, swimming cap',
-    434:
-        'bath towel',
-    435:
-        'bathtub, bathing tub, bath, tub',
-    436:
-        'beach wagon, station wagon, wagon, estate car, beach waggon, station '
-        'waggon, waggon',
-    437:
-        'beacon, lighthouse, beacon light, pharos',
-    438:
-        'beaker',
-    439:
-        'bearskin, busby, shako',
-    440:
-        'beer bottle',
-    441:
-        'beer glass',
-    442:
-        'bell cote, bell cot',
-    443:
-        'bib',
-    444:
-        'bicycle-built-for-two, tandem bicycle, tandem',
-    445:
-        'bikini, two-piece',
-    446:
-        'binder, ring-binder',
-    447:
-        'binoculars, field glasses, opera glasses',
-    448:
-        'birdhouse',
-    449:
-        'boathouse',
-    450:
-        'bobsled, bobsleigh, bob',
-    451:
-        'bolo tie, bolo, bola tie, bola',
-    452:
-        'bonnet, poke bonnet',
-    453:
-        'bookcase',
-    454:
-        'bookshop, bookstore, bookstall',
-    455:
-        'bottlecap',
-    456:
-        'bow',
-    457:
-        'bow tie, bow-tie, bowtie',
-    458:
-        'brass, memorial tablet, plaque',
-    459:
-        'brassiere, bra, bandeau',
-    460:
-        'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
-    461:
-        'breastplate, aegis, egis',
-    462:
-        'broom',
-    463:
-        'bucket, pail',
-    464:
-        'buckle',
-    465:
-        'bulletproof vest',
-    466:
-        'bullet train, bullet',
-    467:
-        'butcher shop, meat market',
-    468:
-        'cab, hack, taxi, taxicab',
-    469:
-        'caldron, cauldron',
-    470:
-        'candle, taper, wax light',
-    471:
-        'cannon',
-    472:
-        'canoe',
-    473:
-        'can opener, tin opener',
-    474:
-        'cardigan',
-    475:
-        'car mirror',
-    476:
-        'carousel, carrousel, merry-go-round, roundabout, whirligig',
-    477:
-        "carpenter's kit, tool kit",
-    478:
-        'carton',
-    479:
-        'car wheel',
-    480:
-        'cash machine, cash dispenser, automated teller machine, automatic '
-        'teller machine, automated teller, automatic teller, ATM',
-    481:
-        'cassette',
-    482:
-        'cassette player',
-    483:
-        'castle',
-    484:
-        'catamaran',
-    485:
-        'CD player',
-    486:
-        'cello, violoncello',
-    487:
-        'cellular telephone, cellular phone, cellphone, cell, mobile phone',
-    488:
-        'chain',
-    489:
-        'chainlink fence',
-    490:
-        'chain mail, ring mail, mail, chain armor, chain armour, ring armor, '
-        'ring armour',
-    491:
-        'chain saw, chainsaw',
-    492:
-        'chest',
-    493:
-        'chiffonier, commode',
-    494:
-        'chime, bell, gong',
-    495:
-        'china cabinet, china closet',
-    496:
-        'Christmas stocking',
-    497:
-        'church, church building',
-    498:
-        'cinema, movie theater, movie theatre, movie house, picture palace',
-    499:
-        'cleaver, meat cleaver, chopper',
-    500:
-        'cliff dwelling',
-    501:
-        'cloak',
-    502:
-        'clog, geta, patten, sabot',
-    503:
-        'cocktail shaker',
-    504:
-        'coffee mug',
-    505:
-        'coffeepot',
-    506:
-        'coil, spiral, volute, whorl, helix',
-    507:
-        'combination lock',
-    508:
-        'computer keyboard, keypad',
-    509:
-        'confectionery, confectionary, candy store',
-    510:
-        'container ship, containership, container vessel',
-    511:
-        'convertible',
-    512:
-        'corkscrew, bottle screw',
-    513:
-        'cornet, horn, trumpet, trump',
-    514:
-        'cowboy boot',
-    515:
-        'cowboy hat, ten-gallon hat',
-    516:
-        'cradle',
-    517:
-        'crane',
-    518:
-        'crash helmet',
-    519:
-        'crate',
-    520:
-        'crib, cot',
-    521:
-        'Crock Pot',
-    522:
-        'croquet ball',
-    523:
-        'crutch',
-    524:
-        'cuirass',
-    525:
-        'dam, dike, dyke',
-    526:
-        'desk',
-    527:
-        'desktop computer',
-    528:
-        'dial telephone, dial phone',
-    529:
-        'diaper, nappy, napkin',
-    530:
-        'digital clock',
-    531:
-        'digital watch',
-    532:
-        'dining table, board',
-    533:
-        'dishrag, dishcloth',
-    534:
-        'dishwasher, dish washer, dishwashing machine',
-    535:
-        'disk brake, disc brake',
-    536:
-        'dock, dockage, docking facility',
-    537:
-        'dogsled, dog sled, dog sleigh',
-    538:
-        'dome',
-    539:
-        'doormat, welcome mat',
-    540:
-        'drilling platform, offshore rig',
-    541:
-        'drum, membranophone, tympan',
-    542:
-        'drumstick',
-    543:
-        'dumbbell',
-    544:
-        'Dutch oven',
-    545:
-        'electric fan, blower',
-    546:
-        'electric guitar',
-    547:
-        'electric locomotive',
-    548:
-        'entertainment center',
-    549:
-        'envelope',
-    550:
-        'espresso maker',
-    551:
-        'face powder',
-    552:
-        'feather boa, boa',
-    553:
-        'file, file cabinet, filing cabinet',
-    554:
-        'fireboat',
-    555:
-        'fire engine, fire truck',
-    556:
-        'fire screen, fireguard',
-    557:
-        'flagpole, flagstaff',
-    558:
-        'flute, transverse flute',
-    559:
-        'folding chair',
-    560:
-        'football helmet',
-    561:
-        'forklift',
-    562:
-        'fountain',
-    563:
-        'fountain pen',
-    564:
-        'four-poster',
-    565:
-        'freight car',
-    566:
-        'French horn, horn',
-    567:
-        'frying pan, frypan, skillet',
-    568:
-        'fur coat',
-    569:
-        'garbage truck, dustcart',
-    570:
-        'gasmask, respirator, gas helmet',
-    571:
-        'gas pump, gasoline pump, petrol pump, island dispenser',
-    572:
-        'goblet',
-    573:
-        'go-kart',
-    574:
-        'golf ball',
-    575:
-        'golfcart, golf cart',
-    576:
-        'gondola',
-    577:
-        'gong, tam-tam',
-    578:
-        'gown',
-    579:
-        'grand piano, grand',
-    580:
-        'greenhouse, nursery, glasshouse',
-    581:
-        'grille, radiator grille',
-    582:
-        'grocery store, grocery, food market, market',
-    583:
-        'guillotine',
-    584:
-        'hair slide',
-    585:
-        'hair spray',
-    586:
-        'half track',
-    587:
-        'hammer',
-    588:
-        'hamper',
-    589:
-        'hand blower, blow dryer, blow drier, hair dryer, hair drier',
-    590:
-        'hand-held computer, hand-held microcomputer',
-    591:
-        'handkerchief, hankie, hanky, hankey',
-    592:
-        'hard disc, hard disk, fixed disk',
-    593:
-        'harmonica, mouth organ, harp, mouth harp',
-    594:
-        'harp',
-    595:
-        'harvester, reaper',
-    596:
-        'hatchet',
-    597:
-        'holster',
-    598:
-        'home theater, home theatre',
-    599:
-        'honeycomb',
-    600:
-        'hook, claw',
-    601:
-        'hoopskirt, crinoline',
-    602:
-        'horizontal bar, high bar',
-    603:
-        'horse cart, horse-cart',
-    604:
-        'hourglass',
-    605:
-        'iPod',
-    606:
-        'iron, smoothing iron',
-    607:
-        "jack-o'-lantern",
-    608:
-        'jean, blue jean, denim',
-    609:
-        'jeep, landrover',
-    610:
-        'jersey, T-shirt, tee shirt',
-    611:
-        'jigsaw puzzle',
-    612:
-        'jinrikisha, ricksha, rickshaw',
-    613:
-        'joystick',
-    614:
-        'kimono',
-    615:
-        'knee pad',
-    616:
-        'knot',
-    617:
-        'lab coat, laboratory coat',
-    618:
-        'ladle',
-    619:
-        'lampshade, lamp shade',
-    620:
-        'laptop, laptop computer',
-    621:
-        'lawn mower, mower',
-    622:
-        'lens cap, lens cover',
-    623:
-        'letter opener, paper knife, paperknife',
-    624:
-        'library',
-    625:
-        'lifeboat',
-    626:
-        'lighter, light, igniter, ignitor',
-    627:
-        'limousine, limo',
-    628:
-        'liner, ocean liner',
-    629:
-        'lipstick, lip rouge',
-    630:
-        'Loafer',
-    631:
-        'lotion',
-    632:
-        'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',
-    633:
-        "loupe, jeweler's loupe",
-    634:
-        'lumbermill, sawmill',
-    635:
-        'magnetic compass',
-    636:
-        'mailbag, postbag',
-    637:
-        'mailbox, letter box',
-    638:
-        'maillot',
-    639:
-        'maillot, tank suit',
-    640:
-        'manhole cover',
-    641:
-        'maraca',
-    642:
-        'marimba, xylophone',
-    643:
-        'mask',
-    644:
-        'matchstick',
-    645:
-        'maypole',
-    646:
-        'maze, labyrinth',
-    647:
-        'measuring cup',
-    648:
-        'medicine chest, medicine cabinet',
-    649:
-        'megalith, megalithic structure',
-    650:
-        'microphone, mike',
-    651:
-        'microwave, microwave oven',
-    652:
-        'military uniform',
-    653:
-        'milk can',
-    654:
-        'minibus',
-    655:
-        'miniskirt, mini',
-    656:
-        'minivan',
-    657:
-        'missile',
-    658:
-        'mitten',
-    659:
-        'mixing bowl',
-    660:
-        'mobile home, manufactured home',
-    661:
-        'Model T',
-    662:
-        'modem',
-    663:
-        'monastery',
-    664:
-        'monitor',
-    665:
-        'moped',
-    666:
-        'mortar',
-    667:
-        'mortarboard',
-    668:
-        'mosque',
-    669:
-        'mosquito net',
-    670:
-        'motor scooter, scooter',
-    671:
-        'mountain bike, all-terrain bike, off-roader',
-    672:
-        'mountain tent',
-    673:
-        'mouse, computer mouse',
-    674:
-        'mousetrap',
-    675:
-        'moving van',
-    676:
-        'muzzle',
-    677:
-        'nail',
-    678:
-        'neck brace',
-    679:
-        'necklace',
-    680:
-        'nipple',
-    681:
-        'notebook, notebook computer',
-    682:
-        'obelisk',
-    683:
-        'oboe, hautboy, hautbois',
-    684:
-        'ocarina, sweet potato',
-    685:
-        'odometer, hodometer, mileometer, milometer',
-    686:
-        'oil filter',
-    687:
-        'organ, pipe organ',
-    688:
-        'oscilloscope, scope, cathode-ray oscilloscope, CRO',
-    689:
-        'overskirt',
-    690:
-        'oxcart',
-    691:
-        'oxygen mask',
-    692:
-        'packet',
-    693:
-        'paddle, boat paddle',
-    694:
-        'paddlewheel, paddle wheel',
-    695:
-        'padlock',
-    696:
-        'paintbrush',
-    697:
-        "pajama, pyjama, pj's, jammies",
-    698:
-        'palace',
-    699:
-        'panpipe, pandean pipe, syrinx',
-    700:
-        'paper towel',
-    701:
-        'parachute, chute',
-    702:
-        'parallel bars, bars',
-    703:
-        'park bench',
-    704:
-        'parking meter',
-    705:
-        'passenger car, coach, carriage',
-    706:
-        'patio, terrace',
-    707:
-        'pay-phone, pay-station',
-    708:
-        'pedestal, plinth, footstall',
-    709:
-        'pencil box, pencil case',
-    710:
-        'pencil sharpener',
-    711:
-        'perfume, essence',
-    712:
-        'Petri dish',
-    713:
-        'photocopier',
-    714:
-        'pick, plectrum, plectron',
-    715:
-        'pickelhaube',
-    716:
-        'picket fence, paling',
-    717:
-        'pickup, pickup truck',
-    718:
-        'pier',
-    719:
-        'piggy bank, penny bank',
-    720:
-        'pill bottle',
-    721:
-        'pillow',
-    722:
-        'ping-pong ball',
-    723:
-        'pinwheel',
-    724:
-        'pirate, pirate ship',
-    725:
-        'pitcher, ewer',
-    726:
-        "plane, carpenter's plane, woodworking plane",
-    727:
-        'planetarium',
-    728:
-        'plastic bag',
-    729:
-        'plate rack',
-    730:
-        'plow, plough',
-    731:
-        "plunger, plumber's helper",
-    732:
-        'Polaroid camera, Polaroid Land camera',
-    733:
-        'pole',
-    734:
-        'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',
-    735:
-        'poncho',
-    736:
-        'pool table, billiard table, snooker table',
-    737:
-        'pop bottle, soda bottle',
-    738:
-        'pot, flowerpot',
-    739:
-        "potter's wheel",
-    740:
-        'power drill',
-    741:
-        'prayer rug, prayer mat',
-    742:
-        'printer',
-    743:
-        'prison, prison house',
-    744:
-        'projectile, missile',
-    745:
-        'projector',
-    746:
-        'puck, hockey puck',
-    747:
-        'punching bag, punch bag, punching ball, punchball',
-    748:
-        'purse',
-    749:
-        'quill, quill pen',
-    750:
-        'quilt, comforter, comfort, puff',
-    751:
-        'racer, race car, racing car',
-    752:
-        'racket, racquet',
-    753:
-        'radiator',
-    754:
-        'radio, wireless',
-    755:
-        'radio telescope, radio reflector',
-    756:
-        'rain barrel',
-    757:
-        'recreational vehicle, RV, R.V.',
-    758:
-        'reel',
-    759:
-        'reflex camera',
-    760:
-        'refrigerator, icebox',
-    761:
-        'remote control, remote',
-    762:
-        'restaurant, eating house, eating place, eatery',
-    763:
-        'revolver, six-gun, six-shooter',
-    764:
-        'rifle',
-    765:
-        'rocking chair, rocker',
-    766:
-        'rotisserie',
-    767:
-        'rubber eraser, rubber, pencil eraser',
-    768:
-        'rugby ball',
-    769:
-        'rule, ruler',
-    770:
-        'running shoe',
-    771:
-        'safe',
-    772:
-        'safety pin',
-    773:
-        'saltshaker, salt shaker',
-    774:
-        'sandal',
-    775:
-        'sarong',
-    776:
-        'sax, saxophone',
-    777:
-        'scabbard',
-    778:
-        'scale, weighing machine',
-    779:
-        'school bus',
-    780:
-        'schooner',
-    781:
-        'scoreboard',
-    782:
-        'screen, CRT screen',
-    783:
-        'screw',
-    784:
-        'screwdriver',
-    785:
-        'seat belt, seatbelt',
-    786:
-        'sewing machine',
-    787:
-        'shield, buckler',
-    788:
-        'shoe shop, shoe-shop, shoe store',
-    789:
-        'shoji',
-    790:
-        'shopping basket',
-    791:
-        'shopping cart',
-    792:
-        'shovel',
-    793:
-        'shower cap',
-    794:
-        'shower curtain',
-    795:
-        'ski',
-    796:
-        'ski mask',
-    797:
-        'sleeping bag',
-    798:
-        'slide rule, slipstick',
-    799:
-        'sliding door',
-    800:
-        'slot, one-armed bandit',
-    801:
-        'snorkel',
-    802:
-        'snowmobile',
-    803:
-        'snowplow, snowplough',
-    804:
-        'soap dispenser',
-    805:
-        'soccer ball',
-    806:
-        'sock',
-    807:
-        'solar dish, solar collector, solar furnace',
-    808:
-        'sombrero',
-    809:
-        'soup bowl',
-    810:
-        'space bar',
-    811:
-        'space heater',
-    812:
-        'space shuttle',
-    813:
-        'spatula',
-    814:
-        'speedboat',
-    815:
-        "spider web, spider's web",
-    816:
-        'spindle',
-    817:
-        'sports car, sport car',
-    818:
-        'spotlight, spot',
-    819:
-        'stage',
-    820:
-        'steam locomotive',
-    821:
-        'steel arch bridge',
-    822:
-        'steel drum',
-    823:
-        'stethoscope',
-    824:
-        'stole',
-    825:
-        'stone wall',
-    826:
-        'stopwatch, stop watch',
-    827:
-        'stove',
-    828:
-        'strainer',
-    829:
-        'streetcar, tram, tramcar, trolley, trolley car',
-    830:
-        'stretcher',
-    831:
-        'studio couch, day bed',
-    832:
-        'stupa, tope',
-    833:
-        'submarine, pigboat, sub, U-boat',
-    834:
-        'suit, suit of clothes',
-    835:
-        'sundial',
-    836:
-        'sunglass',
-    837:
-        'sunglasses, dark glasses, shades',
-    838:
-        'sunscreen, sunblock, sun blocker',
-    839:
-        'suspension bridge',
-    840:
-        'swab, swob, mop',
-    841:
-        'sweatshirt',
-    842:
-        'swimming trunks, bathing trunks',
-    843:
-        'swing',
-    844:
-        'switch, electric switch, electrical switch',
-    845:
-        'syringe',
-    846:
-        'table lamp',
-    847:
-        'tank, army tank, armored combat vehicle, armoured combat vehicle',
-    848:
-        'tape player',
-    849:
-        'teapot',
-    850:
-        'teddy, teddy bear',
-    851:
-        'television, television system',
-    852:
-        'tennis ball',
-    853:
-        'thatch, thatched roof',
-    854:
-        'theater curtain, theatre curtain',
-    855:
-        'thimble',
-    856:
-        'thresher, thrasher, threshing machine',
-    857:
-        'throne',
-    858:
-        'tile roof',
-    859:
-        'toaster',
-    860:
-        'tobacco shop, tobacconist shop, tobacconist',
-    861:
-        'toilet seat',
-    862:
-        'torch',
-    863:
-        'totem pole',
-    864:
-        'tow truck, tow car, wrecker',
-    865:
-        'toyshop',
-    866:
-        'tractor',
-    867:
-        'trailer truck, tractor trailer, trucking rig, rig, articulated lorry,'
-        ' semi',
-    868:
-        'tray',
-    869:
-        'trench coat',
-    870:
-        'tricycle, trike, velocipede',
-    871:
-        'trimaran',
-    872:
-        'tripod',
-    873:
-        'triumphal arch',
-    874:
-        'trolleybus, trolley coach, trackless trolley',
-    875:
-        'trombone',
-    876:
-        'tub, vat',
-    877:
-        'turnstile',
-    878:
-        'typewriter keyboard',
-    879:
-        'umbrella',
-    880:
-        'unicycle, monocycle',
-    881:
-        'upright, upright piano',
-    882:
-        'vacuum, vacuum cleaner',
-    883:
-        'vase',
-    884:
-        'vault',
-    885:
-        'velvet',
-    886:
-        'vending machine',
-    887:
-        'vestment',
-    888:
-        'viaduct',
-    889:
-        'violin, fiddle',
-    890:
-        'volleyball',
-    891:
-        'waffle iron',
-    892:
-        'wall clock',
-    893:
-        'wallet, billfold, notecase, pocketbook',
-    894:
-        'wardrobe, closet, press',
-    895:
-        'warplane, military plane',
-    896:
-        'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
-    897:
-        'washer, automatic washer, washing machine',
-    898:
-        'water bottle',
-    899:
-        'water jug',
-    900:
-        'water tower',
-    901:
-        'whiskey jug',
-    902:
-        'whistle',
-    903:
-        'wig',
-    904:
-        'window screen',
-    905:
-        'window shade',
-    906:
-        'Windsor tie',
-    907:
-        'wine bottle',
-    908:
-        'wing',
-    909:
-        'wok',
-    910:
-        'wooden spoon',
-    911:
-        'wool, woolen, woollen',
-    912:
-        'worm fence, snake fence, snake-rail fence, Virginia fence',
-    913:
-        'wreck',
-    914:
-        'yawl',
-    915:
-        'yurt',
-    916:
-        'web site, website, internet site, site',
-    917:
-        'comic book',
-    918:
-        'crossword puzzle, crossword',
-    919:
-        'street sign',
-    920:
-        'traffic light, traffic signal, stoplight',
-    921:
-        'book jacket, dust cover, dust jacket, dust wrapper',
-    922:
-        'menu',
-    923:
-        'plate',
-    924:
-        'guacamole',
-    925:
-        'consomme',
-    926:
-        'hot pot, hotpot',
-    927:
-        'trifle',
-    928:
-        'ice cream, icecream',
-    929:
-        'ice lolly, lolly, lollipop, popsicle',
-    930:
-        'French loaf',
-    931:
-        'bagel, beigel',
-    932:
-        'pretzel',
-    933:
-        'cheeseburger',
-    934:
-        'hotdog, hot dog, red hot',
-    935:
-        'mashed potato',
-    936:
-        'head cabbage',
-    937:
-        'broccoli',
-    938:
-        'cauliflower',
-    939:
-        'zucchini, courgette',
-    940:
-        'spaghetti squash',
-    941:
-        'acorn squash',
-    942:
-        'butternut squash',
-    943:
-        'cucumber, cuke',
-    944:
-        'artichoke, globe artichoke',
-    945:
-        'bell pepper',
-    946:
-        'cardoon',
-    947:
-        'mushroom',
-    948:
-        'Granny Smith',
-    949:
-        'strawberry',
-    950:
-        'orange',
-    951:
-        'lemon',
-    952:
-        'fig',
-    953:
-        'pineapple, ananas',
-    954:
-        'banana',
-    955:
-        'jackfruit, jak, jack',
-    956:
-        'custard apple',
-    957:
-        'pomegranate',
-    958:
-        'hay',
-    959:
-        'carbonara',
-    960:
-        'chocolate sauce, chocolate syrup',
-    961:
-        'dough',
-    962:
-        'meat loaf, meatloaf',
-    963:
-        'pizza, pizza pie',
-    964:
-        'potpie',
-    965:
-        'burrito',
-    966:
-        'red wine',
-    967:
-        'espresso',
-    968:
-        'cup',
-    969:
-        'eggnog',
-    970:
-        'alp',
-    971:
-        'bubble',
-    972:
-        'cliff, drop, drop-off',
-    973:
-        'coral reef',
-    974:
-        'geyser',
-    975:
-        'lakeside, lakeshore',
-    976:
-        'promontory, headland, head, foreland',
-    977:
-        'sandbar, sand bar',
-    978:
-        'seashore, coast, seacoast, sea-coast',
-    979:
-        'valley, vale',
-    980:
-        'volcano',
-    981:
-        'ballplayer, baseball player',
-    982:
-        'groom, bridegroom',
-    983:
-        'scuba diver',
-    984:
-        'rapeseed',
-    985:
-        'daisy',
-    986:
-        "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, "
-        'Cypripedium parviflorum',
-    987:
-        'corn',
-    988:
-        'acorn',
-    989:
-        'hip, rose hip, rosehip',
-    990:
-        'buckeye, horse chestnut, conker',
-    991:
-        'coral fungus',
-    992:
-        'agaric',
-    993:
-        'gyromitra',
-    994:
-        'stinkhorn, carrion fungus',
-    995:
-        'earthstar',
-    996:
-        'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola '
-        'frondosa',
-    997:
-        'bolete',
-    998:
-        'ear, spike, capitulum',
-    999:
-        'toilet tissue, toilet paper, bathroom tissue'
-}
diff --git a/lit_nlp/examples/models/mobilenet.py b/lit_nlp/examples/models/mobilenet.py
deleted file mode 100644
index a9fc7105..00000000
--- a/lit_nlp/examples/models/mobilenet.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""MobileNet model trained on ImageNet dataset."""
-
-from lit_nlp.api import model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import imagenet_labels
-from lit_nlp.lib import image_utils
-import numpy as np
-import tensorflow as tf
-
-# Internal shape of the model input (h, w, c).
-IMAGE_SHAPE = (224, 224, 3)
-
-
-class MobileNet(model.BatchedModel):
-  """MobileNet model trained on ImageNet dataset."""
-
-  def __init__(self, name='mobilenet_v2') -> None:
-    # Initialize imagenet labels.
-    self.labels = [''] * len(imagenet_labels.IMAGENET_2012_LABELS)
-    self.label_to_idx = {}
-    for i, l in imagenet_labels.IMAGENET_2012_LABELS.items():
-      l = l.split(',', 1)[0]
-      self.labels[i] = l
-      self.label_to_idx[l] = i
-
-    if name == 'mobilenet_v2':
-      self.model = tf.keras.applications.mobilenet_v2.MobileNetV2()
-    elif name == 'mobilenet':
-      self.model = tf.keras.applications.mobilenet.MobileNet()
-
-  def predict_minibatch(
-      self, input_batch: list[lit_types.JsonDict]
-  ) -> list[lit_types.JsonDict]:
-    output = []
-    for example in input_batch:
-      # Convert input to the model acceptable format.
-      img_data = example['image']
-      if isinstance(img_data, str):
-        img_data = image_utils.convert_image_str_to_array(img_data, IMAGE_SHAPE)
-      # Get predictions.
-      x = img_data[np.newaxis, ...]
-      x = tf.convert_to_tensor(x)
-      preds = self.model(x).numpy()[0]
-      # Determine the gradient target.
-      if (grad_target := example.get('label')) is None:
-        grad_target_idx = np.argmax(preds)
-      else:
-        grad_target_idx = self.label_to_idx[grad_target]
-      # Calculate gradients.
-      with tf.GradientTape() as tape:
-        tape.watch(x)
-        y = self.model(x)[0, grad_target_idx]
-        grads = tape.gradient(y, x).numpy()[0]
-      # Add results to the output.
-      output.append({
-          'preds': preds,
-          'grads': grads,
-      })
-
-    return output
-
-  def input_spec(self):
-    return {
-        'image': lit_types.ImageBytes(),
-        # If `grad_target` is not specified then the label with the highest
-        # predicted score is used as the gradient target.
-        'label': lit_types.CategoryLabel(vocab=self.labels, required=False),
-    }
-
-  def output_spec(self):
-    return {
-        'preds': lit_types.MulticlassPreds(
-            vocab=self.labels, autosort=True, parent='label'
-        ),
-        'grads': lit_types.ImageGradients(
-            align='image', grad_target_field_key='label'
-        ),
-    }
diff --git a/lit_nlp/examples/models/mobilenet_test.py b/lit_nlp/examples/models/mobilenet_test.py
deleted file mode 100644
index 6df157cc..00000000
--- a/lit_nlp/examples/models/mobilenet_test.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from absl.testing import absltest
-from absl.testing import parameterized
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types
-from lit_nlp.examples.models import mobilenet
-from lit_nlp.lib import image_utils
-import numpy as np
-from PIL import Image as PILImage
-
-
-class MobileNetTest(parameterized.TestCase):
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='compatible_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'image': types.ImageBytes()},
-          expected=True,
-      ),
-      dict(
-          testcase_name='empty_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={},
-          expected=False,
-      ),
-      dict(
-          testcase_name='no_images_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'text': types.TextSegment()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='wrong_keys_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'wrong_image_key': types.ImageBytes()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='compatible_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'image': types.ImageBytes()},
-          expected=True,
-      ),
-      dict(
-          testcase_name='empty_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={},
-          expected=False,
-      ),
-      dict(
-          testcase_name='no_images_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'text': types.TextSegment()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='wrong_keys_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'wrong_image_key': types.ImageBytes()},
-          expected=False,
-      ),
-  )
-  def test_compatibility(
-      self, model_name: str, dataset_spec: types.Spec, expected: bool
-  ):
-    dataset = lit_dataset.Dataset(spec=dataset_spec)
-    model = mobilenet.MobileNet(model_name)
-    self.assertEqual(model.is_compatible_with_dataset(dataset), expected)
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='model_v1',
-          model_name='mobilenet',
-      ),
-      dict(
-          testcase_name='model_v2',
-          model_name='mobilenet_v2',
-      ),
-  )
-  def test_model(self, model_name: str):
-    # Create an input with base64 encoded image.
-    input_1 = {
-        'image': np.zeros(shape=(mobilenet.IMAGE_SHAPE), dtype=np.float32)
-    }
-    # Create an input with image data in Numpy array.
-    pil_image = PILImage.new(mode='RGB', size=(300, 200))
-    input_2 = {'image': image_utils.convert_pil_to_image_str(pil_image)}
-    model = mobilenet.MobileNet(model_name)
-    model_out = model.predict([input_1, input_2])
-    model_out = list(model_out)
-    # Check first output.
-    self.assertIn('preds', model_out[0])
-    self.assertIn('grads', model_out[0])
-    # Check second output.
-    self.assertIn('preds', model_out[1])
-    self.assertIn('grads', model_out[1])
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 19c4dde0..3af3a0eb 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -147,8 +147,6 @@ LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 `SparseMultilabelPreds` and `SparseMultilabel` types.
 
-*   For a basic example on an image labeling task, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
 *   Models should define a `SparseMultilabelPreds` field in their output spec
     with the`vocab=` attribute as the set of class labels, and for each example
     should return a list of class score tuples. Each tuple contains two
@@ -246,11 +244,8 @@ NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don't break existing checked-in code with
 such a change.
 
-*   See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a
-    dataset containing images, including converting images to base64 encoded
-    strings.
-*   For a demo of an image classifier, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
+*   See [examples/dalle/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/dalle/demo.py) for a demo on
+    image generation and how LIT handles image bytes.
 
 ## Token-based Salience
 
@@ -488,8 +483,6 @@ using either KerasNLP or Transformers.
 *   Transformers model wrappers:
     [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
 
-
-
 ## Salience Clustering
 
 LIT includes a basic implementation of the salience clustering method from
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index dfc77b50..c919790d 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -169,13 +169,3 @@ https://pair-code.github.io/lit/tutorials/coref
 *   Use binary classifier threshold setters to find best thresholds for slices
     of examples to achieve specific fairness constraints, such as demographic
     parity.
-
-### Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \
-**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py)
-
-*   Classification on ImageNet labels using a MobileNet model.
-*   Showing using of LIT on image data.
-*   Explore results of multiple gradient-based image saliency techniques in the
-    Salience Maps module.
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index de6a5b57..9ff5b3d5 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -17,8 +17,8 @@ works with any modern ML framework. For more information, see
 In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
 
-*   [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) -
-    image classification, using a Mobilenet model.
+*   [DALL·E demo](google3/third_party/py/lit_nlp/examples/dalle/) - image
+    generation, using DALL·E Mini model.
 *   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the
@@ -99,8 +99,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir`
 is specified, may be cached to disk.
 
 LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to
-disk, then pass the `--demo_mode` runtime flag to the LIT server.
+disk. If you do not want the tool to be able to write edited datapoints to disk,
+then pass the `--demo_mode` runtime flag to the LIT server.
 
 ### I have proprietary data. Is LIT secure for my team to use?
 

From c2fb41b4945edb91fac973cf0ddbca48c6257511 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Wed, 5 Jun 2024 07:42:32 -0700
Subject: [PATCH 12/47] Remove the toxicity demos from the LIT examples.

PiperOrigin-RevId: 640521996
---
 lit_nlp/examples/datasets/classification.py | 32 ---------
 lit_nlp/examples/models/glue_models.py      | 22 ------
 lit_nlp/examples/tools/glue_trainer.py      |  5 --
 lit_nlp/examples/toxicity_demo.py           | 75 ---------------------
 website/sphinx_src/components.md            |  4 +-
 5 files changed, 1 insertion(+), 137 deletions(-)
 delete mode 100644 lit_nlp/examples/toxicity_demo.py

diff --git a/lit_nlp/examples/datasets/classification.py b/lit_nlp/examples/datasets/classification.py
index 92a7e916..0a022f1c 100644
--- a/lit_nlp/examples/datasets/classification.py
+++ b/lit_nlp/examples/datasets/classification.py
@@ -187,35 +187,3 @@ def spec(self) -> lit_types.Spec:
         "label": lit_types.CategoryLabel(vocab=self.LABELS),
     }
 
-
-class ToxicityData(lit_dataset.Dataset):
-  """Jigsaw toxicity dataset; see https://www.tensorflow.org/datasets/catalog/wikipedia_toxicity_subtypes."""
-
-  LABELS = ["non-toxic", "toxic"]
-
-  def __init__(self, split="test", max_seq_len=500):
-    """Dataset constructor, loads the data into memory."""
-    raw_examples = load_tfds("wikipedia_toxicity_subtypes", split=split)
-    self._examples = []  # populate this with data records
-    for record in raw_examples:
-      self._examples.append({
-          "sentence": record["text"].decode("utf-8"),
-          "label": self.LABELS[int(record["toxicity"])],
-          "identity_attack": bool(int(record["identity_attack"])),
-          "insult": bool(int(record["insult"])),
-          "obscene": bool(int(record["obscene"])),
-          "severe_toxicity": bool(int(record["severe_toxicity"])),
-          "threat": bool(int(record["threat"]))
-      })
-
-  def spec(self) -> lit_types.Spec:
-    """Dataset spec, which should match the model"s input_spec()."""
-    return {
-        "sentence": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS),
-        "identity_attack": lit_types.Boolean(),
-        "insult": lit_types.Boolean(),
-        "obscene": lit_types.Boolean(),
-        "severe_toxicity": lit_types.Boolean(),
-        "threat": lit_types.Boolean()
-    }
diff --git a/lit_nlp/examples/models/glue_models.py b/lit_nlp/examples/models/glue_models.py
index 466f3172..a70cb1f0 100644
--- a/lit_nlp/examples/models/glue_models.py
+++ b/lit_nlp/examples/models/glue_models.py
@@ -648,25 +648,3 @@ def input_spec(self):
     ret = super().input_spec()
     ret[self.config.label_name] = lit_types.Scalar(min_val=0, max_val=5)
     return ret
-
-
-class ToxicityModel(GlueModel):
-  """Classification model on Jigsaw Toxicity Dataset."""
-
-  def __init__(self, *args, **kw):
-    super().__init__(
-        *args,
-        text_a_name="sentence",
-        text_b_name=None,
-        labels=["non-toxic", "toxic"],
-        null_label_idx=0,
-        **kw)
-
-  def output_spec(self) -> Spec:
-    ret = super().output_spec()
-    ret["probas"] = lit_types.MulticlassPreds(
-        parent=self.config.label_name,
-        vocab=self.config.labels,
-        null_idx=self.config.null_label_idx,
-        threshold=0.3)
-    return ret
diff --git a/lit_nlp/examples/tools/glue_trainer.py b/lit_nlp/examples/tools/glue_trainer.py
index 034668e8..e217f8fb 100644
--- a/lit_nlp/examples/tools/glue_trainer.py
+++ b/lit_nlp/examples/tools/glue_trainer.py
@@ -25,7 +25,6 @@
 from absl import flags
 from absl import logging
 
-from lit_nlp.examples.datasets import classification
 from lit_nlp.examples.datasets import glue
 from lit_nlp.examples.models import glue_models
 from lit_nlp.lib import serialize
@@ -130,10 +129,6 @@ def main(argv: Sequence[str]) -> None:
     train_data = glue.STSBData("train")
     val_data = glue.STSBData("validation")
     model = glue_models.STSBModel(_ENCODER_NAME.value)
-  elif _TASK.value == "toxicity":
-    train_data = classification.ToxicityData("train")
-    val_data = classification.ToxicityData("test")
-    model = glue_models.ToxicityModel(_ENCODER_NAME.value)
   else:
     raise ValueError(f"Unrecognized task name: '{_TASK.value:s}'")
 
diff --git a/lit_nlp/examples/toxicity_demo.py b/lit_nlp/examples/toxicity_demo.py
deleted file mode 100644
index f14a4c4e..00000000
--- a/lit_nlp/examples/toxicity_demo.py
+++ /dev/null
@@ -1,75 +0,0 @@
-r"""LIT Demo for a Toxicity model.
-
-To run locally:
-  python -m lit_nlp.examples.toxicity_demo --port=5432
-
-Once you see the ASCII-art LIT logo, navigate to localhost:5432 to access the
-demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.models import glue_models
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path",
-    "https://storage.googleapis.com/what-if-tool-resources/lit-models/toxicity.tar.gz",
-    "Path to saved model (from transformers library).",
-)
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples", 1000, "Maximum number of examples to load into LIT. ")
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info(
-        "toxicity_demo:get_wsgi_app() called with unused args: %s", unused
-    )
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  model_path = _MODEL_PATH.value
-  logging.info("Working directory: %s", model_path)
-
-  # Load our trained model.
-  models = {"toxicity": glue_models.ToxicityModel(model_path)}
-  datasets = {"toxicity_test": classification.ToxicityData("test")}
-
-  # Truncate datasets if --max_examples is set.
-  for name in datasets:
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-    datasets[name] = datasets[name].slice[:_MAX_EXAMPLES.value]
-    logging.info("  truncated to %d examples", len(datasets[name]))
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 3af3a0eb..c9a959c0 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -116,9 +116,7 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types.
 *   A negative class can be designated using the `null_idx` attribute of
     `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as
     precision, recall, F1 will be computed for the remaining classes. AUC and
-    AUCPR will be computed for binary classification tasks. For an example, see
-    the
-    [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180).
+    AUCPR will be computed for binary classification tasks.
 *   If `null_idx` is set and there is only one other class, the other class
     (often, class `1`) is treated as a positive class, and the LIT UI can be
     used to change the classification threshold. If `null_idx` is set and there

From dd196e941058a1d4246b3df3a3c37595f9791b18 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Wed, 5 Jun 2024 11:49:27 -0700
Subject: [PATCH 13/47] Remove the xnli demos from the LIT examples.

PiperOrigin-RevId: 640597959
---
 lit_nlp/examples/datasets/classification.py | 130 --------------------
 lit_nlp/examples/xnli_demo.py               | 103 ----------------
 website/sphinx_src/demos.md                 |  13 --
 website/sphinx_src/faq.md                   |   3 -
 4 files changed, 249 deletions(-)
 delete mode 100644 lit_nlp/examples/xnli_demo.py

diff --git a/lit_nlp/examples/datasets/classification.py b/lit_nlp/examples/datasets/classification.py
index 0a022f1c..0d1e1df3 100644
--- a/lit_nlp/examples/datasets/classification.py
+++ b/lit_nlp/examples/datasets/classification.py
@@ -1,10 +1,8 @@
 """Text classification datasets, including single- and two-sentence tasks."""
 from typing import Optional
 
-from absl import logging
 from lit_nlp.api import dataset as lit_dataset
 from lit_nlp.api import types as lit_types
-import pandas as pd
 import tensorflow_datasets as tfds
 
 
@@ -17,134 +15,6 @@ def load_tfds(*args, **kw):
       tfds.as_numpy(tfds.load(*args, download=True, try_gcs=True, **kw)))
 
 
-class MNLIDataFromTSV(lit_dataset.Dataset):
-  """MultiNLI dataset, from TSV.
-
-  Compared to the TFDS version, this includes:
-  - label2 field for binary labels, with same schema as HANS
-  - genre labels, for stratified analysis
-
-  The downside is that you need to download the data from
-  https://gluebenchmark.com/tasks, and provide a path to the .tsv file.
-  """
-
-  LABELS3 = ["entailment", "neutral", "contradiction"]
-  LABELS2 = ["non-entailment", "entailment"]
-
-  def binarize_label(self, label):
-    return "entailment" if label == "entailment" else "non-entailment"
-
-  def __init__(self, path: str):
-    self._examples = self.load_datapoints(path)
-
-  def load_datapoints(self, path: str):
-    with open(path) as fd:
-      df = pd.read_csv(fd, sep="\t")
-    # pylint: disable=g-complex-comprehension
-    return [{
-        "premise": row["sentence1"],
-        "hypothesis": row["sentence2"],
-        "label": row["gold_label"],
-        "label2": self.binarize_label(row["gold_label"]),
-        "genre": row["genre"],
-    } for _, row in df.iterrows()]
-    # pylint: enable=g-complex-comprehension
-
-  def load(self, path: str):
-    datapoints = self.load_datapoints(path)
-    return lit_dataset.Dataset(base=self, examples=datapoints)
-
-  def save(self, examples: list[lit_types.IndexedInput], path: str):
-    example_data = [ex["data"] for ex in examples]
-    df = pd.DataFrame(example_data).rename(columns={
-        "premise": "sentence1",
-        "hypothesis": "sentence2",
-        "label": "gold_label",
-    })
-    with open(path, "w") as fd:
-      df.to_csv(fd, sep="\t")
-
-  def spec(self) -> lit_types.Spec:
-    """Should match MnliModel's input_spec()."""
-    return {
-        "premise": lit_types.TextSegment(),
-        "hypothesis": lit_types.TextSegment(),
-        # 'label' for 3-way NLI labels, 'label2' for binarized.
-        "label": lit_types.CategoryLabel(vocab=self.LABELS3),
-        "label2": lit_types.CategoryLabel(vocab=self.LABELS2),
-        "genre": lit_types.CategoryLabel(),
-    }
-
-
-class XNLIData(lit_dataset.Dataset):
-  """Cross-lingual NLI; see https://cims.nyu.edu/~sbowman/xnli/."""
-
-  LABELS = ["entailment", "neutral", "contradiction"]
-
-  def _process_example(self, ex, languages: list[str]):
-    # Hypothesis is stored as parallel arrays, so make a map.
-    hyp_map = {
-        lang.decode("utf-8"): hyp.decode("utf-8") for lang, hyp in zip(
-            ex["hypothesis"]["language"], ex["hypothesis"]["translation"])
-    }
-    for lang in languages:
-      if lang not in hyp_map:
-        logging.warning("Missing hypothesis (lang=%s) for premise '%s'", lang,
-                        ex["premise"]["lang"].decode("utf-8"))
-        continue
-      yield {
-          "premise": ex["premise"][lang].decode("utf-8"),
-          "hypothesis": hyp_map[lang],
-          "label": self.LABELS[ex["label"]],
-          "language": lang,
-      }
-
-  def __init__(self, split: str, languages=("en", "es", "hi", "zh")):
-    self._examples = []
-    for ex in load_tfds("xnli", split=split):
-      # Each TFDS example contains all the translations; we unpack to individual
-      # (premise, hypothesis) pairs that are compatible with a standard NLI
-      # model.
-      self._examples.extend(self._process_example(ex, languages))
-
-  def spec(self):
-    return {
-        "premise": lit_types.TextSegment(),
-        "hypothesis": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS),
-        "language": lit_types.CategoryLabel(),
-    }
-
-
-class HansNLIData(lit_dataset.Dataset):
-  """HANS NLI challenge set (https://arxiv.org/abs/1902.01007); 30k examples."""
-
-  LABELS = ["non-entailment", "entailment"]
-
-  def __init__(self, path: str):
-    with open(path) as fd:
-      df = pd.read_csv(fd, sep="\t", header=0)
-    # pylint: disable=g-complex-comprehension
-    self._examples = [{
-        "premise": row["sentence1"],
-        "hypothesis": row["sentence2"],
-        "label2": row["gold_label"],
-        "heuristic": row["heuristic"],
-        "template": row["template"],
-    } for _, row in df.iterrows()]
-    # pylint: enable=g-complex-comprehension
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        "premise": lit_types.TextSegment(),
-        "hypothesis": lit_types.TextSegment(),
-        # 'label2' for 2-way NLI labels
-        "label2": lit_types.CategoryLabel(vocab=self.LABELS),
-        "heuristic": lit_types.CategoryLabel(),
-        "template": lit_types.CategoryLabel(),
-    }
-
-
 class IMDBData(lit_dataset.Dataset):
   """IMDB reviews dataset; see http://ai.stanford.edu/~amaas/data/sentiment/."""
 
diff --git a/lit_nlp/examples/xnli_demo.py b/lit_nlp/examples/xnli_demo.py
deleted file mode 100644
index 4f209e39..00000000
--- a/lit_nlp/examples/xnli_demo.py
+++ /dev/null
@@ -1,103 +0,0 @@
-r"""Example demo for multilingual NLI on the XNLI eval set.
-
-To run locally with our trained model:
-  python -m lit_nlp.examples.xnli_demo --port=5432
-
-Then navigate to localhost:5432 to access the demo UI.
-
-To train a model for this task, use tools/glue_trainer.py or your favorite
-trainer script to fine-tune a multilingual encoder, such as
-bert-base-multilingual-cased, on the mnli task.
-
-Note: the LIT UI can handle around 10k examples comfortably, depending on your
-hardware. The monolingual (english) eval sets for MNLI are about 9.8k each,
-while each language for XNLI is about 2.5k examples, so we recommend using the
---languages flag to load only the languages you're interested in.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.models import glue_models
-from lit_nlp.lib import file_cache
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_LANGUAGES = flags.DEFINE_list(
-    "languages", ["en", "es", "hi", "zh"],
-    "Languages to load from XNLI. Available languages: "
-    "ar,bg,de,el,en,es,fr,hi,ru,sw,th,tr,ur,zh,vi"
-)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path",
-    "https://storage.googleapis.com/what-if-tool-resources/lit-models/mbert_mnli.tar.gz",
-    (
-        "Path to fine-tuned model files. Expects model to be in standard "
-        "transformers format, e.g. as saved by model.save_pretrained() and "
-        "tokenizer.save_pretrained()."
-    ),
-)
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples", None, "Maximum number of examples to load into LIT. "
-    "Note: MNLI eval set is 10k examples, so will take a while to run and may "
-    "be slow on older machines. Set --max_examples=200 for a quick start.")
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("xnli_demo:get_wsgi_app() called with unused args: %s", unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Normally path is a directory; if it's an archive file, download and
-  # extract to the transformers cache.
-  model_path = _MODEL_PATH.value
-  if model_path.endswith(".tar.gz"):
-    model_path = file_cache.cached_path(
-        model_path, extract_compressed_file=True)
-
-  models = {"nli": glue_models.MNLIModel(model_path, inference_batch_size=16)}
-  datasets = {
-      "xnli": classification.XNLIData("validation", _LANGUAGES.value),
-      "mnli_dev": glue.MNLIData("validation_matched"),
-      "mnli_dev_mm": glue.MNLIData("validation_mismatched"),
-  }
-
-  # Truncate datasets if --max_examples is set.
-  for name in datasets:
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-    datasets[name] = datasets[name].slice[:_MAX_EXAMPLES.value]
-    logging.info("  truncated to %d examples", len(datasets[name]))
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index c919790d..336d2b43 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -43,19 +43,6 @@ https://pair-code.github.io/lit/demos/.
 Tip: check out a case study for this demo on the public LIT website:
 https://pair-code.github.io/lit/tutorials/sentiment
 
-### Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} -->
-
-**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py)
-
-*   [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of
-    MultiNLI into 14 different languages.
-*   Specify `--languages=en,jp,hi,...` flag to select which languages to load.
-*   NLI as a three-way classification task with two-segment input (premise,
-    hypothesis).
-*   Fine-tuned multilingual BERT model.
-*   Salience methods work with non-whitespace-delimited text, by using the
-    model's wordpiece tokenization.
-
 --------------------------------------------------------------------------------
 
 ## Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} -->
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index 9ff5b3d5..fc70e29c 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -34,9 +34,6 @@ All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
 scripts should work without any modifications. For examples, see:
 
-*   [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) -
-    cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
-    model.
 *   [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) -
     includes WMT data for machine translation
 

From 72fd772fa02c7445f27fb517e667987ea8ab34d7 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Thu, 6 Jun 2024 07:53:55 -0700
Subject: [PATCH 14/47] Remove the is_eval_demo from the LIT examples.

PiperOrigin-RevId: 640897240
---
 lit_nlp/examples/is_eval/datasets.py        |  44 ------
 lit_nlp/examples/is_eval/is_eval_demo.py    | 157 --------------------
 lit_nlp/examples/is_eval/is_eval_trainer.py | 138 -----------------
 lit_nlp/examples/is_eval/models.py          |  48 ------
 4 files changed, 387 deletions(-)
 delete mode 100644 lit_nlp/examples/is_eval/datasets.py
 delete mode 100644 lit_nlp/examples/is_eval/is_eval_demo.py
 delete mode 100644 lit_nlp/examples/is_eval/is_eval_trainer.py
 delete mode 100644 lit_nlp/examples/is_eval/models.py

diff --git a/lit_nlp/examples/is_eval/datasets.py b/lit_nlp/examples/is_eval/datasets.py
deleted file mode 100644
index 25c3d7ed..00000000
--- a/lit_nlp/examples/is_eval/datasets.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Text classification dataset for binary, single input data."""
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-import pandas as pd
-
-
-class SingleInputClassificationFromTSV(lit_dataset.Dataset):
-  """TSV data loader for files having a single input text and a label.
-
-  Files must be in TSV format with 2 columns in this order:
-  1. Input text.
-  2. Numeric label.
-
-  Exported examples have 2 output keys: "sentence" and "label".
-  """
-
-  LABELS = ["0", "1"]
-
-  def __init__(self, path: str, name: str = ""):
-    """Initializes a dataset for the Input Salience Eval demo.
-
-    Args:
-      path: The path from which examples will be loaded.
-      name: Optionally, the name of the dataset. Used by ISEvalModel to
-        determine if the model is intended to be compatible with this dataset.
-    """
-    self._examples = self.load_datapoints(path)
-    self.name = name
-
-  def load_datapoints(self, path: str):
-    with open(path) as fd:
-      df = pd.read_csv(fd, sep="\t", header=None, names=["sentence", "label"])
-    # pylint: disable=g-complex-comprehension
-    return [{
-        "sentence": row["sentence"],
-        "label": self.LABELS[row["label"]],
-    } for _, row in df.iterrows()]
-    # pylint: enable=g-complex-comprehension
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        "sentence": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS),
-    }
diff --git a/lit_nlp/examples/is_eval/is_eval_demo.py b/lit_nlp/examples/is_eval/is_eval_demo.py
deleted file mode 100644
index 0621adef..00000000
--- a/lit_nlp/examples/is_eval/is_eval_demo.py
+++ /dev/null
@@ -1,157 +0,0 @@
-r"""Example demo loading a handful of IS eval models.
-
-To run:
-  blaze run -c opt --config=cuda examples/is_eval:is_eval_demo -- \
-    --port=5432
-"""
-import sys
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.examples.is_eval import datasets
-from lit_nlp.examples.is_eval import models as is_eval_models
-from lit_nlp.lib import file_cache
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-FLAGS.set_default("page_title", "Input Salience Evaluation Demo")
-
-_DOC_STRING = (
-    "# Input Salience Evaluation Demo\nThis demo accompanies our "
-    "[paper](https://arxiv.org/abs/2211.05485) and "
-    "[blogpost](https://ai.googleblog.com/2022/12/will-you-find-these-shortcuts.html)"
-    " \"Will you find these shortcuts?\". We manually inserted one out of "
-    "three artificial data artifacts (shortcuts) into two datasets (SST2, "
-    "Toxicity). In the \"Explanations\" tab you can observe how different "
-    "input salience methods put different weights on the nonsense tokens "
-    "*zeroa*, *onea*, *synt*.")
-
-_MODELS = flags.DEFINE_list(
-    "models",
-    [
-        "sst2_single_token:https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_single_token_bert.tar.gz",
-        "sst2_token_in_context:https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_token_in_context_bert.tar.gz",
-        "sst2_ordered_pair:https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_simple_order_bert.tar.gz",
-        "toxicity_single_token:https://storage.googleapis.com/what-if-tool-resources/lit-models/toxicity_single_token_bert.tar.gz",
-        "toxicity_token_in_context:https://storage.googleapis.com/what-if-tool-resources/lit-models/toxicity_token_in_context_bert.tar.gz",
-        "toxicity_ordered_pair:https://storage.googleapis.com/what-if-tool-resources/lit-models/toxicity_simple_order_bert.tar.gz",
-    ],
-    "List of models to load, as <name>:<path>. "
-    "Path should be the output of saving a transformer model, e.g. "
-    "model.save_pretrained(path) and tokenizer.save_pretrained(path). Remote "
-    ".tar.gz files will be downloaded and cached locally.",
-)
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples", None, "Maximum number of examples to load into LIT. Set "
-    "--max_examples=200 for a quick start.")
-
-DATASETS = {
-    "sst2_single_token_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/sst2_single_token-dev.100syn.tsv",
-    "sst2_token_in_context_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/sst2_token_in_context-dev.100syn.tsv",
-    "sst2_ordered_pair_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/sst2_simple_order-dev.100syn.tsv",
-    "toxicity_single_token_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/toxicity_single_token-dev.100syn.tsv",
-    "toxicity_token_in_context_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/toxicity_token_in_context-dev.100syn.tsv",
-    "toxicity_ordered_pair_dev_100_syn": "https://storage.googleapis.com/what-if-tool-resources/lit-data/toxicity_simple_order-dev.100syn.tsv",
-}
-
-modules = layout.LitModuleName
-IS_EVAL_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        "Main": [
-            modules.DocumentationModule,
-            modules.EmbeddingsModule,
-            modules.DataTableModule,
-            modules.DatapointEditorModule,
-        ]
-    },
-    lower={
-        "Predictions": [
-            modules.ClassificationModule,
-            modules.SalienceMapModule,
-            modules.ScalarModule,
-        ],
-        "Salience Clustering": [modules.SalienceClusteringModule],
-        "Metrics": [
-            modules.MetricsModule,
-            modules.ConfusionMatrixModule,
-            modules.CurvesModule,
-            modules.ThresholderModule,
-        ],
-        "Counterfactuals": [
-            modules.GeneratorModule,
-        ],
-    },
-    description="Custom layout for evaluating input salience methods.")
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {"is_eval": IS_EVAL_LAYOUT}
-# You can change this back via URL param, e.g. localhost:5432/?layout=default
-FLAGS.set_default("default_layout", "is_eval")
-
-
-def get_wsgi_app():
-  """Return WSGI app for container-hosted demos."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  FLAGS.set_default("warm_start", 1.0)
-  FLAGS.set_default("max_examples", 1000)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("is_eval_demo:get_wsgi_app() called with unused args: %s",
-                 unused)
-  return main([])
-
-
-def main(_):
-  models = {}
-  loaded_datasets = {}
-
-  for model_string in _MODELS.value:
-    # Only split on the first two ':', because path may be a URL
-    # containing 'https://'
-    name, path = model_string.split(":", 1)
-    logging.info("Loading model '%s' from '%s'", name, path)
-    # Normally path is a directory; if it's an archive file, download and
-    # extract to the transformers cache.
-    if path.endswith(".tar.gz"):
-      path = file_cache.cached_path(
-          path, extract_compressed_file=True)
-    # Load the model from disk.
-    models[name] = is_eval_models.ISEvalModel(
-        name, path, output_attention=False)
-
-  logging.info("Loading data for SST-2 task.")
-  for data_key, url in DATASETS.items():
-    path = file_cache.cached_path(url)
-    loaded_datasets[data_key] = datasets.SingleInputClassificationFromTSV(
-        path, data_key)
-
-  # Truncate datasets if --max_examples is set.
-  for name in loaded_datasets:
-    logging.info("Dataset: '%s' with %d examples", name,
-                 len(loaded_datasets[name]))
-    loaded_datasets[name] = loaded_datasets[name].shuffle().slice[:_MAX_EXAMPLES
-                                                                  .value]
-    logging.info("  truncated to %d examples", len(loaded_datasets[name]))
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(
-      models,
-      loaded_datasets,
-      layouts=CUSTOM_LAYOUTS,
-      onboard_end_doc=_DOC_STRING,
-      **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/is_eval/is_eval_trainer.py b/lit_nlp/examples/is_eval/is_eval_trainer.py
deleted file mode 100644
index a7a53da5..00000000
--- a/lit_nlp/examples/is_eval/is_eval_trainer.py
+++ /dev/null
@@ -1,138 +0,0 @@
-r"""Lightweight trainer script to fine-tune a model for IS eval.
-
-Usage:
-  python -m lit_nlp.examples.tools.is_eval_trainer \
-    --encoder_name=bert-base-uncased \
-    --train_path=/path/to/saved/model \
-    --train_data_path=/path/to/train/data \
-    --dev_data_path=/path/to/dev/data \
-
-This will finetune a BERT model to reproduce findings of the paper ""Will You
-Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input
-Salience Methods for Text Classification" [https://arxiv.org/abs/2111.07367].
-
-Please ensure that the model's vocabulary file includes all special shortcut
-tokens. When using the provided datasets of the LIT demo these are:
-"ZEROA", "ZEROB", "ONEA", "ONEB", "onea", "oneb", "zeroa", "zerob", "synt".
-
-This will train a BERT-base model [https://arxiv.org/abs/1810.04805]
-which give validation accuracy in the low 90s on SST-2.
-
-Note: you don't have to use this trainer to use LIT; the classifier
-implementation is just a wrapper around HuggingFace Transformers, using
-AutoTokenizer, AutoConfig, and TFAutoModelForSequenceClassification, and can
-load anything compatible with those classes.
-"""
-
-from collections.abc import Sequence
-import os
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp.examples.is_eval import datasets
-from lit_nlp.examples.models import glue_models
-from lit_nlp.lib import serialize
-import tensorflow as tf
-
-_ENCODER_NAME = flags.DEFINE_string(
-    "encoder_name", "bert-base-uncased",
-    "Model name or path to pretrained (base) encoder.")
-_TRAIN_DATA_PATH = flags.DEFINE_string("train_data_path", None, "")
-_DEV_DATA_PATH = flags.DEFINE_string("dev_data_path", None, "")
-_TRAIN_PATH = flags.DEFINE_string("train_path", "/tmp/hf_demo",
-                                  "Path to save fine-tuned model.")
-
-_NUM_EPOCHS = flags.DEFINE_integer(
-    "num_epochs", 3, "Number of epochs to train for.", lower_bound=1)
-_SAVE_INTERMEDIATES = flags.DEFINE_bool(
-    "save_intermediates", False,
-    "If true, save intermediate weights after each epoch.")
-
-
-def history_to_dict(keras_history):
-  return {
-      "epochs": keras_history.epoch,
-      "history": keras_history.history,
-      "params": keras_history.params,
-      "optimizer_params": keras_history.model.optimizer.get_config(),
-  }
-
-
-class EpochSaverCallback(tf.keras.callbacks.Callback):
-  """Save model at the beginning of training and after every epoch.
-
-  Similar to tf.keras.callbacks.ModelCheckpoint, but this allows us to specify
-  a custom save fn to call, such as the HuggingFace model.save() which writes
-  .h5 files and config information.
-  """
-
-  def __init__(self, save_path_base: str, save_fn=None):
-    super().__init__()
-    self.save_path_base = save_path_base
-    self.save_fn = save_fn or self.model.save
-
-  def on_train_begin(self, logs=None):
-    self.on_epoch_end(-1, logs=logs)  # write epoch-0
-
-  def on_epoch_end(self, epoch, logs=None):
-    # Save path 1-indexed = # of completed epochs.
-    save_path = os.path.join(self.save_path_base, f"epoch-{epoch+1}")
-    self.save_fn(save_path)
-
-
-def train_and_save(model,
-                   train_data,
-                   val_data,
-                   train_path,
-                   save_intermediates=False,
-                   **train_kw):
-  """Run training and save model."""
-  # Set up logging for TensorBoard. To view, run:
-  #   tensorboard --log_dir=<train_path>/tensorboard
-  keras_callbacks = [
-      tf.keras.callbacks.TensorBoard(
-          log_dir=os.path.join(train_path, "tensorboard"))
-  ]
-  if save_intermediates:
-    keras_callbacks.append(EpochSaverCallback(train_path, save_fn=model.save))
-  history = model.train(
-      train_data.examples,
-      validation_inputs=val_data.examples,
-      keras_callbacks=keras_callbacks,
-      **train_kw)
-
-  # Save training history too, since this is human-readable and more concise
-  # than the TensorBoard log files.
-  with open(os.path.join(train_path, "train.history.json"), "w") as fd:
-    # Use LIT's custom JSON encoder to handle dicts containing NumPy data.
-    fd.write(serialize.to_json(history_to_dict(history), simple=True, indent=2))
-
-  model.save(train_path)
-  logging.info("Saved model files: \n  %s",
-               "\n  ".join(os.listdir(train_path)))
-
-
-def main(argv: Sequence[str]) -> None:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  model = glue_models.SST2Model(_ENCODER_NAME.value)
-  train_data = datasets.SingleInputClassificationFromTSV(_TRAIN_DATA_PATH.value)
-  dev_data = datasets.SingleInputClassificationFromTSV(_DEV_DATA_PATH.value)
-
-  train_and_save(
-      model,
-      train_data,
-      dev_data,
-      _TRAIN_PATH.value,
-      save_intermediates=_SAVE_INTERMEDIATES.value,
-      num_epochs=_NUM_EPOCHS.value,
-      learning_rate=1e-5,
-      batch_size=16,
-  )
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/is_eval/models.py b/lit_nlp/examples/is_eval/models.py
deleted file mode 100644
index d6765df7..00000000
--- a/lit_nlp/examples/is_eval/models.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Custom GLUE Model and ModelSpec for the Input Salience Evaluation demo."""
-from typing import cast
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.examples.is_eval import datasets as is_eval_datasets
-from lit_nlp.examples.models import glue_models
-
-
-class ISEvalModel(glue_models.SST2Model):
-  """Custom GLUE model for the Input Salience Evaluation demo."""
-
-  def __init__(self, model_name: str, *args, **kw):
-    """Initializes a custom SST-2 model for the Input Salience Eval demo.
-
-    Args:
-      model_name: The model's name. Used to determine dataset compatibility.
-      *args: Additional positional args to pass to the SST2Model base class.
-      **kw: Additional keyword args to pass to the SST2Model base class.
-    """
-    super().__init__(*args, **kw)
-    self._model_name = model_name
-
-  def is_compatible_with_dataset(self, dataset: lit_dataset.Dataset) -> bool:
-    """Returns true if the model is compatible with the dataset.
-
-    The Input Salience Eval demo is somewhat unique in that each model and
-    dataset have compatible specs but the intention is to pair them for
-    specific tasks.
-
-    This class determines compatibility by:
-
-    1.  Ensuring that the value of `model_name` is contained in the `default`
-        value of the `dataset_name` field in the provided `dataset_spec`.
-    2.  Calling super().is_compatible_with_dataset() to check compatibility
-        using the base ModelSpec check.
-
-    Args:
-      dataset: The dataset for which compatibility will be determined.
-    """
-    if not isinstance(dataset,
-                      is_eval_datasets.SingleInputClassificationFromTSV):
-      return False
-
-    eval_dataset = cast(is_eval_datasets.SingleInputClassificationFromTSV,
-                        dataset)
-    if self.model_name in eval_dataset.name:
-      return super().is_compatible_with_dataset(dataset)
-    else:
-      return False

From 71d88fb86eb88ffb80d665cf7571b21d7ae06bd2 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Thu, 6 Jun 2024 11:23:01 -0700
Subject: [PATCH 15/47] Remove the coref demo from the LIT examples.

PiperOrigin-RevId: 640962672
---
 docs/assets/images/lit-coref-compare.png      | Bin 80342 -> 0 bytes
 docs/assets/images/lit-coref-data.png         | Bin 65860 -> 0 bytes
 docs/assets/images/lit-coref-metric-top.png   | Bin 39022 -> 0 bytes
 docs/assets/images/lit-coref-metrics.png      | Bin 124661 -> 0 bytes
 docs/assets/images/lit-coref-pred.png         | Bin 44707 -> 0 bytes
 docs/assets/images/lit-coref-select.png       | Bin 103595 -> 0 bytes
 lit_nlp/examples/coref/__init__.py            |  13 -
 lit_nlp/examples/coref/coref_demo.py          | 226 ---------------
 lit_nlp/examples/coref/datasets/__init__.py   |  13 -
 lit_nlp/examples/coref/datasets/ontonotes.py  |  41 ---
 lit_nlp/examples/coref/datasets/winogender.py | 200 -------------
 lit_nlp/examples/coref/edge_predictor.py      | 273 ------------------
 lit_nlp/examples/coref/encoders.py            |  96 ------
 lit_nlp/examples/coref/model.py               | 138 ---------
 lit_nlp/examples/coref/retokenize.py          |  40 ---
 website/sphinx_src/api.md                     |   2 +-
 website/sphinx_src/demos.md                   |  18 --
 website/sphinx_src/docker.md                  |   1 -
 website/sphinx_src/faq.md                     |   5 +-
 .../src/assets/images/lit-coref-compare.png   | Bin 80342 -> 0 bytes
 website/src/assets/images/lit-coref-data.png  | Bin 65860 -> 0 bytes
 .../assets/images/lit-coref-metric-top.png    | Bin 39022 -> 0 bytes
 .../src/assets/images/lit-coref-metrics.png   | Bin 124661 -> 0 bytes
 website/src/assets/images/lit-coref-pred.png  | Bin 44707 -> 0 bytes
 .../src/assets/images/lit-coref-select.png    | Bin 103595 -> 0 bytes
 website/src/tutorials.md                      |   3 -
 website/src/tutorials/coref.md                |  64 ----
 website/src/tutorials/sequence-salience.md    |   4 +-
 28 files changed, 3 insertions(+), 1134 deletions(-)
 delete mode 100644 docs/assets/images/lit-coref-compare.png
 delete mode 100644 docs/assets/images/lit-coref-data.png
 delete mode 100644 docs/assets/images/lit-coref-metric-top.png
 delete mode 100644 docs/assets/images/lit-coref-metrics.png
 delete mode 100644 docs/assets/images/lit-coref-pred.png
 delete mode 100644 docs/assets/images/lit-coref-select.png
 delete mode 100644 lit_nlp/examples/coref/__init__.py
 delete mode 100644 lit_nlp/examples/coref/coref_demo.py
 delete mode 100644 lit_nlp/examples/coref/datasets/__init__.py
 delete mode 100644 lit_nlp/examples/coref/datasets/ontonotes.py
 delete mode 100644 lit_nlp/examples/coref/datasets/winogender.py
 delete mode 100644 lit_nlp/examples/coref/edge_predictor.py
 delete mode 100644 lit_nlp/examples/coref/encoders.py
 delete mode 100644 lit_nlp/examples/coref/model.py
 delete mode 100644 lit_nlp/examples/coref/retokenize.py
 delete mode 100644 website/src/assets/images/lit-coref-compare.png
 delete mode 100644 website/src/assets/images/lit-coref-data.png
 delete mode 100644 website/src/assets/images/lit-coref-metric-top.png
 delete mode 100644 website/src/assets/images/lit-coref-metrics.png
 delete mode 100644 website/src/assets/images/lit-coref-pred.png
 delete mode 100644 website/src/assets/images/lit-coref-select.png
 delete mode 100644 website/src/tutorials/coref.md

diff --git a/docs/assets/images/lit-coref-compare.png b/docs/assets/images/lit-coref-compare.png
deleted file mode 100644
index eaa0c7e743b1e4fcc13b17d5cf56d0da7171b4f3..0000000000000000000000000000000000000000
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diff --git a/lit_nlp/examples/coref/__init__.py b/lit_nlp/examples/coref/__init__.py
deleted file mode 100644
index c6334245..00000000
--- a/lit_nlp/examples/coref/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/lit_nlp/examples/coref/coref_demo.py b/lit_nlp/examples/coref/coref_demo.py
deleted file mode 100644
index d960909c..00000000
--- a/lit_nlp/examples/coref/coref_demo.py
+++ /dev/null
@@ -1,226 +0,0 @@
-r"""Coreference demo, trainer and LIT server.
-
-To run LIT locally with a pre-trained model:
-  blaze run -c opt --config=cuda examples/coref:coref_demo -- --port=5432
-
-This demo shows a simple gold-mention coreference model, highlighting how LIT
-can support intersectional analysis for fairness evaluation. It also
-demonstrates a multi-headed model, with both a structured prediction head
-and a two-class classifier to predict the binary answer on Winogender examples.
-
-Our model is a probing-style classifier over a frozen BERT encoder, similar to
-Tenney et al. 2019 (https://arxiv.org/abs/1905.06316). This model takes gold
-mention pairs as input and predicts binary labels - 1 if coreferent, else 0.
-This is not the best coference model one could build, but it's a good one if
-we're interested in probing the kinds of biases encoded in a language model
-such as BERT. Plus, because we can pre-compute the BERT activations, we can
-train our model very quickly.
-
-For evaluation, we use the Winogender dataset of Rudinger et al. 2018
-(https://arxiv.org/abs/1804.09301), which consists of 720 template-generated
-sentences, each with a pronoun and two candidate mentions: an occupation term,
-and an neutral participant such as 'customer'. Each instance is annotated with
-pf_bls, the fraction of that occupation identifying as female, per the U.S.
-Bureau of Labor Statistics.
-
-For more details on the analysis, see the case study in Section 3 of
-the LIT paper (https://arxiv.org/abs/2008.05122).
-
-To train the model, you'll need the OntoNotes 5.0 dataset in the edge probing
-JSON format. See
-https://github.com/nyu-mll/jiant-v1-legacy/tree/master/probing/data#ontonotes
-for instructions. Then run:
-  blaze run -c opt --config=cuda examples/coref:coref_demo -- \
-    --encoder_name=bert-base-uncased --do_train \
-    --ontonotes_edgeprobe_path=/path/to/ontonotes/coref/ \
-    --model_path=/path/to/save/model \
-    --do_serve --port=5432
-
-With bert-base-uncased on a single Titan Xp GPU, it takes about 10-12 minutes
-to train this model, including the time to extract representations, and should
-get around 85% F1 on the OntoNotes development set. Exact numbers on Winogender
-will vary, but the qualitative behavior on slices by gender and answer should
-match Figure 3 of the paper.
-"""
-
-from collections.abc import Sequence
-import os
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import dtypes as lit_dtypes
-from lit_nlp.api import layout
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.coref import edge_predictor
-from lit_nlp.examples.coref import encoders
-from lit_nlp.examples.coref import model
-from lit_nlp.examples.coref.datasets import ontonotes
-from lit_nlp.examples.coref.datasets import winogender
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_DO_TRAIN = flags.DEFINE_bool(
-    "do_train",
-    False,
-    "If true, train a new model and save to FLAGS.model_path."
-)
-_DO_SERVE = flags.DEFINE_bool(
-    "do_serve",
-    True,
-    "If true, start a LIT server with the model at FLAGS.model_path."
-)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path",
-    "https://storage.googleapis.com/what-if-tool-resources/lit-models/coref_base.tar.gz",
-    "Path to save or load trained model."
-)
-
-##
-# Training-only flags; these are ignored if only serving a pre-trained model.
-_ENCODER_NAME = flags.DEFINE_string(
-    "encoder_name",
-    "bert-base-uncased",
-    "Name of BERT variant to use for fine-tuning. See "
-    "https://huggingface.co/models."
-)
-
-_ONTONOTES_EDGEPROBE_PATH = flags.DEFINE_string(
-    "ontonotes_edgeprobe_path", None,
-    "Path to OntoNotes coreference data in edge probing JSON format. "
-    "This is needed for training, and optional for running LIT.")
-
-# Custom frontend layout; see client/lib/types.ts
-modules = layout.LitModuleName
-WINOGENDER_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        "Main": [modules.DataTableModule, modules.DatapointEditorModule],
-    },
-    lower={
-        "Predictions": [
-            modules.SpanGraphGoldModule,
-            modules.SpanGraphModule,
-            modules.ClassificationModule,
-        ],
-        "Performance": [
-            modules.MetricsModule,
-            modules.ScalarModule,
-            modules.ConfusionMatrixModule,
-        ],
-    },
-    description="Custom layout for the Winogender coreference demo.",
-)
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {"winogender": WINOGENDER_LAYOUT}
-
-FLAGS.set_default("default_layout", "winogender")
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Return WSGI app for container-hosted demos."""
-  # Set defaults for container-hosted demo.
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("do_train", False)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("coref_demo:get_wsgi_app() called with unused args: %s",
-                 unused)
-  return main([])
-
-
-def symmetrize_edges(dataset: lit_dataset.Dataset) -> lit_dataset.Dataset:
-  """Symmetrize edges by adding copies with span1 and span2 interchanged."""
-
-  def _swap(edge):
-    return lit_dtypes.EdgeLabel(edge.span2, edge.span1, edge.label)
-
-  edge_fields = utils.find_spec_keys(dataset.spec(), lit_types.EdgeLabels)
-  examples = []
-  for ex in dataset.examples:
-    new_ex = dict(ex)
-    for field in edge_fields:
-      new_ex[field] = ex[field] + [_swap(edge) for edge in ex[field]]
-    examples.append(new_ex)
-  return lit_dataset.Dataset(dataset.spec(), examples)
-
-
-def train(save_path: str):
-  """Train a coreference model using encoder features over OntoNotes."""
-  # Load OntoNotes data for training.
-  ontonotes_train = ontonotes.OntonotesCorefDataset(
-      os.path.join(FLAGS.ontonotes_edgeprobe_path, "train.json"))
-  ontonotes_dev = ontonotes.OntonotesCorefDataset(
-      os.path.join(FLAGS.ontonotes_edgeprobe_path, "development.json"))
-
-  # Assemble our model.
-  encoder = encoders.BertEncoderWithOffsets(FLAGS.encoder_name)
-  input_dim = encoder.model.config.hidden_size
-  classifier = edge_predictor.SingleEdgePredictor(
-      input_dim=input_dim, hidden_dim=min(input_dim, 256))
-  full_model = model.FrozenEncoderCoref(encoder, classifier)
-
-  # Train our model.
-  train_dataset = symmetrize_edges(ontonotes_train)
-  full_model.train(
-      train_dataset.examples,
-      ontonotes_dev.examples,
-      batch_size=128,
-      num_epochs=15)
-  # Save classifier and encoder
-  full_model.save(save_path)
-
-
-def run_server(load_path: str):
-  """Run a LIT server with the trained coreference model."""
-  # Normally path is a directory; if it's an archive file, download and
-  # extract to the transformers cache.
-  if load_path.endswith(".tar.gz"):
-    load_path = file_cache.cached_path(
-        load_path, extract_compressed_file=True)
-  # Load model from disk.
-  full_model = model.FrozenEncoderCoref.from_saved(
-      load_path,
-      encoder_cls=encoders.BertEncoderWithOffsets,
-      classifier_cls=edge_predictor.SingleEdgePredictor)
-
-  # Set up the LIT server.
-  models = {"model": full_model}
-  datasets = {"winogender": winogender.WinogenderDataset()}
-  if _ONTONOTES_EDGEPROBE_PATH.value:
-    datasets["ontonotes_dev"] = ontonotes.OntonotesCorefDataset(
-        os.path.join(FLAGS.ontonotes_edgeprobe_path, "development.json"))
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(
-      models, datasets, layouts=CUSTOM_LAYOUTS, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  assert _MODEL_PATH.value, "Must specify --model_path to run."
-
-  if _DO_TRAIN.value:
-    train(_MODEL_PATH.value)
-
-  if _DO_SERVE.value:
-    return run_server(_MODEL_PATH.value)
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/coref/datasets/__init__.py b/lit_nlp/examples/coref/datasets/__init__.py
deleted file mode 100644
index c6334245..00000000
--- a/lit_nlp/examples/coref/datasets/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/lit_nlp/examples/coref/datasets/ontonotes.py b/lit_nlp/examples/coref/datasets/ontonotes.py
deleted file mode 100644
index 7ec48cd8..00000000
--- a/lit_nlp/examples/coref/datasets/ontonotes.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Loader for OntoNotes coreference data."""
-import json
-
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import dtypes as lit_dtypes
-from lit_nlp.api import types as lit_types
-
-EdgeLabel = lit_dtypes.EdgeLabel
-
-
-class OntonotesCorefDataset(lit_dataset.Dataset):
-  """OntoNotes coreference data, from edge probing format.
-
-  To get the edge probing data, see instructions at
-  https://github.com/nyu-mll/jiant-v1-legacy/tree/master/probing/data#ontonotes
-  """
-
-  def convert_ep_record(self, record):
-    """Convert edge probing record to LIT inputs."""
-    edges = [
-        EdgeLabel(span1=t['span1'], span2=t['span2'], label=int(t['label']))
-        for t in record['targets']
-    ]
-    return {
-        'text': record['text'],
-        'tokens': record['text'].split(),
-        'coref': edges,
-    }
-
-  def __init__(self, edgeprobe_json_path: str):
-    with open(edgeprobe_json_path) as fd:
-      ep_records = [json.loads(line) for line in fd]
-
-    self._examples = [self.convert_ep_record(r) for r in ep_records]
-
-  def spec(self):
-    return {
-        'text': lit_types.TextSegment(),
-        'tokens': lit_types.Tokens(parent='text'),
-        'coref': lit_types.EdgeLabels(align='tokens'),
-    }
diff --git a/lit_nlp/examples/coref/datasets/winogender.py b/lit_nlp/examples/coref/datasets/winogender.py
deleted file mode 100644
index 9080144b..00000000
--- a/lit_nlp/examples/coref/datasets/winogender.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"""Coreference version of the Winogender dataset.
-
-Each instance has two edges, one between the pronoun and the occupation and one
-between the pronoun and the participant. The pronoun is always span1.
-
-There are 120 templates in the Winogender set, 60 coreferent with the
-occupation, and 60 coreferent with the participant. Each is instantiated
-six times: with and without "someone" substituting for the participant,
-and with {male, female, neutral} pronouns, for a total of 720 examples.
-
-Winogender repo: https://github.com/rudinger/winogender-schemas
-Paper: Gender Bias in Coreference Resolution (Rudinger et al. 2018),
-https://arxiv.org/pdf/1804.09301.pdf
-"""
-import enum
-import os
-from typing import Optional
-
-from absl import logging
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import dtypes as lit_dtypes
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import file_cache
-import pandas as pd
-
-EdgeLabel = lit_dtypes.EdgeLabel
-
-DATA_ROOT = "https://raw.githubusercontent.com/rudinger/winogender-schemas/master/data/"  # pylint: disable=line-too-long
-
-
-def cache_data(url):
-  """Download data or return local cache path."""
-  logging.info("Winogender: retrieving data file %s", url)
-  return file_cache.cached_path(url)
-
-
-## From gap-coreference/constants.py
-class Gender(enum.Enum):
-  UNKNOWN = 0
-  MASCULINE = 1
-  FEMININE = 2
-
-
-NOM = "$NOM_PRONOUN"
-POSS = "$POSS_PRONOUN"
-ACC = "$ACC_PRONOUN"
-
-PRONOUN_MAP = {
-    Gender.FEMININE: {
-        NOM: "she",
-        POSS: "her",
-        ACC: "her"
-    },
-    Gender.MASCULINE: {
-        NOM: "he",
-        POSS: "his",
-        ACC: "him"
-    },
-    Gender.UNKNOWN: {
-        NOM: "they",
-        POSS: "their",
-        ACC: "them"
-    },
-}
-
-ANSWER_VOCAB = ["occupation", "participant"]
-
-PRONOUNS_BY_GENDER = {k: "/".join(PRONOUN_MAP[k].values()) for k in PRONOUN_MAP}
-
-
-# Based on winogender-schemas/scripts/instantiate.py, but adapted to LIT format.
-def generate_instance(occupation,
-                      participant,
-                      answer,
-                      sentence,
-                      gender=Gender.UNKNOWN,
-                      someone=False):
-  """Generate a Winogender example from a template row."""
-  toks = sentence.split(" ")
-  part_index = toks.index("$PARTICIPANT")
-  if not someone:
-    # we are using the instantiated participant,
-    # e.g. "client", "patient", "customer",...
-    toks[part_index] = participant
-  else:  # we are using the bleached NP "someone" for the other participant
-    # first, remove the token that precedes $PARTICIPANT, i.e. "the"
-    toks = toks[:part_index - 1] + toks[part_index:]
-    # recompute participant index (it should be part_index - 1)
-    part_index = toks.index("$PARTICIPANT")
-    toks[part_index] = "Someone" if part_index == 0 else "someone"
-
-  # Make sure we do this /after/ substituting "someone",
-  # since that may change indices.
-  occ_index = toks.index("$OCCUPATION")
-  # This should always pass on the regular Winogender dataset.
-  assert " " not in occupation, "Occupation must be single-token."
-  toks[occ_index] = occupation
-
-  pronoun_idx = None
-  gendered_toks = []
-  for i, t in enumerate(toks):
-    sub = PRONOUN_MAP[gender].get(t, t)
-    if sub != t:
-      pronoun_idx = i
-    gendered_toks.append(sub)
-
-  # NOM, POSS, ACC
-  pronoun_type = toks[pronoun_idx][1:].replace("_PRONOUN", "")
-
-  # Process text for fluency
-  text = " ".join(gendered_toks)
-  text = text.replace("they was", "they were")
-  text = text.replace("They was", "They were")
-
-  record = {"text": text, "tokens": text.split()}
-  t0 = EdgeLabel(
-      span1=(occ_index, occ_index + 1),
-      span2=(pronoun_idx, pronoun_idx + 1),
-      label=int(1 if answer == 0 else 0))
-  t1 = EdgeLabel(
-      span1=(part_index, part_index + 1),
-      span2=(pronoun_idx, pronoun_idx + 1),
-      label=int(1 if answer == 1 else 0))
-  record["coref"] = [t0, t1]
-  record.update({
-      "occupation": occupation,
-      "participant": participant,
-      "answer": ANSWER_VOCAB[answer],
-      "someone": str(someone),
-      "pronouns": PRONOUNS_BY_GENDER[gender],
-      "pronoun_type": pronoun_type,
-      "gender": gender.name,
-  })
-  return record
-
-
-class WinogenderDataset(lit_dataset.Dataset):
-  """Coreference on Winogender schemas (Rudinger et al. 2018)."""
-
-  # These should match the args to generate_instance()
-  TSV_COLUMN_NAMES = ["occupation", "participant", "answer", "sentence"]
-
-  def __init__(self,
-               templates_path: Optional[str] = None,
-               occupation_stats_path: Optional[str] = None):
-    templates_path_to_cache = templates_path or os.path.join(
-        DATA_ROOT, "templates.tsv"
-    )
-    cached_templates_path = cache_data(templates_path_to_cache)
-
-    occupation_stats_path_to_cache = occupation_stats_path or os.path.join(
-        DATA_ROOT, "occupations-stats.tsv"
-    )
-    cached_occupation_stats_path = cache_data(occupation_stats_path_to_cache)
-
-    # Load templates and make a DataFrame.
-    with open(cached_templates_path) as fd:
-      self.templates_df = pd.read_csv(
-          fd, sep="\t", header=0, names=self.TSV_COLUMN_NAMES)
-
-    # Load occpuation stats.
-    with open(cached_occupation_stats_path) as fd:
-      self.occupation_df = pd.read_csv(fd, sep="\t").set_index("occupation")
-
-    # Make examples for each {someone} x {gender} x {template}
-    self._examples = []
-    for _, row in self.templates_df.iterrows():
-      for someone in {False, True}:
-        for gender in Gender:
-          r = generate_instance(someone=someone, gender=gender, **row)
-          r["pf_bls"] = (
-              self.occupation_df.bls_pct_female[r["occupation"]] / 100.0)
-          self._examples.append(r)
-
-  def spec(self):
-    return {
-        "text":
-            lit_types.TextSegment(),
-        "tokens":
-            lit_types.Tokens(parent="text"),
-        "coref":
-            lit_types.EdgeLabels(align="tokens"),
-        # Metadata fields for filtering and analysis.
-        "occupation":
-            lit_types.CategoryLabel(),
-        "participant":
-            lit_types.CategoryLabel(),
-        "answer":
-            lit_types.CategoryLabel(vocab=ANSWER_VOCAB),
-        "someone":
-            lit_types.CategoryLabel(vocab=["True", "False"]),
-        "pronouns":
-            lit_types.CategoryLabel(vocab=list(PRONOUNS_BY_GENDER.values())),
-        "pronoun_type":
-            lit_types.CategoryLabel(vocab=["NOM", "POSS", "ACC"]),
-        "gender":
-            lit_types.CategoryLabel(vocab=[g.name for g in Gender]),
-        "pf_bls":
-            lit_types.Scalar(),
-    }
diff --git a/lit_nlp/examples/coref/edge_predictor.py b/lit_nlp/examples/coref/edge_predictor.py
deleted file mode 100644
index 0bc4e823..00000000
--- a/lit_nlp/examples/coref/edge_predictor.py
+++ /dev/null
@@ -1,273 +0,0 @@
-"""LIT model implementation for single-edge classifier."""
-import os
-from collections.abc import Iterable
-from typing import Optional
-
-from absl import logging
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import dtypes as lit_dtypes
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import utils
-import numpy as np
-import tensorflow as tf
-
-EdgeLabel = lit_dtypes.EdgeLabel
-JsonDict = lit_types.JsonDict
-Spec = lit_types.Spec
-
-
-def _extract_span(
-    span: tuple[int, int], embs: np.ndarray, offsets: np.ndarray
-) -> np.ndarray:
-  start = offsets[span[0]]
-  end = offsets[span[1]]
-  # <float>[span_length, emb_dim]
-  range_embs = embs[start:end]
-  # <float>[emb_dim]
-  pooled_embs = np.mean(range_embs, axis=0)
-  return pooled_embs
-
-
-def _make_probe_inputs(
-    edges: list[EdgeLabel], embs: np.ndarray, offsets: np.ndarray, src_idx: int
-):
-  for j, edge in enumerate(edges):
-    span1_embs = _extract_span(edge.span1, embs, offsets)
-    span2_embs = _extract_span(edge.span2, embs, offsets)
-    yield {
-        'span1_embs': span1_embs,
-        'span2_embs': span2_embs,
-        'label': edge.label,
-        'src_idx': src_idx,
-        'edge_idx': j
-    }
-
-
-def _estimate_memory_needs(
-    inputs: list[JsonDict], edge_field: str, output_example: JsonDict
-):
-  """Estimate how much memory is needed to store all activations.
-
-  We store all activations in memory to simplify the implementation, but this
-  can grow to be quite large (many GB up to 100s of GB) for large encoders
-  and large numbers of targets (such as OntoNotes tasks).
-
-  This will log the estimated memory required, based on the dimension of
-  activations and the number of total edges.
-
-  Args:
-    inputs: all inputs
-    edge_field: name of edges field in input (such as 'coref')
-    output_example: a single example from an EdgeFeaturesDataset, used to get
-      embedding size
-  """
-  # Count total edges
-  total_edges = sum([len(ex[edge_field]) for ex in inputs])
-  # Get embedding size from first output example
-  span1_dim = output_example['span1_embs'].size
-  span2_dim = output_example['span2_embs'].size
-  # pylint: disable=logging-format-interpolation
-  logging.warning(
-      'Found %d total edges with embedding dimensions %d + %d = %d.',
-      total_edges,
-      span1_dim,
-      span2_dim,
-      (span1_dim + span2_dim),
-  )
-  bytes_per_edge = (
-      output_example['span1_embs'].nbytes + output_example['span2_embs'].nbytes)
-  total_mbytes = total_edges * bytes_per_edge / 1e6
-  if total_mbytes < 1e3:
-    logging.warning('Estimated memory requirement: %.3G MB', total_mbytes)
-  else:
-    logging.warning(
-        'Estimated memory requirement: %.3G GB', (total_mbytes / 1e3)
-    )
-  # pylint: enable=logging-format-interpolation
-  # TODO(lit-dev): add factor to account for pointers/overhead and text fields.
-  # TODO(lit-dev): check system memory and confirm before proceeding
-
-
-class EdgeFeaturesDataset(lit_dataset.Dataset):
-  """Input examples to a SingleEdgePredictor."""
-
-  def __init__(self, examples):
-    self._examples = examples
-
-  @classmethod
-  def build(
-      cls,
-      inputs: list[JsonDict],
-      encoder: lit_model.BatchedModel,
-      edge_field: str,
-      embs_field: str,
-      offset_field: str,
-      progress=lambda x: x,
-      verbose=False,
-  ):
-    """Run encoder and extract span representations for coreference.
-
-    'encoder' should be a model returning one TokenEmbeddings field,
-    from which span features will be extracted, as well as a TokenOffsets field
-    which maps input tokens to output tokens.
-
-    The returned dataset will contain one example for each label in the inputs'
-    EdgeLabels field.
-
-    Args:
-      inputs: input Dataset
-      encoder: encoder model, compatible with inputs
-      edge_field: name of edge field in data
-      embs_field: name of embeddings field in model output
-      offset_field: name of offset field in model output
-      progress: optional pass-through progress indicator
-      verbose: if true, print estimated memory usage
-
-    Returns:
-      EdgeFeaturesDataset with extracted span representations
-    """
-    examples = []
-    encoder_outputs = progress(encoder.predict(inputs))
-    for i, output in enumerate(encoder_outputs):
-      exs = _make_probe_inputs(
-          inputs[i][edge_field],
-          output[embs_field],
-          output[offset_field],
-          src_idx=i)
-      examples.extend(exs)
-      if verbose and i == 10:
-        _estimate_memory_needs(inputs, edge_field, examples[0])
-
-    return cls(examples)
-
-  def spec(self):
-    return {
-        'span1_embs': lit_types.Embeddings(),
-        'span2_embs': lit_types.Embeddings(),
-        'label': lit_types.Scalar(),
-        'src_idx': lit_types.Scalar(),
-        'edge_idx': lit_types.Scalar(),
-    }
-
-
-class SingleEdgePredictor(lit_model.BatchedModel):
-  """Coref model for a single edge. Compatible with EdgeFeaturesDataset."""
-
-  def build_model(self, input_dim: int, hidden_dim: int = 256):
-    """Construct a Keras model using the Functional API."""
-    span1_input = tf.keras.Input(shape=[input_dim])
-    span2_input = tf.keras.Input(shape=[input_dim])
-    concat_input = tf.keras.layers.Concatenate(axis=-1)(
-        [span1_input, span2_input])
-    h_repr = tf.keras.layers.Dense(hidden_dim, activation='relu')(concat_input)
-    probas = tf.keras.layers.Dense(1, activation='sigmoid')(h_repr)
-    return tf.keras.Model(
-        inputs={
-            'span1_embs': span1_input,
-            'span2_embs': span2_input
-        },
-        outputs=[probas],
-        name='coref_model')
-
-  def __init__(self, model_path: Optional[str] = None, **model_kw):
-    if model_path:
-      # Load from SavedModel
-      self.model = tf.keras.models.load_model(model_path)
-    else:
-      # Construct from params
-      self.model = self.build_model(**model_kw)
-
-  def _make_feature_columns(self, inputs: Iterable[JsonDict]):
-    """Extract features from input records and return as dict of columns."""
-    return {
-        'span1_embs':
-            tf.constant([ex['span1_embs'] for ex in inputs], dtype=tf.float32),
-        'span2_embs':
-            tf.constant([ex['span2_embs'] for ex in inputs], dtype=tf.float32),
-    }
-
-  def _make_dataset(self, inputs: Iterable[JsonDict]) -> tf.data.Dataset:
-    """Make a tf.data.Dataset from inputs in LIT format."""
-    # Convert to feature columns for tf.data APIs
-    features = self._make_feature_columns(inputs)
-    labels = tf.constant([ex['label'] for ex in inputs], dtype=tf.float32)
-    return tf.data.Dataset.from_tensor_slices((features, labels))
-
-  def train(
-      self,
-      train_inputs: list[JsonDict],
-      validation_inputs: list[JsonDict],
-      learning_rate=2e-5,
-      batch_size=32,
-      num_epochs=50,
-      keras_callbacks=None,
-  ):
-    """Train an edge classifier."""
-    train_dataset = self._make_dataset(train_inputs).shuffle(
-        len(train_inputs)).batch(batch_size).repeat(-1)
-    # Use larger batch for validation since inference is about 1/2 memory usage
-    # of backprop.
-    eval_batch_size = 2 * batch_size
-    validation_dataset = self._make_dataset(validation_inputs).batch(
-        eval_batch_size)
-
-    # Prepare model for training.
-    opt = tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08)
-    # TODO(lit-dev): get Keras to train on logits but eval on probas.
-    loss = tf.keras.losses.BinaryCrossentropy()
-    metrics = [
-        tf.keras.metrics.BinaryAccuracy(name='accuracy'),
-        tf.keras.metrics.Precision(name='precision'),
-        tf.keras.metrics.Recall(name='recall'),
-    ]
-    self.model.compile(optimizer=opt, loss=loss, metrics=[metrics])
-
-    steps_per_epoch = len(train_inputs) // batch_size
-    validation_steps = len(validation_inputs) // eval_batch_size
-    history = self.model.fit(
-        train_dataset,
-        epochs=num_epochs,
-        steps_per_epoch=steps_per_epoch,
-        validation_data=validation_dataset,
-        validation_steps=validation_steps,
-        callbacks=keras_callbacks,
-        verbose=2)
-    return history
-
-  def save(self, path: str):
-    """Save model weights.
-
-    To re-load, pass the path to the constructor instead of the name of a
-    base model.
-
-    Args:
-      path: directory to save to. Will write several files here.
-    """
-    if not os.path.isdir(path):
-      os.mkdir(path)
-    self.model.save(path)
-
-  ##
-  # LIT API methods
-  def max_minibatch_size(self):
-    return 128
-
-  def predict_minibatch(self, inputs):
-    features = self._make_feature_columns(inputs)
-    probas = self.model(features)  # <tf.float32>[batch_size, 1]
-    preds = {'proba': tf.squeeze(probas, axis=-1).numpy()}
-    return list(utils.unbatch_preds(preds))
-
-  def input_spec(self):
-    return {
-        'span1_embs': lit_types.Embeddings(),
-        'span2_embs': lit_types.Embeddings(),
-        'label': lit_types.Scalar(required=False),  # in range [0,1]
-    }
-
-  def output_spec(self):
-    return {
-        'proba':
-            lit_types.RegressionScore(parent='label')  # in range [0,1]
-    }
diff --git a/lit_nlp/examples/coref/encoders.py b/lit_nlp/examples/coref/encoders.py
deleted file mode 100644
index f674311b..00000000
--- a/lit_nlp/examples/coref/encoders.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""Encoder implementation for frozen-encoder coref."""
-import os
-
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.coref import retokenize
-from lit_nlp.examples.models import model_utils
-from lit_nlp.lib import utils
-import numpy as np
-import tensorflow as tf
-import transformers
-
-
-class BertEncoderWithOffsets(lit_model.BatchedModel):
-  """BERT encoder for pre-tokenized text."""
-
-  @property
-  def max_seq_length(self):
-    return self.model.config.max_position_embeddings
-
-  def __init__(self, model_name_or_path: str):
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path)
-    self.model = model_utils.load_pretrained(
-        transformers.TFBertForMaskedLM,
-        model_name_or_path,
-        output_hidden_states=True,
-        output_attentions=False)
-
-  def _postprocess(self, output: dict[str, np.ndarray]):
-    """Postprocess, modifying output dict in-place."""
-    # Slice to remove padding, omitting initial [CLS] and final [SEP]
-    slicer = slice(1, output.pop('ntok') - 1)
-    output['wpm_tokens'] = self.tokenizer.convert_ids_to_tokens(
-        output.pop('input_ids')[slicer])
-    # <float>[num_tokens, emb_dim]
-    output['top_layer_embs'] = output['top_layer_embs'][slicer]
-    return output
-
-  def save(self, path: str):
-    if not os.path.isdir(path):
-      os.mkdir(path)
-    self.tokenizer.save_pretrained(path)  # configs and vocab files
-    self.model.save_pretrained(path)  # as HDF5 weights file + config
-
-  ##
-  # LIT API implementations.
-  def max_minibatch_size(self):
-    # Rough heuristic for how much we can handle on one GPU with 12G of memory.
-    return 64 if self.model.config.num_hidden_layers <= 12 else 32
-
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    tokens_and_offsets = [
-        retokenize.subtokenize(ex['tokens'], self.tokenizer.tokenize)
-        for ex in inputs
-    ]
-    tokenized_texts, offsets = zip(*tokens_and_offsets)
-    # Process to ids, add special tokens, and compute segment ids and masks.
-    encoded_input = self.tokenizer.batch_encode_plus(
-        list(tokenized_texts),
-        is_split_into_words=True,
-        return_tensors='tf',
-        add_special_tokens=True,
-        max_length=self.max_seq_length,
-        padding='longest',
-        truncation='longest_first')
-
-    out: transformers.modeling_tf_outputs.TFMaskedLMOutput = \
-        self.model(encoded_input)
-    batched_outputs = {
-        'input_ids': encoded_input['input_ids'].numpy(),
-        'ntok': tf.reduce_sum(encoded_input['attention_mask'], axis=1).numpy(),
-        'top_layer_embs':
-            out.hidden_states[-1].numpy(),  # last layer, all tokens
-    }
-    # List of dicts, one per example.
-    unbatched_outputs = list(utils.unbatch_preds(batched_outputs))
-    # Postprocess to remove padding and add offsets.
-    ret = [self._postprocess(ubo) for ubo in unbatched_outputs]
-    for preds, offset_indices in zip(ret, offsets):
-      preds['offsets'] = offset_indices
-    return ret
-
-  def input_spec(self):
-    return {'tokens': lit_types.Tokens()}
-
-  def output_spec(self):
-    return {
-        'top_layer_embs':
-            lit_types.TokenEmbeddings(),
-        'wpm_tokens':
-            lit_types.Tokens(),
-        'offsets':
-            lit_types.SubwordOffsets(align_in='tokens', align_out='wpm_tokens')
-    }
diff --git a/lit_nlp/examples/coref/model.py b/lit_nlp/examples/coref/model.py
deleted file mode 100644
index 6ea1dde1..00000000
--- a/lit_nlp/examples/coref/model.py
+++ /dev/null
@@ -1,138 +0,0 @@
-"""LIT model implementation for frozen-encoder coreference."""
-import os
-
-from absl import logging
-from lit_nlp.api import dtypes as lit_dtypes
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.coref import edge_predictor
-from lit_nlp.examples.coref import encoders
-from lit_nlp.examples.coref.datasets import winogender
-from lit_nlp.lib import utils
-import numpy as np
-import tqdm
-
-EdgeLabel = lit_dtypes.EdgeLabel
-JsonDict = lit_types.JsonDict
-
-
-class FrozenEncoderCoref(lit_model.BatchedModel):
-  """Frozen-encoder coreference model."""
-
-  @classmethod
-  def from_saved(cls, path: str, encoder_cls, classifier_cls):
-    """Reload from the output of .save()."""
-    encoder_path = os.path.join(path, 'encoder')
-    encoder = encoder_cls(encoder_path)
-    classifier_path = os.path.join(path, 'classifier')
-    classifier = classifier_cls(classifier_path)
-    return cls(encoder, classifier)
-
-  def __init__(self, encoder: encoders.BertEncoderWithOffsets,
-               classifier: edge_predictor.SingleEdgePredictor):
-    self.encoder = encoder
-    self.classifier = classifier
-
-    embs_field = utils.find_spec_keys(self.encoder.output_spec(),
-                                      lit_types.TokenEmbeddings)[0]
-    offset_field = utils.find_spec_keys(self.encoder.output_spec(),
-                                        lit_types.SubwordOffsets)[0]
-    self.extractor_kw = dict(
-        edge_field='coref', embs_field=embs_field, offset_field=offset_field)
-
-  def _make_edges(self, inputs: list[JsonDict], show_progress=False):
-    if show_progress:
-      progress = lambda x: tqdm.tqdm(x, total=len(inputs))
-    else:
-      progress = lambda x: x
-    return edge_predictor.EdgeFeaturesDataset.build(
-        inputs,
-        self.encoder,
-        progress=progress,
-        verbose=show_progress,
-        **self.extractor_kw)
-
-  def train(
-      self,
-      train_inputs: list[JsonDict],
-      validation_inputs: list[JsonDict],
-      **train_kw
-  ):
-    # Extract encoder features.
-    logging.info('Train: extracting span representations...')
-    train_edges = self._make_edges(train_inputs, show_progress=True)
-    logging.info('Train: %d edge targets from %d inputs.', len(train_edges),
-                 len(train_inputs))
-    logging.info('Validation: extracting span representations...')
-    validation_edges = self._make_edges(validation_inputs, show_progress=True)
-    logging.info('Validation: %d edge targets from %d inputs.',
-                 len(validation_edges), len(validation_inputs))
-
-    # Train classifier layer.
-    history = self.classifier.train(train_edges.examples,
-                                    validation_edges.examples, **train_kw)
-    return history
-
-  def save(self, path: str):
-    if not os.path.isdir(path):
-      os.mkdir(path)
-    self.classifier.save(os.path.join(path, 'classifier'))
-    self.encoder.save(os.path.join(path, 'encoder'))
-
-  ##
-  # LIT API implementations
-  def max_minibatch_size(self):
-    return self.encoder.max_minibatch_size()
-
-  def predict_minibatch(self, inputs: list[JsonDict]):
-    edges = self._make_edges(inputs, show_progress=False)
-    edge_preds = list(self.classifier.predict(edges.examples))
-    # Re-pack outputs to align with inputs.
-    preds = [{'coref': [], 'tokens': ex['tokens']} for ex in inputs]
-    for edge, ep in zip(edges.examples, edge_preds):
-      orig_input = inputs[edge['src_idx']]
-      orig_edge = orig_input['coref'][edge['edge_idx']]
-      new_edge = EdgeLabel(
-          span1=orig_edge.span1, span2=orig_edge.span2, label=ep['proba'])
-      preds[edge['src_idx']]['coref'].append(new_edge)
-    for ex, p in zip(inputs, preds):
-      # Choose an answer if there are only two target edges.
-      if len(p['coref']) == 2 and 'answer' in ex:
-        probas = np.array([ep.label for ep in p['coref']])
-        # Renormalize as a binary choice.
-        p['pred_answer'] = probas / np.sum(probas)
-      # Otherwise, it's ok for this field to be missing;
-      # metrics will safely ignore.
-    return preds
-
-  def input_spec(self):
-    return {
-        'text':
-            lit_types.TextSegment(),
-        'tokens':
-            lit_types.Tokens(parent='text'),
-        'coref':
-            lit_types.EdgeLabels(align='tokens'),
-        # Index of predicted (single) edge for Winogender
-        'answer':
-            lit_types.CategoryLabel(
-                vocab=winogender.ANSWER_VOCAB, required=False),
-        # TODO(b/172975096): allow plotting of scalars from input data,
-        # so we don't need to add this to the predictions.
-        'pf_bls':
-            lit_types.Scalar(required=False),
-    }
-
-  def output_spec(self):
-    # TODO(lit-dev): also return the embeddings for each span on datasets
-    # with a fixed number of targets; for Winogender this would be
-    # {occupation, other participant, pronoun}
-    return {
-        'tokens':
-            lit_types.Tokens(parent='text'),
-        'coref':
-            lit_types.EdgeLabels(align='tokens'),
-        'pred_answer':
-            lit_types.MulticlassPreds(
-                required=False, vocab=winogender.ANSWER_VOCAB, parent='answer'),
-    }
diff --git a/lit_nlp/examples/coref/retokenize.py b/lit_nlp/examples/coref/retokenize.py
deleted file mode 100644
index 54673a10..00000000
--- a/lit_nlp/examples/coref/retokenize.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Helpers to apply a subword tokenizer while retaining original token boundaries."""
-
-from collections.abc import Callable, Sequence
-import itertools
-
-import numpy as np
-
-
-# tokenizer: str -> list(str)
-TokenizerFn = Callable[[str], Sequence[str]]
-
-
-def flatten(lists):
-  """Flatten a list-of-lists."""
-  return list(itertools.chain.from_iterable(lists))
-
-
-def subtokenize(tokens: Sequence[str], subtokenizer_fn: TokenizerFn):
-  """Apply a sub-word tokenizer and return start indices for the original token boundaries.
-
-  The offsets returned by this can be used directly to project token indices to
-  the new tokenization. For example:
-    span = [i, j]  # end-exclusive,
-    pieces, offsets = subtokenize(tokens, my_tokenizer_fn)
-    wpm_span = offsets[span]  # re-map to subword indices
-
-  Args:
-    tokens: list of strings
-    subtokenizer_fn: function that maps string -> list of strings
-
-  Returns:
-    pieces: (np.ndarray of string) subword strings
-    offsets: (np.ndarray) indices into pieces corresponding to the start of each
-    original token. len(offsets) = len(pieces) + 1, and offsets[-1] ==
-    len(pieces).
-  """
-  # Subwords for each token, as list-of-lists
-  splits = [subtokenizer_fn(token) for token in tokens]
-  lengths = np.array([0] + [len(split) for split in splits])
-  return flatten(splits), np.cumsum(lengths)
diff --git a/website/sphinx_src/api.md b/website/sphinx_src/api.md
index a174bf39..e734be39 100644
--- a/website/sphinx_src/api.md
+++ b/website/sphinx_src/api.md
@@ -859,7 +859,7 @@ naming collisions with protected TypeScript keywords.*
 Some properties of the LIT frontend can be configured from Python as
 **arguments to `dev_server.Server()`**. These include:
 
-*   `page_title`: set a custom page title, such as "Coreference Demo".
+*   `page_title`: set a custom page title.
 *   `canonical_url`: set a "canonical" URL (such as a shortlink) that will be
     used as the base when copying links from the LIT UI.
 *   `default_layout`: set the default UI layout, by name. See `layout.ts` and
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 336d2b43..56aabbc6 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -121,24 +121,6 @@ https://pair-code.github.io/lit/tutorials/generation
 
 ## Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} -->
 
-### Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \
-**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py)
-
-*   Gold-mention coreference model, trained on
-    [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19).
-*   Evaluate on the Winogender schemas
-    ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for
-    gendered associations with profession names.
-*   Visualizations of coreference edges, as well as binary classification
-    between two candidate referents.
-*   Stratified metrics for quantifying model bias as a function of pronoun
-    gender or Bureau of Labor Statistics profession data.
-
-Tip: check out a case study for this demo on the public LIT website:
-https://pair-code.github.io/lit/tutorials/coref
-
 --------------------------------------------------------------------------------
 
 ## Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} -->
diff --git a/website/sphinx_src/docker.md b/website/sphinx_src/docker.md
index c9cad085..dae9c90b 100644
--- a/website/sphinx_src/docker.md
+++ b/website/sphinx_src/docker.md
@@ -54,7 +54,6 @@ below.
 # lit_nlp/examples directory, such as
 #
 #   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
-#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.
 docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index fc70e29c..395dd90d 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -32,10 +32,7 @@ For more details, see
 
 All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications. For examples, see:
-
-*   [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) -
-    includes WMT data for machine translation
+scripts should work without any modifications.
 
 ## Scale
 
diff --git a/website/src/assets/images/lit-coref-compare.png b/website/src/assets/images/lit-coref-compare.png
deleted file mode 100644
index eaa0c7e743b1e4fcc13b17d5cf56d0da7171b4f3..0000000000000000000000000000000000000000
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zazBK1&wT(bz)WJ@;-|+nqvQPswbX-=vD;7<H|Kjx>+ye_cM2QMp_)V$ZRl9amU2@K
zQNjXPWX@RXy0*&UTXxi6f4u%eQmx|4^a)nnjU_-a@-C=wFrexN{lFEc{e9IzdCPNy
zd=jbl*@uyD%8hMAK7jHSk+!ZUVt;q=>i0#<O_AXRc%o_&ZhisR?wrOz59n$(U1k-l
z5L@C;bP1bugWraymZBJ$O~ds`K@OP!H010pc`l`vO0kStR5_RLH&7Q?bQ<_ex3=mk
zK6B5S@W7~@h@7*_>n07j#={QX3h;+T50(|2Rl=i!UjtY0Z`+f<qD5M-afu)INAYWq
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z(1LmDw{&j__g0s?Z5)J`q7q+-=xT;3n%!~ns%w2;sbc~h#R!x>=E&*ha;~Nh9%VX3
z{xpP#9y0%Lu_!z??%ZR4ei4gYqI)7vZ>4GWbM<|wocO1~@StvZsQtTgTBLFKx$eW+
z*XFRKsT-|rr3zn{^SU)`N!NBUUeV-#1VD5bZ>gREtsPBfw3bjlC+dTjoi8#RSg2)<
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diff --git a/website/src/tutorials.md b/website/src/tutorials.md
index 678ef192..a2e95743 100644
--- a/website/src/tutorials.md
+++ b/website/src/tutorials.md
@@ -43,9 +43,6 @@ c-copy: "Learn about how we used LIT to analyze a sentiment classifier." %}
 {% include partials/tutorial-link-element c-title: "Debugging a Text Generator", link: "/tutorials/generation",
 c-copy: "Learn about how we used LIT to debug summarization by a text generation model." %}
 
-{% include partials/tutorial-link-element c-title: "Gender Bias in Coreference", link: "/tutorials/coref",
-c-copy: "Learn how we used LIT to explore gendered associations in a pronoun resolution model." %}
-
 {% include partials/spacer height:50 %}
 
 </div>
diff --git a/website/src/tutorials/coref.md b/website/src/tutorials/coref.md
deleted file mode 100644
index fdc6030b..00000000
--- a/website/src/tutorials/coref.md
+++ /dev/null
@@ -1,64 +0,0 @@
----
-title: Gender Bias in Coreference
-layout: layouts/tutorial.liquid
-
-hero-image: /assets/images/sample-banner.png
-hero-title: "Gender Bias in Coreference"
-hero-copy: "Learn how we can use LIT to explore gendered associations in a pronoun resolution model."
-
-bc-anchor-category: "analysis"
-bc-category-title: "Analysis"
-bc-title: "Coreference"
-
-time: "10 minutes"
-takeaways: "Learn about how to explore fairness using datapoint comparison and metrics comparisons."
----
-
-## Gender Bias in Coreference
-
-{% include partials/link-out link: "../../demos/coref.html", text: "Explore this demo yourself." %}
-
-Or, run your own with [`examples/coref/coref_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py)
-
-Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence "The technician told the customer that they could pay with cash.", we understand from the context that "they" refers to "the customer", the one paying.
-
-The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:
-- "The technician told the customer that **he** could pay with cash."
-- "The technician told the customer that **she** could pay with cash."
-
-In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.
-
-We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:
-
-{% include partials/inset-image image: '/assets/images/lit-coref-pred.png', 
-  caption: 'Above: A coreference prediction.'%}
-
-We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the "Predictions" tab below. To look at two predictions side-by-side, we can enable "Compare datapoints" mode in the toolbar, which will pin our first selection as a "reference" and allow us to select another point to compare:
-
-
-{% include partials/inset-image image: '/assets/images/lit-coref-select.png', 
-  caption: 'Above: Selecting two datapoints to compare.'%}
-
-We see that LIT automatically replicates the predictions view, allowing us to see how our model handles "he" and "she" differently on these two sentences:
-
-{% include partials/inset-image image: '/assets/images/lit-coref-compare.png', 
-  caption: 'Above: Comparing coreference predictions of two datapoints.'%}
-
-To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:
-
-{% include partials/inset-image image: '/assets/images/lit-coref-data.png', 
-  caption: 'Above: Datapoints with extra informational feature columns.'%}
-
-We see that "technician" is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify "he" as the technician in the example above.
-
-Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the "Performance" tab, we see that our model gets around 63% accuracy overall.
-
-{% include partials/inset-image image: '/assets/images/lit-coref-metric-top.png', 
-  caption: 'Above: Overall model accuracy in the metrics table.'%}
-
-Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (< 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:
-
-{% include partials/inset-image image: '/assets/images/lit-coref-metrics.png', 
-  caption: 'Above: Metrics faceted into sub-groups and scalar results plots.'%}
-
-We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.
diff --git a/website/src/tutorials/sequence-salience.md b/website/src/tutorials/sequence-salience.md
index bb045c1e..371b1271 100644
--- a/website/src/tutorials/sequence-salience.md
+++ b/website/src/tutorials/sequence-salience.md
@@ -280,8 +280,7 @@ them in prompt engineering use cases.
 ## Case Study 3: Side-by-Side Behavior Comparisons
 
 LIT support a [side-by-side (SxS) mode][lit_sxs] that can be used to compare two
-models, or here, compare model behavior on two related examples. (Check out the
-[gender bias tutorial][coref_tutorial] for another case study in SxS analysis.)
+models, or here, compare model behavior on two related examples.
 Let's see how we can use this to understand differences in prompt designs with
 Sequence Salience.
 
@@ -451,7 +450,6 @@ helpful guides that can help you develop better prompts, including:
 [ai_studio]: https://cloud.google.com/generative-ai-studio?hl=en
 [constitution_maker]: https://arxiv.org/abs/2310.15428
 [constitutions]: https://arxiv.org/abs/2212.08073
-[coref_tutorial]: ../../tutorials/coref/
 [cot]: https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html
 [data_table]: ../../documentation/ui_guide.html#data-table
 [datapoint_editor]: ../../documentation/ui_guide.html#datapoint-editor

From aa493409c454a2ed269fdedd15353404c14b4936 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Fri, 7 Jun 2024 08:53:19 -0700
Subject: [PATCH 16/47] Remove the t5 demo, models, dataset, and other related
 documentation from the LIT examples.

PiperOrigin-RevId: 641263684
---
 lit_nlp/components/similarity_searcher.py  |  76 ---
 lit_nlp/examples/blank_slate_demo.py       |  20 -
 lit_nlp/examples/datasets/mt.py            | 201 --------
 lit_nlp/examples/datasets/summarization.py | 129 -----
 lit_nlp/examples/models/t5.py              | 561 ---------------------
 lit_nlp/examples/models/t5_int_test.py     |  25 -
 lit_nlp/examples/models/t5_test.py         |  20 -
 lit_nlp/examples/t5_demo.py                | 245 ---------
 website/sphinx_src/components.md           |  11 +-
 website/sphinx_src/demos.md                |  21 -
 website/src/tutorials/generation.md        |  38 --
 11 files changed, 5 insertions(+), 1342 deletions(-)
 delete mode 100644 lit_nlp/components/similarity_searcher.py
 delete mode 100644 lit_nlp/examples/datasets/mt.py
 delete mode 100644 lit_nlp/examples/datasets/summarization.py
 delete mode 100644 lit_nlp/examples/models/t5.py
 delete mode 100644 lit_nlp/examples/models/t5_int_test.py
 delete mode 100644 lit_nlp/examples/models/t5_test.py
 delete mode 100644 lit_nlp/examples/t5_demo.py
 delete mode 100644 website/src/tutorials/generation.md

diff --git a/lit_nlp/components/similarity_searcher.py b/lit_nlp/components/similarity_searcher.py
deleted file mode 100644
index acd17336..00000000
--- a/lit_nlp/components/similarity_searcher.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Uses nearest neighbor search for similar examples."""
-
-from typing import Optional
-
-from lit_nlp.api import components as lit_components
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types
-from lit_nlp.components import index
-from lit_nlp.lib import utils
-
-JsonDict = types.JsonDict
-IndexedInput = types.IndexedInput
-
-
-class SimilaritySearcher(lit_components.Generator):
-  """Searching by similarity."""
-
-  def __init__(self, indexer: index.Indexer):
-    self.index = indexer
-
-  def _get_embedding(self, example: types.Input, model: lit_model.Model,
-                     embedding_name: str):
-    """Calls the model on the example to get the embedding."""
-    model_output = model.predict([example])
-    embedding = list(model_output)[0][embedding_name]
-    return embedding
-
-  def _find_nn(self, model_name, dataset_name, embedding_name, embedding):
-    """wrapper around the Index() class api."""
-    similar_examples = self.index.find_nn(
-        model_name, dataset_name, embedding_name, embedding, num_neighbors=25)
-    return similar_examples
-
-  def is_compatible(self, model: lit_model.Model,
-                    dataset: lit_dataset.Dataset) -> bool:
-    dataset_embs = utils.spec_contains(dataset.spec(), types.Embeddings)
-    model_in_embs = utils.spec_contains(model.input_spec(), types.Embeddings)
-    model_out_embs = utils.spec_contains(model.output_spec(), types.Embeddings)
-    return dataset_embs or model_in_embs or model_out_embs
-
-  def generate(  # pytype: disable=signature-mismatch  # overriding-parameter-type-checks
-      self,
-      example: types.Input,
-      model: lit_model.Model,
-      dataset: lit_dataset.IndexedDataset,
-      config: Optional[JsonDict] = None) -> list[JsonDict]:
-    """Find similar examples for an example/model/dataset."""
-    model_name = config['model_name']
-    dataset_name = config['dataset_name']
-    embedding_name = config['Embedding Field']
-    embedding = self._get_embedding(example, model, embedding_name)
-    neighbors = self._find_nn(model_name, dataset_name, embedding_name,
-                              embedding)
-    return neighbors
-
-  def config_spec(self) -> types.Spec:
-    return {
-        # Requires an embedding layer specified from a model.
-        'Embedding Field': types.SingleFieldMatcher(
-            spec='output', types=['Embeddings'])
-    }
diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index 6fbefd40..32642a74 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -32,11 +32,8 @@
 from lit_nlp.examples.datasets import classification
 from lit_nlp.examples.datasets import glue
 from lit_nlp.examples.datasets import lm
-from lit_nlp.examples.datasets import mt
-from lit_nlp.examples.datasets import summarization
 from lit_nlp.examples.models import glue_models
 from lit_nlp.examples.models import pretrained_lms
-from lit_nlp.examples.models import t5
 from lit_nlp.examples.penguin import data as penguin_data
 from lit_nlp.examples.penguin import model as penguin_model
 
@@ -88,16 +85,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       penguin_model.PenguinModel.init_spec(),
   )
 
-  # t5 demo model loaders.
-  model_loaders["T5 summarization"] = (
-      t5.T5Summarization,
-      t5.T5Summarization.init_spec(),
-  )
-  model_loaders["T5 translation"] = (
-      t5.T5Translation,
-      t5.T5Translation.init_spec(),
-  )
-
   # lm demo model loaders.
   model_loaders["bert"] = (
       pretrained_lms.BertMLM,
@@ -122,13 +109,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       penguin_data.PenguinDataset.init_spec(),
   )
 
-  # t5 demo dataset loaders.
-  dataset_loaders["CNN DailyMail (t5)"] = (
-      summarization.CNNDMData,
-      summarization.CNNDMData.init_spec(),
-  )
-  dataset_loaders["WMT 14 (t5)"] = (mt.WMT14Data, mt.WMT14Data.init_spec())
-
   # lm demo dataset loaders.
   dataset_loaders["sst (lm)"] = (
       glue.SST2DataForLM,
diff --git a/lit_nlp/examples/datasets/mt.py b/lit_nlp/examples/datasets/mt.py
deleted file mode 100644
index 5ddaa15a..00000000
--- a/lit_nlp/examples/datasets/mt.py
+++ /dev/null
@@ -1,201 +0,0 @@
-"""Machine translation datasets."""
-
-from collections.abc import Mapping
-from typing import Any, Optional
-
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import file_cache
-import pandas as pd
-import six
-import tensorflow_datasets as tfds
-
-JsonDict = lit_types.JsonDict
-Spec = lit_types.Spec
-
-_VALID_WMT14_LANG_CODES = ('cs', 'de', 'fr', 'en', 'hi', 'ru')
-_VALID_WMT14_LANG_PAIRS = ('cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en')
-
-
-class WMT14Data(lit_dataset.Dataset):
-  """WMT '14 machine-translation data, via TFDS."""
-
-  tfds_name = 'wmt14_translate'
-
-  def __init__(
-      self,
-      version: str = 'fr-en',
-      reverse: bool = False,
-      split: str = 'validation',
-      filepath: Optional[str] = None,
-      max_examples: Optional[int] = None,
-  ):
-    """Initializes a Dataset wrapper for the WMT-14 dataset.
-
-    More information on the WMT 14 dataset can be found at the links below.
-
-    * [WMT 14 website](https://www.statmt.org/wmt14/translation-task.html)
-    * [TFDS docs](https://www.tensorflow.org/datasets/catalog/wmt14_translate)
-    * [Bojar et al. 2014](https://aclanthology.org/W14-3302/)
-
-    Args:
-      version: An identifier for the dataset comprising two different [ISO
-        639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) language
-        codes separated by a `-`. WMT14 supports a limited subset of languages,
-        valid versions include `cs-en`, `de-en`, `fr-en`, `hi-en`, and `ru-en`.
-      reverse: If true, invert the source and target languages.
-      split: The subset of the data to dowload from TFDS.
-      filepath: If provided, the dataset will be loaded from disk instead of
-        downloaded from TFDS. This is useful for containerized demos where the
-        process of downloading and processing the dataset with TFDS can cause
-        the container to timeout and restart on a pseudo-inifinite loop.
-      max_examples: The maximum number of examples to load in.
-
-    Raises:
-      ValueError: Invalid version identifier.
-    """
-    if version not in _VALID_WMT14_LANG_PAIRS:
-      raise ValueError(f'Invalid version, {version}. Expected one of '
-                       f'{str(_VALID_WMT14_LANG_PAIRS)}.')
-
-    lang_keys = version.split('-')
-    if reverse:
-      source_key = lang_keys[1]
-      target_key = lang_keys[0]
-    else:
-      source_key = lang_keys[0]
-      target_key = lang_keys[1]
-
-    # Load dataset
-    if filepath:
-      self._examples = self._load_datapoints(filepath, source_key, target_key)
-    else:
-      ds_name = f'{self.tfds_name}/{version}'
-      ds = tfds.load(ds_name, download=True, try_gcs=True, split=split)
-      ds_np = list(tfds.as_numpy(ds))
-      self._examples = [
-          self._record_to_dict(d, source_key, target_key) for d in ds_np
-      ][:max_examples]
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    default_filepath = ''
-    return {
-        'version': lit_types.String(default='fr-en'),
-        'reverse': lit_types.Boolean(default=False),
-        'split': lit_types.String(default='validation'),
-        'filepath': lit_types.String(
-            default=default_filepath, required=False
-        ),
-        'max_examples': lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
-
-  def _load_datapoints(self,
-                       path: str,
-                       source_key: str,
-                       target_key: str,
-                       skiplines: int = 0) -> list[JsonDict]:
-    """Loads the dataset from a CSV file.
-
-    Args:
-      path: The location of the CSV file.
-      source_key: ISO 639-1 code for the source language.
-      target_key: ISO 639-1 code for the target language.
-      skiplines: The number of lines to skip in the input file(s).
-
-    Returns:
-      The list of exmaples loaded from the file.
-
-    Raises:
-      ValueError: invalid key passed for the source or target language.
-    """
-    if source_key not in _VALID_WMT14_LANG_CODES:
-      raise ValueError(f'Invalid source_key, {source_key}. Expected one of '
-                       f'{str(_VALID_WMT14_LANG_CODES)}.')
-
-    if target_key not in _VALID_WMT14_LANG_CODES:
-      raise ValueError(f'Invalid target_key, {target_key}. Expected one of '
-                       f'{str(_VALID_WMT14_LANG_CODES)}.')
-
-    path = file_cache.cached_path(path)
-    with open(path) as fd:
-      df = pd.read_csv(fd, skiprows=skiplines)
-    return [
-        self._record_to_dict(row, source_key, target_key)
-        for _, row in df.iterrows()
-    ]
-
-  def _record_to_dict(
-      self,
-      record: Mapping[str, Any],
-      source_key: str,
-      target_key: str,
-  ) -> JsonDict:
-    """Converts a record (typically np.array or pd.Series) into a JsonDict."""
-    return {
-        'source': six.ensure_text(record[source_key]),
-        'source_language': source_key,
-        'target': six.ensure_text(record[target_key]),
-        'target_language': target_key,
-    }
-
-  def spec(self) -> Spec:
-    return {
-        'source': lit_types.TextSegment(),
-        'source_language': lit_types.CategoryLabel(),
-        'target': lit_types.TextSegment(),
-        'target_language': lit_types.CategoryLabel(),
-    }
-
-
-class WMT17Data(lit_dataset.Dataset):
-  """WMT '17 machine-translation data, via TFDS."""
-
-  def __init__(self,
-               version: str = 'de-en',
-               reverse: bool = False,
-               split: str = 'validation',
-               max_examples: Optional[int] = None):
-    lang_keys = version.split('-')
-    assert len(lang_keys) == 2
-    if reverse:
-      source_key = lang_keys[1]
-      target_key = lang_keys[0]
-    else:
-      source_key = lang_keys[0]
-      target_key = lang_keys[1]
-
-    # Pre-load dataset
-    ds_name = 'wmt17_translate/' + version
-    ds = tfds.load(ds_name, download=True, try_gcs=True, split=split)
-    ds_np = list(tfds.as_numpy(ds))
-    # pylint: disable=g-complex-comprehension
-    self._examples = [{
-        'source': six.ensure_text(d[source_key]),
-        'source_language': source_key,
-        'target': six.ensure_text(d[target_key]),
-        'target_language': target_key,
-    } for d in ds_np]
-    # pylint: enable=g-complex-comprehension
-    self._examples = self._examples[:max_examples]
-
-  def spec(self) -> Spec:
-    return {
-        'source': lit_types.TextSegment(),
-        'source_language': lit_types.CategoryLabel(required=False),
-        'target': lit_types.TextSegment(),
-        'target_language': lit_types.CategoryLabel(required=False),
-    }
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        'version': lit_types.String(default='de-en'),
-        'reverse': lit_types.Boolean(default=False),
-        'split': lit_types.String(default='validation'),
-        'max_examples': lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
diff --git a/lit_nlp/examples/datasets/summarization.py b/lit_nlp/examples/datasets/summarization.py
deleted file mode 100644
index 1fd7cfff..00000000
--- a/lit_nlp/examples/datasets/summarization.py
+++ /dev/null
@@ -1,129 +0,0 @@
-"""Data loaders for summarization datasets."""
-
-from typing import Optional
-
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import file_cache
-import pandas as pd
-import tensorflow_datasets as tfds
-
-
-class GigawordData(lit_dataset.Dataset):
-  """English Gigaword summarization dataset."""
-
-  def __init__(self, split="validation", max_examples: Optional[int] = None):
-    """Dataset constructor, loads the data into memory."""
-    ds = tfds.load("gigaword", split=split)
-
-    self._examples = []  # populate this with data records
-    if max_examples is not None:
-      ds = ds.take(max_examples)
-    for record in ds:
-      self._examples.append({
-          "document": record["document"].numpy().decode("utf-8"),
-          "reference": record["summary"].numpy().decode("utf-8"),
-      })
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "split": lit_types.String(default="validation"),
-        "max_examples": lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
-
-  def spec(self) -> lit_types.Spec:
-    """Dataset spec, which should match the model"s input_spec()."""
-    return {
-        "document": lit_types.TextSegment(),
-        "reference": lit_types.TextSegment(),
-    }
-
-
-class CNNDMData(lit_dataset.Dataset):
-  """English CNNDM summarization dataset."""
-
-  tfds_name = "cnn_dailymail"
-
-  def __init__(
-      self,
-      split: str = "validation",
-      max_examples: Optional[int] = None,
-      max_seq_len: int = 500,
-      filepath: Optional[str] = None,
-  ):
-    """Initializes a Dataset wrapper for the the CNN DailyMail dataset.
-
-    Args:
-      split: The subset of the data to dowload from TFDS.
-      max_examples: The number of examples to include from the TFDS dataset.
-      max_seq_len: The maximum length to load for any document in the dataset.
-      filepath: If provided, the dataset will be loaded from disk instead of
-        downloaded from TFDS. This is useful for containerized demos where the
-        process of downloading and processing the dataset with TFDS can cause
-        the container to timeout and restart on a pseudo-inifinite loop.
-    """
-
-    if filepath:
-      examples = self.load_datapoints(filepath)[:max_examples]
-    else:
-      ds = tfds.load(self.tfds_name, split=split)
-      examples = []
-      if max_examples is not None:
-        ds = ds.take(max_examples)
-      for record in ds:
-        # format and truncate from the end to max_seq_len tokens.
-        document = " ".join(record["article"].numpy().decode("utf-8").replace(
-            "<br />", "").split()[-max_seq_len:])
-        reference = record["highlights"].numpy().decode("utf-8")
-        examples.append({"document": document, "reference": reference})
-
-    self._examples = examples
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    default_filepath = ''
-    return {
-        "split": lit_types.String(default="validation"),
-        "max_examples": lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-        "max_seq_len": lit_types.Integer(default=500, min_val=1, max_val=1024),
-        "filepath": lit_types.String(
-            default=default_filepath, required=False
-        ),
-    }
-
-  def load_datapoints(self,
-                      path: str,
-                      max_seq_len: int = 500,
-                      skiplines: int = 0) -> list[lit_types.JsonDict]:
-    """Loads the dataset from a CSV file.
-
-    Args:
-      path: The location of the CSV file.
-      max_seq_len: The maximum length to load for any document in the dataset.
-      skiplines: The number of lines to skip in the input file(s).
-
-    Returns:
-      The list of exmaples loaded from the file.
-    """
-    examples = []
-    path = file_cache.cached_path(path)
-    with open(path) as fd:
-      df = pd.read_csv(fd, skiprows=skiplines)
-    for _, row in df.iterrows():
-      examples.append({
-          "document": row["article"][-max_seq_len:],
-          "reference": row["highlights"],
-      })
-    return examples
-
-  def spec(self) -> lit_types.Spec:
-    """Dataset spec, which should match the model"s input_spec()."""
-    return {
-        "document": lit_types.TextSegment(),
-        "reference": lit_types.TextSegment(),
-    }
diff --git a/lit_nlp/examples/models/t5.py b/lit_nlp/examples/models/t5.py
deleted file mode 100644
index 86899208..00000000
--- a/lit_nlp/examples/models/t5.py
+++ /dev/null
@@ -1,561 +0,0 @@
-"""LIT wrappers for T5, supporting both HuggingFace and SavedModel formats."""
-import re
-
-import attr
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import model_utils
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-
-import tensorflow as tf
-# tensorflow_text is required for T5 SavedModel
-import tensorflow_text  # pylint: disable=unused-import
-import transformers
-
-from rouge_score import rouge_scorer
-
-JsonDict = lit_types.JsonDict
-_DEFAULT_MODEL_PATH = "https://storage.googleapis.com/what-if-tool-resources/lit-models/t5-small.tar.gz"
-
-
-def masked_token_mean(vectors, masks):
-  """Mean over tokens.
-
-  Args:
-    vectors: <tf.float32>[batch_size, num_tokens, emb_dim]
-    masks: <tf.int32>[batch_size, num_tokens]
-
-  Returns:
-    <tf.float32>[batch_size, emb_dim]
-  """
-  masks = tf.cast(masks, tf.float32)
-  weights = masks / tf.reduce_sum(masks, axis=1, keepdims=True)
-  return tf.reduce_sum(vectors * tf.expand_dims(weights, axis=-1), axis=1)
-
-
-@attr.s(auto_attribs=True, kw_only=True)
-class T5ModelConfig(object):
-  """Config options for a T5 generation model."""
-  # Input options
-  inference_batch_size: int = 4
-  # Generation options
-  beam_size: int = 4
-  max_gen_length: int = 50
-  num_to_generate: int = 1
-  # Decoding options
-  token_top_k: int = 10
-  output_attention: bool = False
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "inference_batch_size": lit_types.Integer(
-            default=4, min_val=1, max_val=16, required=False
-        ),
-        "beam_size": lit_types.Integer(
-            default=4, min_val=1, max_val=32, required=False
-        ),
-        "max_gen_length": lit_types.Integer(
-            default=50, min_val=1, max_val=100, required=False
-        ),
-        "num_to_generate": lit_types.Integer(
-            default=1, min_val=1, max_val=10, required=False
-        ),
-        "token_top_k": lit_types.Integer(
-            default=10, min_val=1, max_val=25, required=False
-        ),
-        "output_attention": lit_types.Boolean(default=False, required=False),
-    }
-
-
-def validate_t5_model(model: lit_model.Model) -> lit_model.Model:
-  """Validate that a given model looks like a T5 model.
-
-  This checks the model spec at runtime; it is intended to be used before server
-  start, such as in the __init__() method of a wrapper class.
-
-  Args:
-    model: a LIT model
-
-  Returns:
-    model: the same model
-
-  Raises:
-    AssertionError: if the model's spec does not match that expected for a T5
-    model.
-  """
-  # Check inputs
-  ispec = model.input_spec()
-  assert "input_text" in ispec
-  assert isinstance(ispec["input_text"], lit_types.TextSegment)
-  if "target_text" in ispec:
-    assert isinstance(ispec["target_text"], lit_types.TextSegment)
-
-  # Check outputs
-  ospec = model.output_spec()
-  assert "output_text" in ospec
-  assert isinstance(
-      ospec["output_text"],
-      (lit_types.GeneratedText, lit_types.GeneratedTextCandidates))
-  assert ospec["output_text"].parent == "target_text"
-
-  return model
-
-
-class T5SavedModel(lit_model.BatchedModel):
-  """T5 from a TensorFlow SavedModel, for black-box access.
-
-  To create a SavedModel from a regular T5 checkpoint, see
-  https://github.com/google-research/text-to-text-transfer-transformer#export
-  """
-
-  def __init__(self, saved_model_path: str, model=None, **config_kw):
-    super().__init__()
-    # By default, SavedModels from the original T5 codebase have batch_size=1
-    # hardcoded. Use setdefault here so that the user can still override if
-    # they've fixed this upstream.
-    config_kw.setdefault("inference_batch_size", 1)
-    self.config = T5ModelConfig(**config_kw)
-    self.model = model or tf.saved_model.load(saved_model_path)
-
-  ##
-  # LIT API implementations
-  def max_minibatch_size(self) -> int:
-    # The lit.Model base class handles batching automatically in the
-    # implementation of predict(), and uses this value as the batch size.
-    return self.config.inference_batch_size
-
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    model_inputs = tf.constant([ex["input_text"] for ex in inputs])
-    model_outputs = self.model.signatures["serving_default"](model_inputs)
-    return [{
-        "output_text": m.decode("utf-8")
-    } for m in model_outputs["outputs"].numpy()]
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "saved_model_path": lit_types.String(),
-        **T5ModelConfig.init_spec(),
-    }
-
-  def input_spec(self):
-    return {
-        "input_text": lit_types.TextSegment(),
-        "target_text": lit_types.TextSegment(required=False),
-    }
-
-  def output_spec(self):
-    return {"output_text": lit_types.GeneratedText(parent="target_text")}
-
-
-class T5HFModel(lit_model.BatchedModel):
-  """T5 using HuggingFace Transformers and Keras.
-
-  This version supports embeddings, attention, and force-decoding of the target
-  text, as well as more options to control decoding (such as beam search).
-  """
-
-  @property
-  def num_layers(self):
-    return self.model.config.num_layers
-
-  def __init__(
-      self,
-      model_name_or_path="t5-small",
-      model=None,
-      tokenizer=None,
-      **config_kw,
-  ):
-    super().__init__()
-    # Normally path is a directory; if it's an archive file, download and
-    # extract to the transformers cache.
-    if model_name_or_path.endswith(".tar.gz"):
-      model_name_or_path = file_cache.cached_path(
-          model_name_or_path, extract_compressed_file=True
-      )
-
-    self.config = T5ModelConfig(**config_kw)
-    assert self.config.num_to_generate <= self.config.beam_size
-    self.tokenizer = tokenizer or transformers.T5Tokenizer.from_pretrained(
-        model_name_or_path
-    )
-    self.model = model or model_utils.load_pretrained(
-        transformers.TFT5ForConditionalGeneration,
-        model_name_or_path,
-        output_hidden_states=True,
-        output_attentions=self.config.output_attention,
-    )
-
-  def _encode_texts(self, texts: list[str]):
-    return self.tokenizer.batch_encode_plus(
-        texts,
-        return_tensors="tf",
-        padding="longest",
-        truncation="longest_first")
-
-  def _force_decode(self, encoded_inputs, encoded_targets):
-    """Get predictions for a batch of tokenized examples.
-
-    Each forward pass produces the following:
-      logits: batch_size x dec_len x vocab_size
-      decoder_past_key_value_states: tuple with cached outputs.
-      dec_states: tuple[len:dec_layers]:
-                  batch_size x dec_len x hid_size
-      dec_attn: [optional] tuple[len:dec_layers+1]
-                batch_size x num_heads x dec_len x dec_len
-      enc_final_state: batch_size x enc_len x hid_size
-      enc_states: tuple[len:enc_layers]:
-                  batch_size x enc_len x hid_size
-      enc_attn: [optional] tuple[len:enc_layers+1]
-                batch_size x num_heads x enc_len x enc_len
-
-    The two optional attention fields are only returned if
-    config.output_attention is set.
-
-    Args:
-      encoded_inputs: Dict as returned from Tokenizer for inputs.
-      encoded_targets: Dict as returned from Tokenizer for outputs
-
-    Returns:
-      batched_outputs: dict[str, tf.Tensor]
-    """
-    results = self.model(
-        input_ids=encoded_inputs["input_ids"],
-        decoder_input_ids=encoded_targets["input_ids"],
-        attention_mask=encoded_inputs["attention_mask"],
-        decoder_attention_mask=encoded_targets["attention_mask"])
-
-    model_probs = tf.nn.softmax(results.logits, axis=-1)
-    top_k = tf.math.top_k(
-        model_probs, k=self.config.token_top_k, sorted=True, name=None)
-    batched_outputs = {
-        "input_ids": encoded_inputs["input_ids"],
-        "input_ntok": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1),
-        "target_ids": encoded_targets["input_ids"],
-        "target_ntok": tf.reduce_sum(encoded_targets["attention_mask"], axis=1),
-        "top_k_indices": top_k.indices,
-        "top_k_probs": top_k.values,
-    }
-    # encoder_last_hidden_state is <float>[batch_size, num_tokens, emb_dim]
-    # take the mean over real tokens to get <float>[batch_size, emb_dim]
-    batched_outputs["encoder_final_embedding"] = masked_token_mean(
-        results.encoder_last_hidden_state, encoded_inputs["attention_mask"])
-
-    if self.config.output_attention:
-      for i in range(len(results.decoder_attentions)):
-        batched_outputs[
-            f"decoder_layer_{i+1:d}_attention"] = results.decoder_attentions[i]
-      for i in range(len(results.encoder_attentions)):
-        batched_outputs[
-            f"encoder_layer_{i+1:d}_attention"] = results.encoder_attentions[i]
-
-    return batched_outputs
-
-  def _postprocess(self, preds):
-    """Post-process single-example preds. Operates on numpy arrays."""
-    # Return tokenization for input text.
-    input_ntok = preds.pop("input_ntok")
-    input_ids = preds.pop("input_ids")[:input_ntok]
-    preds["input_tokens"] = self.tokenizer.convert_ids_to_tokens(input_ids)
-    # Return tokenization for target text.
-    target_ntok = preds.pop("target_ntok")
-    target_ids = preds.pop("target_ids")[:target_ntok]
-    preds["target_tokens"] = self.tokenizer.convert_ids_to_tokens(target_ids)
-
-    # Decode predicted top-k tokens.
-    # token_topk_preds will be a list[list[(word, prob)]]
-    # Initialize prediction for 0th token as N/A.
-    token_topk_preds = [[("N/A", 1.)]]
-    pred_ids = preds.pop("top_k_indices")[:target_ntok]  # <int>[num_tokens, k]
-    pred_probs = preds.pop(
-        "top_k_probs")[:target_ntok]  # <float32>[num_tokens, k]
-    for token_pred_ids, token_pred_probs in zip(pred_ids, pred_probs):
-      token_pred_words = self.tokenizer.convert_ids_to_tokens(token_pred_ids)
-      token_topk_preds.append(list(zip(token_pred_words, token_pred_probs)))
-    preds["pred_tokens"] = token_topk_preds
-
-    # Decode generated ids
-    candidates = [
-        self.tokenizer.decode(ids, skip_special_tokens=True)
-        for ids in preds.pop("generated_ids")
-    ]
-    if self.config.num_to_generate > 1:
-      preds["output_text"] = [(s, None) for s in candidates]
-    else:
-      preds["output_text"] = candidates[0]
-
-    # Process attention fields, if present.
-    for key in preds:
-      if not re.match(r"\w+_layer_(\d+)/attention", key):
-        continue
-      if key.startswith("encoder_"):
-        ntok = input_ntok
-      elif key.startswith("decoder_"):
-        ntok = target_ntok
-      else:
-        raise ValueError(f"Invalid attention key: '{key}'")
-      # Select only real tokens, since most of this matrix is padding.
-      # <float32>[num_heads, max_seq_length, max_seq_length]
-      # -> <float32>[num_heads, num_tokens, num_tokens]
-      preds[key] = preds[key][:, :ntok, :ntok].transpose((0, 2, 1))
-      # Make a copy of this array to avoid memory leaks, since NumPy otherwise
-      # keeps a pointer around that prevents the source array from being GCed.
-      preds[key] = preds[key].copy()
-
-    return preds
-
-  ##
-  # LIT API implementations
-  def max_minibatch_size(self) -> int:
-    # The lit.Model base class handles batching automatically in the
-    # implementation of predict(), and uses this value as the batch size.
-    return self.config.inference_batch_size
-
-  def predict_minibatch(self, inputs: list[JsonDict]) -> list[JsonDict]:
-    """Run model on a single batch."""
-    # Text as sequence of sentencepiece ID"s.
-    encoded_inputs = self._encode_texts([ex["input_text"] for ex in inputs])
-    encoded_targets = self._encode_texts(
-        [ex.get("target_text", "") for ex in inputs])
-
-    ##
-    # Force-decode on target text, and also get encoder embs and attention.
-    batched_outputs = self._force_decode(encoded_inputs, encoded_targets)
-    # Get the conditional generation from the model.
-    # Workaround for output_hidden not being compatible with generate.
-    # See https://github.com/huggingface/transformers/issues/8361
-    self.model.config.output_hidden_states = False
-    generated_ids = self.model.generate(
-        encoded_inputs.input_ids,
-        num_beams=self.config.beam_size,
-        attention_mask=encoded_inputs.attention_mask,
-        max_length=self.config.max_gen_length,
-        num_return_sequences=self.config.num_to_generate)
-    # [batch_size*num_return_sequences, num_steps]
-    # -> [batch_size, num_return_sequences, num_steps]
-    batched_outputs["generated_ids"] = tf.reshape(
-        generated_ids,
-        [-1, self.config.num_to_generate, generated_ids.shape[-1]])
-    self.model.config.output_hidden_states = True
-
-    # Convert to numpy for post-processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    # Split up batched outputs, then post-process each example.
-    unbatched_outputs = utils.unbatch_preds(detached_outputs)
-    return list(map(self._postprocess, unbatched_outputs))
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "model_name": lit_types.String(
-            default=_DEFAULT_MODEL_PATH, required=False
-        ),
-        **T5ModelConfig.init_spec(),
-    }
-
-  def input_spec(self):
-    return {
-        "input_text": lit_types.TextSegment(),
-        "target_text": lit_types.TextSegment(required=False),
-    }
-
-  def output_spec(self):
-    spec = {
-        "output_text": lit_types.GeneratedText(parent="target_text"),
-        "input_tokens": lit_types.Tokens(parent="input_text"),
-        "encoder_final_embedding": lit_types.Embeddings(),
-        # If target text is given, the following will also be populated.
-        "target_tokens": lit_types.Tokens(parent="target_text"),
-        "pred_tokens": lit_types.TokenTopKPreds(align="target_tokens"),
-    }
-    if self.config.num_to_generate > 1:
-      spec["output_text"] = lit_types.GeneratedTextCandidates(
-          parent="target_text")
-
-    if self.config.output_attention:
-      # Add attention for each layer.
-      for i in range(self.num_layers):
-        spec[f"encoder_layer_{i+1:d}_attention"] = lit_types.AttentionHeads(
-            align_in="input_tokens", align_out="input_tokens")
-        spec[f"decoder_layer_{i+1:d}_attention"] = lit_types.AttentionHeads(
-            align_in="target_tokens", align_out="target_tokens")
-    return spec
-
-
-##
-# Task-specific wrapper classes.
-
-
-class TranslationWrapper(lit_model.ModelWrapper):
-  """Wrapper class for machine translation."""
-
-  # Mapping from generic T5 fields to this task
-  FIELD_RENAMES = {
-      "input_text": "source",
-      "target_text": "target",
-      "output_text": "translation",
-  }
-
-  # From Appendix D of https://arxiv.org/pdf/1910.10683.pdf.
-  # Add more of these if your model supports them.
-  LANGCODE_TO_NAME = {
-      "en": "English",
-      "de": "German",
-      "fr": "French",
-      "ro": "Romanian",
-  }
-
-  INPUT_TEMPLATE = "translate {source_language} to {target_language}: {source}"
-
-  def __init__(self, model: lit_model.Model):
-    model = validate_t5_model(model)
-    super().__init__(model)
-
-  def preprocess(self, ex: JsonDict) -> JsonDict:
-    input_kw = {
-        "source_language": self.LANGCODE_TO_NAME[ex["source_language"]],
-        "target_language": self.LANGCODE_TO_NAME[ex["target_language"]],
-        "source": ex["source"]
-    }
-    ret = {"input_text": self.INPUT_TEMPLATE.format(**input_kw)}
-    if "target" in ex:
-      ret["target_text"] = ex["target"]
-    return ret
-
-  ##
-  # LIT API implementation
-  def description(self) -> str:
-    return "T5 for machine translation\n" + self.wrapped.description()
-
-  def predict(self, inputs):
-    """Predict on a single minibatch of examples."""
-    model_inputs = (self.preprocess(ex) for ex in inputs)
-    outputs = self.wrapped.predict(model_inputs)
-    return (utils.remap_dict(mo, self.FIELD_RENAMES) for mo in outputs)
-
-  def input_spec(self):
-    spec = lit_types.remap_spec(self.wrapped.input_spec(), self.FIELD_RENAMES)
-    spec["source_language"] = lit_types.CategoryLabel()
-    spec["target_language"] = lit_types.CategoryLabel()
-    return spec
-
-  def output_spec(self):
-    return lit_types.remap_spec(self.wrapped.output_spec(), self.FIELD_RENAMES)
-
-
-class SummarizationWrapper(lit_model.ModelWrapper):
-  """Wrapper class to perform a summarization task."""
-
-  # Mapping from generic T5 fields to this task
-  FIELD_RENAMES = {
-      "input_text": "document",
-      "target_text": "reference",
-  }
-
-  def __init__(self, model: lit_model.Model):
-    model = validate_t5_model(model)
-    super().__init__(model)
-
-    # TODO(gehrmann): temp solution for ROUGE.
-    self._scorer = rouge_scorer.RougeScorer(["rougeL"], use_stemmer=True)
-    # If output is list[(str, score)] instead of just str
-    self._multi_output = isinstance(self.output_spec()["output_text"],
-                                    lit_types.GeneratedTextCandidates)
-    self._get_pred_string = (
-        lit_types.GeneratedTextCandidates.top_text if self._multi_output else
-        (lambda x: x))
-
-  def preprocess(self, ex: JsonDict) -> JsonDict:
-    ret = {"input_text": "summarize: " + ex["document"]}
-    if "reference" in ex:
-      ret["target_text"] = ex["reference"]
-    return ret
-
-  ##
-  # LIT API implementation
-  def description(self) -> str:
-    return "T5 for summarization\n" + self.wrapped.description()
-
-  def predict(self, inputs):
-    """Predict on a single minibatch of examples."""
-    inputs = list(inputs)  # needs to be referenced below, so keep full list
-    model_inputs = (self.preprocess(ex) for ex in inputs)
-    outputs = self.wrapped.predict(model_inputs)
-    outputs = (utils.remap_dict(mo, self.FIELD_RENAMES) for mo in outputs)
-
-    # TODO(gehrmann): temp solution to get ROUGE scores in data table.
-    for ex, mo in zip(inputs, outputs):
-      score = self._scorer.score(
-          target=ex["reference"],
-          prediction=self._get_pred_string(mo["output_text"]))
-      mo["rougeL"] = float(score["rougeL"].fmeasure)
-      yield mo
-
-  def input_spec(self):
-    return lit_types.remap_spec(self.wrapped.input_spec(), self.FIELD_RENAMES)
-
-  def output_spec(self):
-    spec = lit_types.remap_spec(self.wrapped.output_spec(), self.FIELD_RENAMES)
-    spec["rougeL"] = lit_types.Scalar()
-    return spec
-
-
-class T5Translation(TranslationWrapper):
-  """T5 translation model.
-
-  TranslationWrapper class has input_specs compatible with the corresponding
-  dataset, but its init args are not supported by the front-end system, thus
-  we set up a layer of init args on top to work with the front-end.
-  """
-
-  def __init__(
-      self,
-      model_name_or_path=_DEFAULT_MODEL_PATH,
-      model=None,
-      tokenizer=None,
-      **config_kw,
-  ):
-    model = T5HFModel(model_name_or_path, model, tokenizer, **config_kw)
-    super().__init__(model)
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "model_name_or_path": lit_types.String(
-            default=_DEFAULT_MODEL_PATH,
-            required=False,
-        ),
-        **T5ModelConfig.init_spec(),
-    }
-
-
-class T5Summarization(SummarizationWrapper):
-  """T5 summarization model.
-
-  SummarizationWrapper class has input_specs compatible with the corresponding
-  dataset, but its init args are not supported by the front-end system, thus
-  we set up a layer of init args on top to work with the front-end.
-  """
-
-  def __init__(
-      self,
-      model_name_or_path="t5-small",
-      model=None,
-      tokenizer=None,
-      **config_kw,
-  ):
-    model = T5HFModel(model_name_or_path, model, tokenizer, **config_kw)
-    super().__init__(model)
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "model_name_or_path": lit_types.String(
-            default=_DEFAULT_MODEL_PATH,
-            required=False,
-        ),
-        **T5ModelConfig.init_spec(),
-    }
diff --git a/lit_nlp/examples/models/t5_int_test.py b/lit_nlp/examples/models/t5_int_test.py
deleted file mode 100644
index e801a174..00000000
--- a/lit_nlp/examples/models/t5_int_test.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Integration tests for lit_nlp.examples.models.t5."""
-
-from absl.testing import absltest
-from lit_nlp.examples.models import t5
-
-
-class T5IntTest(absltest.TestCase):
-  """Test that model can predict."""
-
-  def test_t5_predict(self):
-    # Run prediction to ensure no failure.
-    model_path = "https://storage.googleapis.com/what-if-tool-resources/lit-models/t5-small.tar.gz"
-    model = t5.T5HFModel(
-        model_path, num_to_generate=1, token_top_k=1, output_attention=False
-    )
-    model_in = [{"input_text": "test text"}]
-    model_out = list(model.predict(model_in))
-
-    # Sanity-check output vs output spec.
-    self.assertLen(model_out, 1)
-    for key in model.output_spec().keys():
-      self.assertIn(key, model_out[0].keys())
-
-if __name__ == "__main__":
-  absltest.main()
diff --git a/lit_nlp/examples/models/t5_test.py b/lit_nlp/examples/models/t5_test.py
deleted file mode 100644
index cd44871a..00000000
--- a/lit_nlp/examples/models/t5_test.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""Tests for lit_nlp.examples.models.t5."""
-
-from absl.testing import absltest
-from lit_nlp.examples.models import t5
-
-
-class T5ValidationTest(absltest.TestCase):
-  """Test that model classes conform to the expected spec."""
-
-  def test_t5hfmodel(self):
-    model = t5.T5HFModel("t5-small", model="dummy", tokenizer="dummy")
-    model = t5.validate_t5_model(model)  # uses asserts internally
-
-  def test_t5savedmodel(self):
-    model = t5.T5SavedModel("/dummy/path", model="dummy")
-    model = t5.validate_t5_model(model)  # uses asserts internally
-
-
-if __name__ == "__main__":
-  absltest.main()
diff --git a/lit_nlp/examples/t5_demo.py b/lit_nlp/examples/t5_demo.py
deleted file mode 100644
index c6dc1710..00000000
--- a/lit_nlp/examples/t5_demo.py
+++ /dev/null
@@ -1,245 +0,0 @@
-r"""Example demo loading a T5 model.
-
-To run locally with a small number of examples:
-  python -m lit_nlp.examples.t5_demo \
-      --alsologtostderr --port=5432 --max_examples=10 \
-      --nouse_indexer
-
-To run using the nearest-neighbor lookup index (warning, this will take a while
-to load):
-  python -m lit_nlp.examples.t5_demo \
-      --alsologtostderr --port=5432 --warm_start 1.0 \
-      --use_indexer --initialize_index --data_dir=/tmp/t5_index
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import app as lit_app
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.components import index
-from lit_nlp.components import similarity_searcher
-from lit_nlp.components import word_replacer
-from lit_nlp.examples.datasets import mt
-from lit_nlp.examples.datasets import summarization
-from lit_nlp.examples.models import t5
-from lit_nlp.lib import caching  # for hash id fn
-
-# NOTE: additional flags defined in server_flags.py
-
-_CNNDM_TRAIN_HOSTED = f"{summarization.CNNDMData.tfds_name}_train"
-_CNNDM_VALIDATION_HOSTED = f"{summarization.CNNDMData.tfds_name}_validation"
-_WMT14_DE_EN_HOSTED = f"{mt.WMT14Data.tfds_name}_de-en_validation"
-_WMT14_FR_EN_HOSTED = f"{mt.WMT14Data.tfds_name}_fr-en_validation"
-
-_CNNDM_TRAIN_HOSTED_URL = "https://storage.googleapis.com/what-if-tool-resources/lit-data/cnn_dailymail_train.csv"
-_CNNDM_VALIDATION_HOSTED_URL = "https://storage.googleapis.com/what-if-tool-resources/lit-data/cnn_dailymail_validation.csv"
-_WMT14_DE_EN_HOSTED_URL = "https://storage.googleapis.com/what-if-tool-resources/lit-data/wmt14_translate_de-en_validation.csv"
-_WMT14_FR_EN_HOSTED_URL = "https://storage.googleapis.com/what-if-tool-resources/lit-data/wmt14_translate_fr-en_validation.csv"
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples", 200,
-    "Maximum number of examples to load from the development set.")
-
-_MAX_INDEX_EXAMPLES = flags.DEFINE_integer(
-    "max_index_examples", 2000,
-    "Maximum number of examples to index from the train set.")
-
-_MODELS = flags.DEFINE_list(
-    "models",
-    [
-        "t5-small:https://storage.googleapis.com/what-if-tool-resources/lit-models/t5-small.tar.gz",
-    ],
-    "Which model(s) to load, as <name>:<path>.",
-)
-_TASKS = flags.DEFINE_list("tasks", ["summarization", "mt"],
-                           "Which task(s) to load.")
-
-_TOKEN_TOP_K = flags.DEFINE_integer(
-    "token_top_k", 10, "Rank to which the output distribution is pruned.")
-_NUM_TO_GEN = flags.DEFINE_integer(
-    "num_to_generate", 4, "Number of generations to produce for each input.")
-
-_HOSTED_DATASETS = flags.DEFINE_list(
-    "hosted_datasets",
-    [_WMT14_DE_EN_HOSTED, _WMT14_FR_EN_HOSTED],
-    "Datasets to use the mirror on storage.googleapis.com.",
-)
-
-##
-# Options for nearest-neighbor indexer.
-_USE_INDEXER = flags.DEFINE_boolean(
-    "use_indexer", False, "If true, will use the nearest neighbor index."
-)
-_INITIALIZE_INDEX = flags.DEFINE_boolean(
-    "initialize_index", True,
-    "If the flag is set, it builds the nearest neighbor index before starting "
-    "the server. If false, will look for one in --data_dir. No effect if "
-    "--use_indexer is False.")
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  FLAGS.set_default("data_dir", "./t5_data/")
-  FLAGS.set_default("initialize_index", False)
-  FLAGS.set_default("hosted_datasets", [
-      _CNNDM_TRAIN_HOSTED, _CNNDM_VALIDATION_HOSTED, _WMT14_DE_EN_HOSTED,
-      _WMT14_FR_EN_HOSTED
-  ])
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("t5_demo:get_wsgi_app() called with unused args: %s", unused)
-  return main([])
-
-
-def build_indexer(models):
-  """Build nearest-neighbor indices."""
-  assert FLAGS.data_dir, "--data_dir must be set to use the indexer."
-  # Datasets for indexer - this one loads the training corpus instead of val.
-  index_datasets = {}
-  if _CNNDM_TRAIN_HOSTED in _HOSTED_DATASETS.value:
-    index_datasets["CNNDM"] = summarization.CNNDMData(
-        filepath=_CNNDM_TRAIN_HOSTED_URL)
-  else:
-    index_datasets["CNNDM"] = summarization.CNNDMData(
-        split="train", max_examples=_MAX_INDEX_EXAMPLES.value)
-
-  index_datasets = lit_dataset.IndexedDataset.index_all(index_datasets,
-                                                        caching.input_hash)
-  # TODO(lit-dev): add training data and indexing for MT task. This will be
-  # easier after we remap the model specs, so it doesn't try to cross-index
-  # between the summarization model and the MT data.
-  index_models = {
-      k: m for k, m in models.items() if isinstance(m, t5.SummarizationWrapper)
-  }
-  # Set up the Indexer, building index if necessary (this may be slow).
-  return index.Indexer(
-      datasets=index_datasets,
-      models=index_models,
-      data_dir=FLAGS.data_dir,
-      initialize_new_indices=_INITIALIZE_INDEX.value)
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-  ##
-  # Load models. You can specify several here, if you want to compare different
-  # models side-by-side, and can also include models of different types that use
-  # different datasets.
-  base_models = {}
-  for model_string in _MODELS.value:
-    # Only split on the first ':', because path may be a URL
-    # containing 'https://'
-    model_name, path = model_string.split(":", 1)
-    if path.startswith("SavedModel"):
-      saved_model_path = path.split(":", 1)[1]
-      base_models[model_name] = t5.T5SavedModel(saved_model_path)
-    else:
-      # TODO(lit-dev): attention is temporarily disabled, because O(n^2) between
-      # tokens in a long document can get very, very large. Re-enable once we
-      # can send this to the frontend more efficiently.
-      base_models[model_name] = t5.T5HFModel(
-          model_name_or_path=path,
-          num_to_generate=_NUM_TO_GEN.value,
-          token_top_k=_TOKEN_TOP_K.value,
-          output_attention=False,
-      )
-
-  model_loaders: lit_app.ModelLoadersMap = {
-      "T5 summarization": (t5.T5Summarization, t5.T5Summarization.init_spec()),
-      "T5 translation": (t5.T5Translation, t5.T5Translation.init_spec()),
-  }
-
-  ##
-  # Load eval sets and model wrappers for each task.
-  # Model wrappers share the same in-memory T5 model, but add task-specific pre-
-  # and post-processing code.
-  models = {}
-  datasets = {}
-  dataset_loaders: lit_app.DatasetLoadersMap = {}
-  if "summarization" in _TASKS.value:
-    for k, m in base_models.items():
-      models[k + "_summarization"] = t5.SummarizationWrapper(m)
-    if _CNNDM_VALIDATION_HOSTED in _HOSTED_DATASETS.value:
-      datasets["CNNDM"] = summarization.CNNDMData(
-          filepath=_CNNDM_VALIDATION_HOSTED_URL)
-    else:
-      datasets["CNNDM"] = summarization.CNNDMData(
-          split="validation", max_examples=_MAX_EXAMPLES.value)
-
-    dataset_loaders["CNN DailyMail"] = (
-        summarization.CNNDMData,
-        summarization.CNNDMData.init_spec(),
-    )
-
-  if "mt" in _TASKS.value:
-    for k, m in base_models.items():
-      models[k + "_translation"] = t5.TranslationWrapper(m)
-
-    if _WMT14_DE_EN_HOSTED in _HOSTED_DATASETS.value:
-      datasets["wmt14_ende"] = mt.WMT14Data(
-          version="de-en", reverse=True, filepath=_WMT14_DE_EN_HOSTED_URL)
-    else:
-      datasets["wmt14_ende"] = mt.WMT14Data(version="de-en", reverse=True)
-
-    if _WMT14_FR_EN_HOSTED in _HOSTED_DATASETS.value:
-      datasets["wmt14_enfr"] = mt.WMT14Data(
-          version="fr-en", reverse=True, filepath=_WMT14_FR_EN_HOSTED_URL)
-    else:
-      datasets["wmt14_enfr"] = mt.WMT14Data(version="fr-en", reverse=True)
-
-    dataset_loaders["WMT 14"] = (mt.WMT14Data, mt.WMT14Data.init_spec())
-
-  # Truncate datasets if --max_examples is set.
-  for name in datasets:
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-    datasets[name] = datasets[name].slice[:_MAX_EXAMPLES.value]
-    logging.info("  truncated to %d examples", len(datasets[name]))
-
-  ##
-  # We can also add custom components. Generators are used to create new
-  # examples by perturbing or modifying existing ones.
-  generators = {
-      # Word-substitution, like "great" -> "terrible"
-      "word_replacer": word_replacer.WordReplacer(),
-  }
-
-  if _USE_INDEXER.value:
-    indexer = build_indexer(models)
-    # Wrap the indexer into a Generator component that we can query.
-    generators["similarity_searcher"] = similarity_searcher.SimilaritySearcher(
-        indexer=indexer)
-
-  ##
-  # Actually start the LIT server, using the models, datasets, and other
-  # components constructed above.
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      generators=generators,
-      model_loaders=model_loaders,
-      dataset_loaders=dataset_loaders,
-      **server_flags.get_flags(),
-  )
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index c9a959c0..39fafcb4 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -2,6 +2,8 @@
 
 <!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
 
+TODO(b/343678324): Remove the files after cl/629748253 is submitted.
+
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
 ## Framework and Model Support
@@ -44,10 +46,9 @@ every invocation.) Generally, you'll need to:
     directly.
 
 Alternatively, you can export to a `SavedModel` and load this in an eager mode
-runtime. This leads to much simpler code (see
-[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)),
-but may require changes to your `SavedModel` exporter in order to access model
-internals like embeddings, gradients, or attention.
+runtime. This leads to much simpler code, but may require changes to your
+`SavedModel` exporter in order to access model internals like embeddings,
+gradients, or attention.
 
 ### Remote or hosted models
 
@@ -175,8 +176,6 @@ or decoder.
     the model can also output a `ReferenceScores` field (with values as
     `list[float]`) with `parent=` set to reference a `TextSegment` or
     `ReferenceTexts` field from the input.
-*   For modeling examples, see
-    [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py)
 
 ![Generated Text Module](images/components/generation-results.png){w=600px align=center}
 
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 56aabbc6..89bb4acb 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -81,27 +81,6 @@ Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
 Generative AI Toolkit.
 
-### T5 <!-- DO NOT REMOVE {#t5 .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \
-**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py)
-
-*   Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
-    formats.
-*   Visualize beam candidates and highlight diffs against references.
-*   Visualize per-token decoder hypotheses to see where the model veers away
-    from desired output.
-*   Filter examples by ROUGE score against reference.
-*   Embeddings from last layer of model, visualized with UMAP or PCA.
-*   Task wrappers to handle pre- and post-processing for summarization and
-    machine translation tasks.
-*   Pre-loaded eval sets for CNNDM and WMT.
-
-Tip: check out a case study for this demo on the public LIT website:
-https://pair-code.github.io/lit/tutorials/generation
-
---------------------------------------------------------------------------------
-
 ## Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} -->
 
 ### BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} -->
diff --git a/website/src/tutorials/generation.md b/website/src/tutorials/generation.md
deleted file mode 100644
index b9680c98..00000000
--- a/website/src/tutorials/generation.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Debugging a Text Generator
-layout: layouts/tutorial.liquid
-
-hero-image: /assets/images/sample-banner.png
-hero-title: "Text Generation"
-hero-copy: "Learn about how we used LIT to debug a text generator."
-
-bc-anchor-category: "analysis"
-bc-category-title: "Analysis"
-bc-title: "Generator"
-
-time: "3 minutes"
-takeaways: "Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set."
----
-
-## Debugging a Text Generator
-
-{% include partials/link-out link: "../../demos/t5.html", text: "Explore this demo yourself." %}
-
-Or, run your own with [`examples/t5_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py)
-
-Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.
-
-
-{% include partials/inset-image image: '/assets/images/lit-t5.png', 
-  caption: 'Above: The datapoint of interest, with its inaccurate summarization.'%}
-
-We find the generated text contains an erroneous subject: "\[A\]lastair \[C\]ook was replaced as captain by former captain ...". In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.
-
-{% include partials/inset-image image: '/assets/images/lit-sim-search.png', 
-  caption: 'Above: Finding the most similar datapoints from the training set.'%}
-
-To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings
-from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.
-
-{% include partials/inset-image image: '/assets/images/lit-datatable-search.png', 
-  caption: 'Above: An example of the existence of "by former" in a similar datapoint from the training set.'%}

From 08289df0dd9927dee7147e5aad6e8b51bbe74f9e Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Fri, 7 Jun 2024 13:14:52 -0700
Subject: [PATCH 17/47] LIT: Improved packaging for prompt debugging code.

PiperOrigin-RevId: 641337890
---
 lit_nlp/examples/lm_salience_demo.py          | 334 ----------
 lit_nlp/examples/models/pretrained_lms.py     | 585 +-----------------
 .../models/pretrained_lms_int_test.py         |  35 --
 lit_nlp/examples/prompt_debugging/datasets.py |  81 +++
 .../keras_lms.py}                             |  32 +-
 lit_nlp/examples/prompt_debugging/layouts.py  |  64 ++
 lit_nlp/examples/prompt_debugging/models.py   | 109 ++++
 lit_nlp/examples/prompt_debugging/notebook.py |  63 ++
 lit_nlp/examples/prompt_debugging/server.py   | 174 ++++++
 .../prompt_debugging/transformers_lms.py      | 572 +++++++++++++++++
 .../transformers_lms_int_test.py              |  41 ++
 website/sphinx_src/components.md              |  10 +-
 website/sphinx_src/demos.md                   |   3 +-
 website/src/tutorials/sequence-salience.md    |  12 +-
 14 files changed, 1156 insertions(+), 959 deletions(-)
 delete mode 100644 lit_nlp/examples/lm_salience_demo.py
 create mode 100644 lit_nlp/examples/prompt_debugging/datasets.py
 rename lit_nlp/examples/{models/instrumented_keras_lms.py => prompt_debugging/keras_lms.py} (94%)
 create mode 100644 lit_nlp/examples/prompt_debugging/layouts.py
 create mode 100644 lit_nlp/examples/prompt_debugging/models.py
 create mode 100644 lit_nlp/examples/prompt_debugging/notebook.py
 create mode 100644 lit_nlp/examples/prompt_debugging/server.py
 create mode 100644 lit_nlp/examples/prompt_debugging/transformers_lms.py
 create mode 100644 lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py

diff --git a/lit_nlp/examples/lm_salience_demo.py b/lit_nlp/examples/lm_salience_demo.py
deleted file mode 100644
index fb555bec..00000000
--- a/lit_nlp/examples/lm_salience_demo.py
+++ /dev/null
@@ -1,334 +0,0 @@
-r"""Demo for sequence salience with a left-to-right language model.
-
-To use with the Gemma, Llama, or Mistral models, install the latest versions of
-Keras, KerasNLP, and/or HuggingFace Transformers:
-
-  pip install keras>=3.1.0 keras-nlp>=0.9.0 transformers>=4.38.0
-
-To run with the default configuration (Gemma on TensorFlow via Keras):
-
-  blaze run -c opt examples:lm_salience_demo -- \
-    --models=gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en \
-    --port=8890 --alsologtostderr
-
-MODELS:
-
-We strongly recommend a GPU or other accelerator to run this server with LLMs.
-The table below shows the model names and presets for common models. Use these
-to parameterize the --models flag with comma-separated `{model}:{preset}`
-strings, and remember the number of models loaded will be limited by the memory
-available on your accelerator.
-
-| Model   | dl_framework | dl_backend=tensorflow Preset | dl_backend=torch Preset              |
-| ------- | ------------ | ---------------------------- | ------------------------------------ |
-| Gemma   | kerasnlp     | gemma_1.1_instruct_7b_en     | gemma_1.1_instruct_7b_en             |
-| Gemma   | transformers | Unavailable                  | google/gemma-1.1-7b-it               |
-| Llama 2 | kerasnlp     | llama2_instruct_7b_en        | llama2_instruct_7b_en                |
-| Llama 2 | transformers | Unavailable                  | meta-llama/Llama-2-7b-hf             |
-| Mistral | kerasnlp     | mistral_instruct_7b_en       | mistral_instruct_7b_en               |
-| Mistral | transformers | Unavailable                  | mistralai/Mistral-7B-Instruct-v0.2   |
-
-Additional model presets can be found at the following locations, though
-compatibility with the LIT model wrappers is not guaranteed:
-
-* KerasNLP: https://keras.io/api/keras_nlp/models/
-* HuggingFace Transformers: https://huggingface.co/models
-
-DATASETS:
-
-By default this includes a small set of sample prompts. You can load your own
-examples using the --datasets flag or through the "Configure" menu in the UI.
-"""
-
-from collections.abc import Sequence
-import functools
-import os
-import re
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.examples.datasets import lm as lm_data
-from lit_nlp.lib import file_cache
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODELS = flags.DEFINE_list(
-    "models",
-    ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"],
-    "Models to load, as <name>:<path>. Path can be a URL, a local file path, or"
-    " the name of a preset for the configured Deep Learning framework (either"
-    " KerasNLP or HuggingFace Transformers; see --dl_framework for more). This"
-    " demo is tested with Gemma, GPT2, Llama, and Mistral on all supported"
-    " --dl_framework values. Other models should work, but adjustments might be"
-    " needed on their tokenizers (e.g., to define custom pad_token"
-    " when eos_token is not available to use as pad_token).",
-)
-
-_DATASETS = flags.DEFINE_list(
-    "datasets",
-    ["sample_prompts"],
-    "Datasets to load, as <name>:<path>. Format should be either .jsonl where"
-    " each record contains 'prompt' and optional 'target' and optional"
-    " 'source', or .txt with one prompt per line.",
-)
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples",
-    1000,
-    (
-        "Maximum number of examples to load from each evaluation set. Set to"
-        " None to load the full set."
-    ),
-)
-
-_BATCH_SIZE = flags.DEFINE_integer(
-    "batch_size", 4, "The number of examples to process per batch.",
-)
-
-_DL_BACKEND = flags.DEFINE_enum(
-    "dl_backend",
-    "tensorflow",
-    ["jax", "torch", "tensorflow"],
-    "The deep learning backend framework that the model runs on. All models"
-    " loaded by this server will use the same backend, incompatibilities will"
-    " result in errors.",
-)
-
-_DL_FRAMEWORK = flags.DEFINE_enum(
-    "dl_framework",
-    "kerasnlp",
-    ["kerasnlp", "transformers"],
-    "The deep learning framework that loads and runs the model on the backend."
-    " This server will attempt to load all models specified by the --models"
-    " flag with the configured framework, incompatibilities will result in"
-    " errors.",
-)
-
-_PRECISION = flags.DEFINE_enum(
-    "precision",
-    "bfloat16",
-    ["bfloat16", "float32"],
-    "Floating point precision for the models, only `bfloat16` and `float32` are"
-    " supported for now.",
-)
-
-
-# TODO(lit-dev): move these layouts to a separate .py file.
-# Custom frontend layout; see api/layout.py
-modules = layout.LitModuleName
-LEFT_RIGHT_LAYOUT = layout.LitCanonicalLayout(
-    left={
-        "Examples": [modules.DataTableModule],
-        "Editor": [modules.SingleDatapointEditorModule],
-    },
-    upper={  # if 'lower' not specified, this fills the right side
-        "Salience": [modules.SequenceSalienceModule],
-    },
-    layoutSettings=layout.LayoutSettings(leftWidth=40),
-    description="Left/right layout for language model salience.",
-)
-TOP_BOTTOM_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        "Examples": [modules.SimpleDataTableModule],
-        "Editor": [modules.SimpleDatapointEditorModule],
-    },
-    lower={
-        "Salience": [modules.SequenceSalienceModule],
-    },
-    layoutSettings=layout.LayoutSettings(
-        hideToolbar=True,
-        mainHeight=40,
-        centerPage=True,
-    ),
-    description="Simplified layout for language model salience.",
-)
-THREE_PANEL_LAYOUT = layout.LitCanonicalLayout(
-    left={
-        "Data Table": [modules.DataTableModule],
-        "Embeddings": [modules.EmbeddingsModule],
-    },
-    upper={
-        "Datapoint Editor": [modules.SingleDatapointEditorModule],
-        "Datapoint Generators": [modules.GeneratorModule],
-    },
-    lower={
-        "Salience": [modules.SequenceSalienceModule],
-        "Metrics": [modules.MetricsModule],
-    },
-    layoutSettings=layout.LayoutSettings(
-        mainHeight=40,
-        leftWidth=40,
-    ),
-    description="Custom layout for language model salience.",
-)
-
-CUSTOM_LAYOUTS = {
-    "left_right": LEFT_RIGHT_LAYOUT,
-    "top_bottom": TOP_BOTTOM_LAYOUT,
-    "three_panel": THREE_PANEL_LAYOUT,
-}
-
-FLAGS.set_default("page_title", "LM Salience Demo")
-FLAGS.set_default("default_layout", "left_right")
-
-_SPLASH_SCREEN_DOC = """
-# Language Model Salience
-
-To begin, select an example, then click the segment(s) (tokens, words, etc.)
-of the output that you would like to explain. Preceding segments(s) will be
-highlighted according to their importance to the selected target segment(s),
-with darker colors indicating a greater influence (salience) of that segment on
-the model's likelihood of the target segment.
-"""
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Return WSGI app for container-hosted demos."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("lm_demo:get_wsgi_app() called with unused args: %s", unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Set Keras backend and floating-point precision.
-  if _DL_FRAMEWORK.value == "kerasnlp":
-    # NOTE: Keras and KerasNLP require that certain environment variables are
-    # set before they are imported.
-    # TODO(b/327281789): Remove FORCE_KERAS_3 once Keras 3 is the default.
-    os.environ["FORCE_KERAS_3"] = "True"
-    os.environ["KERAS_BACKEND"] = _DL_BACKEND.value
-
-    # NOTE: Imported here and not at the top of the file to avoid
-    # initialization issues with the environment variables above. We should also
-    # import keras before any other Keras-related modules (e.g., KerasNLP or the
-    # LIT wrappers) to limit the potenital for improperly configured backends.
-    import keras  # pylint: disable=g-import-not-at-top
-
-    keras.config.set_floatx(_PRECISION.value)
-  elif _DL_BACKEND.value == "torch":
-    # NOTE: Keras sets precision for all backends with set_floatx(), but for
-    # HuggingFace Transformers with PyTorch we need to set it explicitly.
-    import torch  # pylint: disable=g-import-not-at-top # pytype: disable=import-error
-
-    torch.set_default_dtype(
-        torch.bfloat16 if _PRECISION.value == "bfloat16" else torch.float32
-    )
-
-  plaintextPrompts = functools.partial(  # pylint: disable=invalid-name
-      lm_data.PlaintextSents, field_name="prompt"
-  )
-  # Hack: normally dataset loaders are a class object which has a __name__,
-  # rather than a functools.partial
-  plaintextPrompts.__name__ = "PlaintextSents"
-
-  # Pre-loaded datasets.
-  datasets = {}
-  for dataset_string in _DATASETS.value:
-    if dataset_string == "sample_prompts":
-      dataset_name = "sample_prompts"
-      path = lm_data.PromptExamples.SAMPLE_DATA_PATH
-    else:
-      # Only split on the first ':', because path may be a URL
-      # containing 'https://'
-      dataset_name, path = dataset_string.split(":", 1)
-    logging.info("Loading dataset '%s' from '%s'", dataset_name, path)
-
-    if path.endswith(".jsonl"):
-      datasets[dataset_name] = lm_data.PromptExamples(path)
-    # .txt or .txt-#####-of-#####
-    elif path.endswith(".txt") or re.match(r".*\.txt-\d{5}-of-\d{5}$", path):
-      datasets[dataset_name] = plaintextPrompts(path)
-    else:
-      raise ValueError(f"Unsupported dataset format for {dataset_string}")
-
-  # For loading from the UI.
-  dataset_loaders = {
-      "jsonl_examples": (
-          lm_data.PromptExamples,
-          lm_data.PromptExamples.init_spec(),
-      ),
-      "plaintext_inputs": (
-          plaintextPrompts,
-          lm_data.PlaintextSents.init_spec(),
-      ),
-  }
-
-  ##
-  # Load models, according to the --models flag.
-  models = {}
-  for model_string in _MODELS.value:
-    # Only split on the first ':' as path may be a URL containing 'https://'
-    model_name, path = model_string.split(":", 1)
-    logging.info("Loading model '%s' from '%s'", model_name, path)
-
-    # Limit scope of caching to archive files and remote paths, as some preset
-    # names like "google/gemma-1.1-7b-it" look like file paths but should not
-    # be handled as such.
-    if path.endswith(".tar.gz") or file_cache.is_remote(path):
-      path = file_cache.cached_path(
-          path,
-          extract_compressed_file=path.endswith(".tar.gz"),
-      )
-
-    if _DL_FRAMEWORK.value == "kerasnlp":
-      # pylint: disable=g-import-not-at-top
-      from keras_nlp import models as keras_models
-      from lit_nlp.examples.models import instrumented_keras_lms as lit_keras
-      # pylint: enable=g-import-not-at-top
-      # Load the weights once for the underlying Keras model.
-      model = keras_models.CausalLM.from_preset(path)
-      models |= lit_keras.initialize_model_group_for_salience(
-          model_name, model, max_length=512, batch_size=_BATCH_SIZE.value
-      )
-      # Disable embeddings from the generation model.
-      # TODO(lit-dev): re-enable embeddings if we can figure out why UMAP was
-      # crashing? Maybe need n > 2 examples.
-      models[model_name].output_embeddings = False
-    else:
-      # NOTE: (Style Deviation) Imported here to limit uncessary imports.
-      from lit_nlp.examples.models import pretrained_lms  # pylint: disable=g-import-not-at-top
-      # Assuming a valid decoder model name supported by
-      # `transformers.AutoModelForCausalLM` is provided to "path".
-      models |= pretrained_lms.initialize_model_group_for_salience(
-          model_name,
-          path,
-          batch_size=_BATCH_SIZE.value,
-          framework=_DL_BACKEND.value,
-          max_new_tokens=512,
-      )
-
-  for name in datasets:
-    datasets[name] = datasets[name].slice[: _MAX_EXAMPLES.value]
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      layouts=CUSTOM_LAYOUTS,
-      dataset_loaders=dataset_loaders,
-      onboard_start_doc=_SPLASH_SCREEN_DOC,
-      **server_flags.get_flags(),
-  )
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/models/pretrained_lms.py b/lit_nlp/examples/models/pretrained_lms.py
index 56d3e1a9..f9194e11 100644
--- a/lit_nlp/examples/models/pretrained_lms.py
+++ b/lit_nlp/examples/models/pretrained_lms.py
@@ -6,42 +6,18 @@
 functions to predict a batch of examples and extract information such as
 hidden states and attention.
 """
-from collections.abc import Sequence
-import enum
-import functools
+
 import re
 
-from absl import logging
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
 from lit_nlp.examples.models import model_utils
 from lit_nlp.lib import file_cache
 from lit_nlp.lib import utils
 import numpy as np
+import tensorflow as tf
 import transformers
 
-# pylint: disable=g-import-not-at-top
-# pytype: disable=import-error
-try:
-  import tensorflow as tf
-except (ModuleNotFoundError, ImportError):
-  logging.warning("TensorFlow is not available.")
-
-try:
-  import torch
-except (ModuleNotFoundError, ImportError):
-  logging.warning("PyTorch is not available.")
-# pytype: enable=import-error
-# pylint: enable=g-import-not-at-top
-
-
-_DEFAULT_MAX_LENGTH = 1024
-_PYTORCH = "torch"
-_TENSORFLOW = "tensorflow"
-# HuggingFace uses two letter abbreviations for pytorch and tensorflow.
-_HF_PYTORCH = "pt"
-_HF_TENSORFLOW = "tf"
-
 
 class BertMLM(lit_model.BatchedModel):
   """BERT masked LM using Huggingface Transformers and TensorFlow 2."""
@@ -92,7 +68,7 @@ def _get_topk_tokens(
     # Find the vocab indices of top k predictions, at each token.
     # np.argpartition is faster than a full argsort for k << V,
     # but we need to sort the output after slicing (see below).
-    index_array = np.argpartition(scores, -self.top_k, axis=1)[:, -self.top_k:]
+    index_array = np.argpartition(scores, -self.top_k, axis=1)[:, -self.top_k :]
     # These are each [num_tokens, tok_k]
     top_tokens = [
         self.tokenizer.convert_ids_to_tokens(idxs) for idxs in index_array
@@ -112,7 +88,8 @@ def _postprocess(self, output: dict[str, np.ndarray]):
     # Slice to remove padding, omitting initial [CLS] and final [SEP]
     slicer = slice(1, output.pop("ntok") - 1)
     output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-        output.pop("input_ids")[slicer])
+        output.pop("input_ids")[slicer]
+    )
     probas = output.pop("probas")
 
     # Predictions at every position, regardless of masking.
@@ -134,14 +111,16 @@ def predict_minibatch(self, inputs):
         ex.get("tokens") or self.tokenizer.tokenize(ex["text"]) for ex in inputs
     ]
     encoded_input = model_utils.batch_encode_pretokenized(
-        self.tokenizer, tokenized_texts)
+        self.tokenizer, tokenized_texts
+    )
 
     # out.logits is a single tensor
     #    <float32>[batch_size, num_tokens, vocab_size]
     # out.hidden_states is a list of num_layers + 1 tensors, each
     #    <float32>[batch_size, num_tokens, h_dim]
-    out: transformers.modeling_tf_outputs.TFMaskedLMOutput = \
-        self.model(encoded_input)
+    out: transformers.modeling_tf_outputs.TFMaskedLMOutput = self.model(
+        encoded_input
+    )
     batched_outputs = {
         "probas": tf.nn.softmax(out.logits, axis=-1).numpy(),
         "input_ids": encoded_input["input_ids"].numpy(),
@@ -256,8 +235,9 @@ def _pred(self, encoded_inputs):
     Returns:
       payload: Dictionary with items described above, each as single Tensor.
     """
-    out: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast = \
-        self.model(encoded_inputs["input_ids"])
+    out: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast = self.model(
+        encoded_inputs["input_ids"]
+    )
 
     model_probs = tf.nn.softmax(out.logits, axis=-1)
     top_k = tf.math.top_k(model_probs, k=self.top_k, sorted=True, name=None)
@@ -273,7 +253,8 @@ def _pred(self, encoded_inputs):
       batched_outputs[f"layer_{i+1:d}_attention"] = out.attentions[i]
     for i in range(len(out.hidden_states)):
       batched_outputs[f"layer_{i:d}_avg_embedding"] = tf.math.reduce_mean(
-          out.hidden_states[i], axis=1)
+          out.hidden_states[i], axis=1
+      )
 
     return batched_outputs
 
@@ -286,7 +267,7 @@ def _postprocess(self, preds):
     # Decode predicted top-k tokens.
     # token_topk_preds will be a list[list[(word, prob)]]
     # Initialize prediction for 0th token as N/A.
-    token_topk_preds = [[("N/A", 1.)]]
+    token_topk_preds = [[("N/A", 1.0)]]
     pred_ids = preds.pop("top_k_indices")[:ntok]  # <int>[num_tokens, k]
     pred_probs = preds.pop("top_k_probs")[:ntok]  # <float32>[num_tokens, k]
     for token_pred_ids, token_pred_probs in zip(pred_ids, pred_probs):
@@ -348,537 +329,7 @@ def output_spec(self):
     # Add attention and embeddings from each layer.
     for i in range(self.num_layers):
       spec[f"layer_{i+1:d}_attention"] = lit_types.AttentionHeads(
-          align_in="tokens", align_out="tokens")
+          align_in="tokens", align_out="tokens"
+      )
       spec[f"layer_{i:d}_avg_embedding"] = lit_types.Embeddings()
     return spec
-
-
-@enum.unique
-class MLFramework(enum.Enum):
-  """The supported deep learning frameworks."""
-
-  PT = _PYTORCH
-  TF = _TENSORFLOW
-
-
-class HFBaseModel(lit_model.BatchedModel):
-  """Base class for HF generative, salience, tokenizer model wrappers."""
-
-  # Enum str values for entries in MLFramework, used for init_spec and logging.
-  _ML_FRAMEWORK_VALUES = [framework.value for framework in MLFramework]
-
-  @property
-  def num_layers(self):
-    return self.model.config.n_layer
-
-  @classmethod
-  def init_spec(cls) -> lit_model.Spec:
-    return {
-        "model_name_or_path": lit_types.String(default="gpt2"),
-        "batch_size": lit_types.Integer(default=6, min_val=1, max_val=64),
-        "framework": lit_types.CategoryLabel(vocab=cls._ML_FRAMEWORK_VALUES),
-    }
-
-  def __init__(
-      self,
-      model_name_or_path="gpt2",
-      batch_size=6,
-      framework=_PYTORCH,
-      model=None,
-      tokenizer=None,
-  ):
-    """Constructor for HF base model wrappers.
-
-    Note: args "model" and "tokenizer" take priority if both are specified.
-    Otherwise, "model_name_or_path" is used to initialize the model and
-    tokenizer.
-
-    This class supports common HF transformer models such as GPT2, Llama,
-    Mistral, etc.
-
-    Args:
-      model_name_or_path: gpt2, gpt2-medium, gpt2-large, distilgpt2,
-        meta-llama/Llama-2-7b-hf, mistralai/Mistral-7B-v0.1, etc.
-      batch_size: the number of items to process per `predict_minibatch` call.
-      framework: the deep learning framework, only "tensorflow" and "torch"
-        are supported.
-      model: an initialized transformer model.
-      tokenizer: an initialized tokenizer.
-    """
-    super().__init__()
-
-    if model is not None and tokenizer is not None:
-      self.model = model
-      self.tokenizer = tokenizer
-      # Check if the HF model object's framework is supported here.
-      if model.framework == _HF_PYTORCH:
-        self.framework = MLFramework.PT
-      elif model.framework == _HF_TENSORFLOW:
-        self.framework = MLFramework.TF
-      else:
-        raise ValueError(
-            f"The HuggingFace model framework `{model.framework}` is not"
-            " supported."
-        )
-    else:
-      # Normally path is a directory; if it's an archive file, download and
-      # extract to the transformers cache.
-      if model_name_or_path.endswith(".tar.gz"):
-        model_name_or_path = file_cache.cached_path(
-            model_name_or_path, extract_compressed_file=True
-        )
-
-      # Note: we need to left-pad for generation to work properly.
-      # Other modes such as scoring and salience should handle this as well;
-      # see example in HFSalienceModel._postprocess().
-      self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-          model_name_or_path,
-          use_fast=False,
-          padding_side="left",
-          model_max_length=_DEFAULT_MAX_LENGTH,
-      )
-      # Set this after init, as if pad_token= is passed to
-      # AutoTokenizer.from_pretrained() above it will create a new token with
-      # with id = max_vocab_length and cause out-of-bounds errors in
-      # the embedding lookup.
-      if framework == _PYTORCH:
-        auto_model = transformers.AutoModelForCausalLM
-        self.framework = MLFramework.PT
-      elif framework == _TENSORFLOW:
-        auto_model = transformers.TFAutoModelForCausalLM
-        self.framework = MLFramework.TF
-      else:
-        raise ValueError(
-            f"The provided value `{framework}` for arg `framework` is not"
-            f" supported, please choose from {self._ML_FRAMEWORK_VALUES}."
-        )
-      self.model = auto_model.from_pretrained(
-          model_name_or_path,
-          output_hidden_states=True,
-          output_attentions=False,
-      )
-    if self.framework == MLFramework.PT:
-      self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
-      self.model = self.model.to(self.device)
-    self.embedding_table = self.model.get_input_embeddings()
-    self.tokenizer.pad_token = self.tokenizer.eos_token
-    self.batch_size = batch_size
-
-  @property
-  def pad_left(self):
-    return self.tokenizer.padding_side == "left"
-
-  @classmethod
-  def from_loaded(cls, existing: "HFBaseModel", *args, **kw):
-    """Share weights and underlying HF model with another instance."""
-    return cls(model=existing.model, tokenizer=existing.tokenizer, *args, **kw)
-
-  def clean_subword_token(self, tok):
-    # For GPT2 tokenizer.
-    tok = tok.replace("Ċ", "\n")  # newlines
-    tok = tok.replace("Ġ", "▁")  # start of word -> magic underscore
-    # For SentencePiece Tokenizer.
-    tok = tok.replace("<0x0A>", "\n")  # newlines
-    return tok
-
-  def ids_to_clean_tokens(self, ids: Sequence[int]) -> list[str]:
-    tokens = self.tokenizer.convert_ids_to_tokens(ids)
-    return [self.clean_subword_token(t) for t in tokens]
-
-  def max_minibatch_size(self) -> int:
-    # The BatchedModel base class handles batching automatically in the
-    # implementation of predict(), and uses this value as the batch size.
-    return self.batch_size
-
-  def input_spec(self):
-    return {
-        "prompt": lit_types.TextSegment(),
-        "target": lit_types.TextSegment(required=False),
-    }
-
-
-class HFGenerativeModel(HFBaseModel):
-  """Wrapper for a HF Transformer model that generates texts.
-
-  This class loads a tokenizer and model using the Huggingface library and
-  provides the LIT-required functions to generate text responses given input
-  prompts.
-
-  Note that the default model generation config is used such that the response
-  is produced using multinomial sampling.
-  """
-
-  @classmethod
-  def init_spec(cls) -> lit_model.Spec:
-    return super().init_spec() | {
-        "max_new_tokens": lit_types.Integer(default=50, min_val=1, max_val=500)
-    }
-
-  def __init__(self, *args, max_new_tokens=50, **kw):
-    """Constructor for HFGenerativeModel.
-
-    Args:
-      *args: as to HFBaseModel.__init__
-      max_new_tokens: the maximum number of new tokens to generate.
-      **kw: as to HFBaseModel.__init__
-    """
-    super().__init__(*args, **kw)
-    self.max_new_tokens = max_new_tokens
-
-  def _postprocess(self, preds):
-    """Post-process single-example preds. Operates on numpy arrays."""
-    # TODO(b/324957491): return actual decoder scores for each generation.
-    # GeneratedTextCandidates should be a list[(text, score)]
-    preds["response"] = [(preds["response"], 1.0)]
-    ntok_in = preds.pop("ntok_in")
-    embs = preds.pop("embs")
-    # Mean-pool over input tokens.
-    preds["prompt_embeddings"] = np.mean(
-        embs[-(self.max_new_tokens + ntok_in) : -self.max_new_tokens], axis=0
-    )
-    # Mean-pool over output (generated) tokens.
-    # TODO(b/324957491): slice this to only "real" output tokens,
-    # if generation length < max generation length.
-    preds["response_embeddings"] = np.mean(embs[-self.max_new_tokens :], axis=0)
-
-    return preds
-
-  ##
-  # LIT API implementations
-  def predict_minibatch(self, inputs):
-    prompts = [ex["prompt"] for ex in inputs]
-    encoded_inputs = self.tokenizer(
-        prompts,
-        return_tensors=_HF_PYTORCH
-        if self.framework == MLFramework.PT
-        else _HF_TENSORFLOW,
-        add_special_tokens=True,
-        padding="longest",
-        truncation="longest_first",
-    )
-    if self.framework == MLFramework.PT:
-      encoded_inputs = encoded_inputs.to(self.device)
-
-    outputs = self.model.generate(
-        encoded_inputs["input_ids"],
-        attention_mask=encoded_inputs["attention_mask"],
-        max_new_tokens=self.max_new_tokens,
-    )
-
-    responses = self.tokenizer.batch_decode(
-        outputs[:, -self.max_new_tokens :], skip_special_tokens=True
-    )
-
-    if self.framework == MLFramework.PT:
-      with torch.no_grad():
-        # Input embeddings: <float>[batch_size, num_tokens, emb_dim]
-        embeddings = self.embedding_table(outputs)
-
-      batched_outputs = {
-          "embs": embeddings.cpu().to(torch.float),
-          "ntok_in": (
-              torch.sum(encoded_inputs["attention_mask"], axis=1)
-              .cpu()
-              .to(torch.int)
-          ),
-      }
-    else:
-      embeddings = self.embedding_table(outputs)
-      batched_outputs = {
-          "embs": embeddings,
-          "ntok_in": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1),
-          # TODO(b/324957491): compute ntok_out if < max_output_tokens ?
-      }
-
-    # Convert to numpy for post-processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    detached_outputs["response"] = responses
-    # Split up batched outputs, then post-process each example.
-    unbatched_outputs = utils.unbatch_preds(detached_outputs)
-    return map(self._postprocess, unbatched_outputs)
-
-  def output_spec(self) -> lit_types.Spec:
-    return {
-        "response": lit_types.GeneratedTextCandidates(parent="target"),
-        "prompt_embeddings": lit_types.Embeddings(required=False),
-        "response_embeddings": lit_types.Embeddings(required=False),
-    }
-
-
-class HFSalienceModel(HFBaseModel):
-  """Wrapper for a HF Transformer model that computes input (token) salience."""
-
-  def _left_pad_target_masks(self, seq_length, target_masks):
-    """Pads target masks (from left) to the desired sequence length.
-
-    Args:
-      seq_length: desired length of the padded masks.
-      target_masks: list(array_like) of binary (0/1) masks for each input.
-
-    Returns:
-      Numpy array of the padded masks at the desired sequence length.
-    """
-    # It doesn't make sense to interpret the first token, since it is not ever
-    # predicted. But we need to ensure that the mask[0] is zero, so it doesn't
-    # cause problems when 'rolled' to the last position below.
-    modified_masks = [[0] + list(mask[1:]) for mask in target_masks]
-    pad_fn = functools.partial(
-        utils.pad1d,
-        min_len=seq_length,
-        max_len=seq_length,
-        pad_val=0,
-        pad_left=self.pad_left,
-    )
-    padded_target_masks = np.stack(
-        [pad_fn(mask) for mask in modified_masks],
-        axis=0,
-    )
-    return padded_target_masks
-
-  def _pred_tf(self, encoded_inputs, target_masks):
-    """Predicts one batch of tokenized text using TF.
-
-    Also performs some batch-level post-processing in TF.
-    Single-example postprocessing is done in _postprocess(), and operates on
-    numpy arrays.
-
-    Args:
-      encoded_inputs: output of self.tokenizer()
-      target_masks: list(array_like) of binary (0/1) masks for each input
-
-    Returns:
-      payload: Dictionary with items described above, each as single Tensor.
-    """
-    input_ids = encoded_inputs["input_ids"]
-
-    # <tf.int32>[batch_size, num_tokens]; ignore the last one in each row.
-    target_ids = tf.roll(input_ids, shift=-1, axis=1)
-    ##
-    # Process target masks
-    padded_target_masks = tf.constant(
-        self._left_pad_target_masks(target_ids.shape[1], target_masks),
-        dtype=tf.bool,
-    )
-    # Shift masks back so they align with target_ids.
-    loss_mask = tf.roll(padded_target_masks, shift=-1, axis=1)
-
-    with tf.GradientTape(watch_accessed_variables=False) as tape:
-      # We need to run the embedding layer ourselves so we can trace it.
-      # See here for how the model normally does this:
-      # https://github.com/huggingface/transformers/blob/v4.29.2/src/transformers/models/gpt2/modeling_tf_gpt2.py#L450
-      embs = self.embedding_table(input_ids)
-      tape.watch(embs)
-
-      out = self.model(
-          input_ids=None,
-          inputs_embeds=embs,
-          attention_mask=encoded_inputs["attention_mask"],
-      )
-
-      loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
-          from_logits=True, reduction="none"
-      )
-      # <tf.float>[batch_size, num_tokens]
-      per_token_loss = loss_fn(target_ids, out.logits)
-      masked_loss = per_token_loss * tf.cast(loss_mask, per_token_loss.dtype)
-
-    grads = tape.gradient(
-        masked_loss, embs
-    )  # <tf.float>[batch_size, num_tokens, hdim]
-
-    grad_l2 = tf.norm(grads, axis=2)  # <tf.float>[batch_size, num_tokens]
-    grad_dot_input = tf.reduce_sum(
-        grads * embs, axis=2
-    )  # <tf.float>[batch_size, num_tokens]
-
-    batched_outputs = {
-        "input_ids": input_ids,
-        "attention_mask": encoded_inputs["attention_mask"],
-        # Gradients are already aligned to input tokens.
-        "grad_l2": grad_l2,
-        "grad_dot_input": grad_dot_input,
-        # Shift token loss to align with (input) tokens.
-        # "token_loss": tf.roll(per_token_loss, shift=1, axis=1),
-    }
-
-    return batched_outputs
-
-  def _pred_pt(self, encoded_inputs, target_masks):
-    """Predicts one batch of tokenized text using PyTorch.
-
-    Also performs some batch-level post-processing in PyTorch.
-    Single-example postprocessing is done in _postprocess(), and operates on
-    numpy arrays.
-
-    Args:
-      encoded_inputs: output of self.tokenizer()
-      target_masks: list(array_like) of binary (0/1) masks for each input
-
-    Returns:
-      payload: Dictionary with items described above, each as single Tensor.
-    """
-    encoded_inputs = encoded_inputs.to(self.device)
-    input_ids = encoded_inputs["input_ids"]
-    attention_mask = encoded_inputs["attention_mask"]
-
-    # [batch_size, num_tokens]; ignore the last one in each row.
-    target_ids = torch.roll(input_ids, shifts=-1, dims=1).to(self.device)
-    ##
-    # Process target masks
-    padded_target_masks = torch.tensor(
-        self._left_pad_target_masks(target_ids.shape[1], target_masks)
-    ).bool()
-    loss_mask = torch.roll(padded_target_masks, shifts=-1, dims=1).to(
-        self.device
-    )
-
-    embs = self.embedding_table(input_ids)
-    outs = self.model(
-        input_ids=None,
-        inputs_embeds=embs,
-        attention_mask=attention_mask,
-    )
-    loss_func = torch.nn.CrossEntropyLoss(reduction="none")
-    # Need to reshape outs.logits from [batch_size, num_tokens, vocab_size]
-    # to [batch_size, vocab_size, num_tokens] so the last dimension matches that
-    # of target_ids with dimension [batch_size, num_tokens].
-    per_token_loss = loss_func(outs.logits.permute(0, 2, 1), target_ids)
-    masked_loss = per_token_loss * loss_mask
-
-    # returned gradients are wrapped in a single item tuple.
-    grads = torch.autograd.grad(
-        masked_loss, embs, grad_outputs=torch.ones_like(masked_loss)
-    )[0]
-
-    # Remove the grad function from embs.
-    embs = embs.detach()
-    grad_l2 = torch.norm(grads, dim=2)  # [batch_size, num_tokens]
-    grad_dot_input = torch.sum(grads * embs, axis=2)  # [batch_size, num_tokens]
-
-    batched_outputs = {
-        "input_ids": input_ids.cpu().to(torch.int),
-        "attention_mask": attention_mask.cpu().to(torch.int),
-        # Gradients are already aligned to input tokens.
-        "grad_l2": grad_l2.cpu().to(torch.float),
-        "grad_dot_input": grad_dot_input.cpu().to(torch.float),
-        # Shift token loss to align with (input) tokens.
-        # "token_loss": torch.roll(per_token_loss, shifts=1, dims=1),
-    }
-
-    return batched_outputs
-
-  def _postprocess(self, preds):
-    """Post-process single-example preds. Operates on numpy arrays."""
-    # Be sure to cast to bool, otherwise this will select integer positions 0, 1
-    # rather than acting as a boolean mask.
-    mask = preds.pop("attention_mask").astype(bool)
-    ids = preds.pop("input_ids")[mask]
-    preds["tokens"] = self.ids_to_clean_tokens(ids)
-    for key in utils.find_spec_keys(self.output_spec(), lit_types.TokenScores):
-      preds[key] = preds[key][mask]
-    # First token (usually <s>) is not actually predicted, so return 0 for loss.
-    # preds["token_loss"][0] = 0
-
-    return preds
-
-  # LIT API implementations
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    # Preprocess inputs.
-    texts = [ex["prompt"] + ex.get("target", "") for ex in inputs]
-    encoded_inputs = self.tokenizer(
-        texts,
-        return_tensors=_HF_PYTORCH
-        if self.framework == MLFramework.PT
-        else _HF_TENSORFLOW,
-        add_special_tokens=True,
-        padding="longest",
-        truncation="longest_first",
-    )
-    target_masks = [ex.get("target_mask", []) for ex in inputs]
-
-    # Get the predictions.
-    if self.framework == MLFramework.PT:
-      batched_outputs = self._pred_pt(encoded_inputs, target_masks)
-    else:
-      batched_outputs = self._pred_tf(encoded_inputs, target_masks)
-
-    # Convert to numpy for post-processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    # Split up batched outputs, then post-process each example.
-    unbatched_outputs = utils.unbatch_preds(detached_outputs)
-    return map(self._postprocess, unbatched_outputs)
-
-  def input_spec(self):
-    return super().input_spec() | {
-        "target_mask": lit_types.TokenScores(align="", required=False),
-    }
-
-  def output_spec(self) -> lit_types.Spec:
-    return {
-        "tokens": lit_types.Tokens(parent=""),  # all tokens
-        "grad_l2": lit_types.TokenScores(align="tokens"),
-        "grad_dot_input": lit_types.TokenScores(align="tokens"),
-        # "token_loss": lit_types.TokenScores(align="tokens"),
-    }
-
-
-class HFTokenizerModel(HFBaseModel):
-  """Wrapper to run only the tokenizer.
-
-  Should exactly match tokens from HFSalienceModel.
-  """
-
-  def _postprocess(self, preds):
-    """Post-process single-example preds. Operates on numpy arrays."""
-    # Be sure to cast to bool, otherwise this will select intger positions 0, 1
-    # rather than acting as a boolean mask.
-    mask = preds.pop("attention_mask").astype(bool)
-    ids = preds.pop("input_ids")[mask]
-    preds["tokens"] = self.ids_to_clean_tokens(ids)
-    return preds
-
-  # LIT API implementations
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    # Preprocess inputs.
-    texts = [ex["prompt"] + ex.get("target", "") for ex in inputs]
-    encoded_inputs = self.tokenizer(
-        texts,
-        return_tensors=_HF_PYTORCH
-        if self.framework == MLFramework.PT
-        else _HF_TENSORFLOW,
-        add_special_tokens=True,
-        padding="longest",
-        truncation="longest_first",
-    )
-    batched_outputs = {
-        "input_ids": encoded_inputs["input_ids"],
-        "attention_mask": encoded_inputs["attention_mask"],
-    }
-    # Convert to numpy for post-processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    # Split up batched outputs, then post-process each example.
-    unbatched_outputs = utils.unbatch_preds(detached_outputs)
-    return map(self._postprocess, unbatched_outputs)
-
-  def output_spec(self) -> lit_types.Spec:
-    return {
-        "tokens": lit_types.Tokens(parent=""),  # all tokens
-    }
-
-
-def initialize_model_group_for_salience(
-    name, *args, max_new_tokens=512, **kw
-) -> dict[str, lit_model.Model]:
-  """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'."""
-  generation_model = HFGenerativeModel(
-      *args, **kw, max_new_tokens=max_new_tokens
-  )
-  salience_model = HFSalienceModel.from_loaded(generation_model)
-  tokenizer_model = HFTokenizerModel.from_loaded(generation_model)
-  return {
-      name: generation_model,
-      f"_{name}_salience": salience_model,
-      f"_{name}_tokenizer": tokenizer_model,
-  }
diff --git a/lit_nlp/examples/models/pretrained_lms_int_test.py b/lit_nlp/examples/models/pretrained_lms_int_test.py
index 229be85b..187a335c 100644
--- a/lit_nlp/examples/models/pretrained_lms_int_test.py
+++ b/lit_nlp/examples/models/pretrained_lms_int_test.py
@@ -1,7 +1,6 @@
 """Integration tests for pretrained_lms."""
 
 from absl.testing import absltest
-from absl.testing import parameterized
 from lit_nlp.examples.models import pretrained_lms
 
 
@@ -33,39 +32,5 @@ def test_gpt2(self):
       self.assertIn(key, model_out[0].keys())
 
 
-class GPT2Generation(parameterized.TestCase):
-  """Test that model classes can predict."""
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name="tensorflow",
-          framework=pretrained_lms.MLFramework.TF.value,
-          model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz",
-      ),
-      dict(
-          testcase_name="pytorch",
-          framework=pretrained_lms.MLFramework.PT.value,
-          model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2-pt.tar.gz",
-      ),
-  )
-  def test_gpt2_generation(self, framework, model_path):
-    model = pretrained_lms.HFGenerativeModel(
-        model_name_or_path=model_path, framework=framework
-    )
-    model_in = [{"prompt": "Today is"}, {"prompt": "What is the color of"}]
-    model_out = list(model.predict(model_in))
-
-    # Sanity-check output vs output spec.
-    self.assertLen(model_out, 2)
-    for key in model.output_spec().keys():
-      self.assertIn(key, model_out[0].keys())
-
-    # Check that the embedding dimension is the same for prompt and response.
-    self.assertEqual(
-        model_out[0]["prompt_embeddings"].shape,
-        model_out[0]["response_embeddings"].shape,
-    )
-
-
 if __name__ == "__main__":
   absltest.main()
diff --git a/lit_nlp/examples/prompt_debugging/datasets.py b/lit_nlp/examples/prompt_debugging/datasets.py
new file mode 100644
index 00000000..4681c951
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/datasets.py
@@ -0,0 +1,81 @@
+"""Methods for configuring prompt debugging datasets."""
+
+from collections.abc import Mapping, Sequence
+import functools
+import re
+from typing import Optional
+
+from absl import logging
+from lit_nlp import app as lit_app
+from lit_nlp.api import dataset as lit_dataset
+from lit_nlp.examples.datasets import lm as lm_data
+
+
+DEFAULT_DATASETS = ["sample_prompts"]
+DEFAULT_MAX_EXAMPLES = 1000
+
+_plaintext_prompts = functools.partial(  # pylint: disable=invalid-name
+    lm_data.PlaintextSents, field_name="prompt"
+)
+# Hack: normally dataset loaders are a class object which has a __name__,
+# rather than a functools.partial
+_plaintext_prompts.__name__ = "PlaintextSents"
+
+
+def get_datasets(
+    datasets_config: Optional[Sequence[str]] = None,
+    max_examples: int = DEFAULT_MAX_EXAMPLES,
+) -> Mapping[str, lit_dataset.Dataset]:
+  """Loads datasets from the provided configs.
+
+  Args:
+    datasets_config: A sequence of configs in the form of <name>:<path> where
+      the path points to is either: 1) a JSON Lines file containing records with
+      a required "prompt" field and optional "target" and "source" fields; or 2)
+      a plain text file where each line is a prompt.
+    max_examples: Maximum number of examples in each loaded dataset.
+
+  Returns:
+    A mapping from dataset name to the initialized LIT dataset.
+  """
+
+  if not datasets_config:
+    return {}
+
+  datasets: dict[str, lit_dataset.Dataset] = {}
+  for dataset_string in datasets_config:
+    if dataset_string == "sample_prompts":
+      dataset_name = "sample_prompts"
+      path = lm_data.PromptExamples.SAMPLE_DATA_PATH
+    else:
+      # Only split on the first ':', because path may be a URL
+      # containing 'https://'
+      dataset_name, path = dataset_string.split(":", 1)
+    logging.info("Loading dataset '%s' from '%s'", dataset_name, path)
+
+    if path.endswith(".jsonl"):
+      datasets[dataset_name] = lm_data.PromptExamples(path)
+    # .txt or .txt-#####-of-#####
+    elif path.endswith(".txt") or re.match(r".*\.txt-\d{5}-of-\d{5}$", path):
+      datasets[dataset_name] = _plaintext_prompts(path)
+    else:
+      raise ValueError(f"Unsupported dataset format for {dataset_string}")
+
+  for name in datasets:
+    datasets[name] = datasets[name].slice[:max_examples]
+    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
+
+  return datasets
+
+
+def get_dataset_loaders() -> lit_app.DatasetLoadersMap:
+  return {
+      "jsonl_examples": (
+          lm_data.PromptExamples,
+          lm_data.PromptExamples.init_spec(),
+      ),
+      "plaintext_inputs": (
+          _plaintext_prompts,
+          lm_data.PlaintextSents.init_spec(),
+      ),
+  }
diff --git a/lit_nlp/examples/models/instrumented_keras_lms.py b/lit_nlp/examples/prompt_debugging/keras_lms.py
similarity index 94%
rename from lit_nlp/examples/models/instrumented_keras_lms.py
rename to lit_nlp/examples/prompt_debugging/keras_lms.py
index b3bdb41c..2f7025e9 100644
--- a/lit_nlp/examples/models/instrumented_keras_lms.py
+++ b/lit_nlp/examples/prompt_debugging/keras_lms.py
@@ -1,12 +1,14 @@
 """LIT model wrappers for generic instrumented Keras LMs."""
 
+from collections.abc import Sequence
 import functools
 import inspect
 import types
-from typing import Sequence
+from typing import Optional
 
 from absl import logging
 import keras
+from keras_nlp import models as keras_models
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
 from lit_nlp.lib import utils as lit_utils
@@ -20,12 +22,10 @@
 # https://keras.io/getting_started/#configuring-your-backend
 if keras.backend.backend() == "tensorflow":
   import tensorflow as tf
-elif keras.backend.backend() == "jax":
-  # TODO(lit-dev): Update imports once a solution to JAX salience is decided.
-  pass
 elif keras.backend.backend() == "torch":
   import torch
 else:
+  # TODO(b/333373960): Update imports once a JAX salience is supported.
   raise ValueError(f"Unsupported backend: {keras.backend.backend()}")
 # pytype: enable=import-error
 # pylint: enable=g-import-not-at-top
@@ -54,12 +54,13 @@ class _KerasBaseModel(lit_model.BatchedModel):
   # Should be keras_nlp.models.generative_task.GenerativeTask
   def __init__(
       self,
-      model,
+      model: Optional[keras_models.CausalLM] = None,
+      model_name_or_path: Optional[str] = None,
       max_length: int = _DEFAULT_MAX_LENGTH,
       dynamic_sequence_length: bool = True,
       batch_size: int = 16,
   ):
-    """Base wrapper for a Keras/TF2 LM supporting the layer_intercept_fn API.
+    """Base wrapper for a Keras CausalLM supporting the layer_intercept_fn API.
 
     Model should support the following methods:
     - .generate()
@@ -72,7 +73,8 @@ def __init__(
     and manipulate activations between layers. We use this for salience, below.
 
     Args:
-      model: pre-loaded Keras LM
+      model: A pre-loaded Keras CausalLM, prioritized over model_name_or_path.
+      model_name_or_path: A URL, path, or preset name for the model to load,
       max_length: max sequence length
       dynamic_sequence_length: if true, will trim padding to the length of the
         longest sequence in a batch. Recommended for CPU and GPU usage, but may
@@ -81,7 +83,13 @@ def __init__(
     """
     super().__init__()
 
-    self.model = model
+    if model is not None:
+      self.model = model
+    elif model_name_or_path is not None:
+      self.model = keras_models.CausalLM.from_preset(model_name_or_path)
+    else:
+      raise ValueError("Must provide either model or model_name_or_path.")
+
     self.batch_size = batch_size
     self.max_length = max_length
     self.dynamic_sequence_length = dynamic_sequence_length
@@ -290,7 +298,7 @@ def __init__(self, *args, **kw):
     if score_fn is None or not inspect.ismethod(score_fn):
       raise TypeError(
           "Salience is computed via a .score() API, which is not supported by "
-          "all GenerativeTask models in KerasNLP. Please provide a model that "
+          "all KerasNLP CausalLM models. Please provide a model that "
           "supports this API."
       )
 
@@ -530,8 +538,10 @@ def initialize_model_group_for_salience(
 ) -> dict[str, lit_model.Model]:
   """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'."""
   generation_model = KerasGenerationModel(*args, **kw)
-  salience_model = KerasSalienceModel(*args, **kw)
-  tokenizer_model = KerasTokenizerModel(*args, **kw)
+  salience_model = KerasSalienceModel(model=generation_model.model, *args, **kw)
+  tokenizer_model = KerasTokenizerModel(
+      model=generation_model.model, *args, **kw
+  )
   return {
       name: generation_model,
       f"_{name}_salience": salience_model,
diff --git a/lit_nlp/examples/prompt_debugging/layouts.py b/lit_nlp/examples/prompt_debugging/layouts.py
new file mode 100644
index 00000000..84cefe9b
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/layouts.py
@@ -0,0 +1,64 @@
+"""Layouts for debugging language models in LIT."""
+
+from lit_nlp.api import layout
+
+
+_modules = layout.LitModuleName
+
+LEFT_RIGHT_LAYOUT = layout.LitCanonicalLayout(
+    left={
+        "Examples": [_modules.DataTableModule],
+        "Editor": [_modules.SingleDatapointEditorModule],
+    },
+    upper={  # if 'lower' not specified, this fills the right side
+        "Salience": [_modules.SequenceSalienceModule],
+    },
+    layoutSettings=layout.LayoutSettings(leftWidth=40),
+    description="Left/right layout for language model salience.",
+)
+
+TOP_BOTTOM_LAYOUT = layout.LitCanonicalLayout(
+    upper={
+        "Examples": [_modules.SimpleDataTableModule],
+        "Editor": [_modules.SimpleDatapointEditorModule],
+    },
+    lower={
+        "Salience": [_modules.SequenceSalienceModule],
+    },
+    layoutSettings=layout.LayoutSettings(
+        hideToolbar=True,
+        mainHeight=40,
+        centerPage=True,
+    ),
+    description="Simplified layout for language model salience.",
+)
+
+THREE_PANEL_LAYOUT = layout.LitCanonicalLayout(
+    left={
+        "Data Table": [_modules.DataTableModule],
+        "Embeddings": [_modules.EmbeddingsModule],
+    },
+    upper={
+        "Datapoint Editor": [_modules.SingleDatapointEditorModule],
+        "Datapoint Generators": [_modules.GeneratorModule],
+    },
+    lower={
+        "Salience": [_modules.SequenceSalienceModule],
+        "Metrics": [_modules.MetricsModule],
+    },
+    layoutSettings=layout.LayoutSettings(
+        mainHeight=40,
+        leftWidth=40,
+    ),
+    description="Custom layout for language model salience.",
+)
+
+LEFT_RIGHT = "left_right"
+TOP_BOTTOM = "top_bottom"
+THREE_PANEL = "three_panel"
+
+PROMPT_DEBUGGING_LAYOUTS = {
+    LEFT_RIGHT: LEFT_RIGHT_LAYOUT,
+    TOP_BOTTOM: TOP_BOTTOM_LAYOUT,
+    THREE_PANEL: THREE_PANEL_LAYOUT,
+}
diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py
new file mode 100644
index 00000000..6f2347f0
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/models.py
@@ -0,0 +1,109 @@
+"""Methods for configuring models for prompt debugging."""
+
+from collections.abc import Mapping, Sequence
+import os
+from typing import Optional
+
+from absl import logging
+from lit_nlp.api import model as lit_model
+from lit_nlp.lib import file_cache
+
+
+DEFAULT_BATCH_SIZE = 4
+DEFAULT_DL_FRAMEWORK = "kerasnlp"
+DEFAULT_DL_RUNTIME = "tensorflow"
+DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"],
+DEFAULT_PRECISION = "bfloat16"
+
+
+def _initialize_modeling_environment(
+    dl_framework: str,
+    dl_runtime: str,
+    precision: str,
+) -> None:
+  """Configure the modeling environment."""
+  if dl_framework == "kerasnlp":
+    # NOTE: Keras requires that the KERAS_BACKEND variable is set before import.
+    os.environ["KERAS_BACKEND"] = dl_runtime
+
+    # NOTE: Imported here and not at the top of the file to avoid
+    # initialization issues with the environment variables above.
+    import keras  # pylint: disable=g-import-not-at-top
+
+    keras.config.set_floatx(precision)
+  elif dl_runtime == "torch":
+    # NOTE: Keras sets precision for all backends with set_floatx(), but for
+    # HuggingFace Transformers with PyTorch we need to set it explicitly.
+    import torch  # pylint: disable=g-import-not-at-top # pytype: disable=import-error
+
+    torch.set_default_dtype(
+        torch.bfloat16 if precision == "bfloat16" else torch.float32
+    )
+
+
+def get_models(
+    models_config: Optional[Sequence[str]] = None,
+    dl_framework: str = DEFAULT_DL_FRAMEWORK,
+    dl_runtime: str = DEFAULT_DL_RUNTIME,
+    precision: str = DEFAULT_PRECISION,
+    batch_size: int = DEFAULT_BATCH_SIZE,
+) -> Mapping[str, lit_model.Model]:
+  """Loads models from the given configs.
+
+  Args:
+    models_config: A list of model names and paths to load from, as
+      "model:path", where path can be a URL, a local file path, or the name of a
+      preset for the configured deep learning framework.
+    dl_framework: The deep learning framework that loads and runs the model on
+      the runtime, `models_config.path` incompatibilities will result in errors.
+    dl_runtime: The deep learning runtime that the model runs on, either
+      "tensorflow" or "torch". All loaded models will use the same runtime,
+      incompatibilities will result in errors.
+    precision: Floating point precision for the models, either `bfloat16` or
+      `float32`.
+    batch_size: The number of examples to process per batch.
+
+  Returns:
+    A mapping from model name to initialized LIT model.
+  """
+
+  if not models_config:
+    return {}
+
+  # NOTE: Always call this function before initializing models to ensure the
+  # environment is properly configured.
+  _initialize_modeling_environment(dl_framework, dl_runtime, precision)
+
+  models: dict[str, lit_model.Model] = {}
+  for model_string in models_config:
+    # Only split on the first ':' as path may be a URL containing 'https://'
+    model_name, path = model_string.split(":", 1)
+    logging.info("Loading model '%s' from '%s'", model_name, path)
+
+    if path.endswith(".tar.gz") or file_cache.is_remote(path):
+      path = file_cache.cached_path(
+          path,
+          extract_compressed_file=path.endswith(".tar.gz"),
+      )
+
+    if dl_framework == "kerasnlp":
+      from lit_nlp.examples.prompt_debugging import keras_lms  # pylint: disable=g-import-not-at-top # pytype: disable=import-error
+
+      models |= keras_lms.initialize_model_group_for_salience(
+          model_name,
+          model_name_or_path=path,
+          max_length=512,
+          batch_size=batch_size,
+      )
+    else:
+      from lit_nlp.examples.prompt_debugging import transformers_lms  # pylint: disable=g-import-not-at-top # pytype: disable=import-error
+
+      models |= transformers_lms.initialize_model_group_for_salience(
+          model_name,
+          path,
+          batch_size=batch_size,
+          framework=dl_runtime,
+          max_new_tokens=512,
+      )
+
+  return models
diff --git a/lit_nlp/examples/prompt_debugging/notebook.py b/lit_nlp/examples/prompt_debugging/notebook.py
new file mode 100644
index 00000000..a7812fc2
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/notebook.py
@@ -0,0 +1,63 @@
+"""Convenience functions for configuring LIT prompt debugging in a notebook."""
+
+from collections.abc import Sequence
+
+from lit_nlp import notebook as lit_notebook
+from lit_nlp.examples.prompt_debugging import datasets
+from lit_nlp.examples.prompt_debugging import layouts
+from lit_nlp.examples.prompt_debugging import models
+
+
+def make_notebook_widget(
+    datasets_config: Sequence[str],
+    models_config: Sequence[str],
+    *,
+    # keep-sorted start
+    batch_size: int = models.DEFAULT_BATCH_SIZE,
+    dl_framework: str = "kerasnlp",
+    dl_runtime: str = "tensorflow",
+    max_examples: int = datasets.DEFAULT_MAX_EXAMPLES,
+    precision: str = "bfloat16",
+    # keep-sorted end,
+    **kwargs,
+) -> lit_notebook.LitWidget:
+  """Initializes a LIT widget for prompt debugging in a notebook.
+
+  Args:
+    datasets_config: A list of dataset names and paths to load from, as
+      "dataset:path", where path can be a URL, a local file path, or the name of
+      a preset for the configured deep learning framework.
+    models_config: A list of model names and paths to load from, as
+      "model:path", where path can be a URL, a local file path, or the name of a
+      preset for the configured deep learning framework.
+    batch_size: The number of examples the model will process per batch.
+    dl_framework: The deep learning framework that loads and runs the model on
+      the runtime, `models_config.path` incompatibilities will result in errors.
+    dl_runtime: The deep learning runtime that the model runs on, either
+      "tensorflow" or "torch". All loaded models will use the same runtime,
+      incompatibilities will result in errors.
+    max_examples: Maximum number of examples in each loaded dataset.
+    precision: Floating point precision for the models, either `bfloat16` or
+      `float32`.
+    **kwargs: Additional keyword arguments passed to the LitWidget. See also
+      LitApp for additinoal keyword arguments accepted by the LitWidget.
+
+  Returns:
+    A LitWidget with the configured models and datasets. Call `widget.render()`
+    to load the data and render the UI.
+  """
+  return lit_notebook.LitWidget(
+      models=models.get_models(
+          models_config=models_config,
+          dl_framework=dl_framework,
+          dl_runtime=dl_runtime,
+          precision=precision,
+          batch_size=batch_size,
+      ),
+      datasets=datasets.get_datasets(
+          datasets_config=datasets_config, max_examples=max_examples
+      ),
+      layouts=layouts.PROMPT_DEBUGGING_LAYOUTS,
+      default_layout=layouts.LEFT_RIGHT,
+      **kwargs,
+  )
diff --git a/lit_nlp/examples/prompt_debugging/server.py b/lit_nlp/examples/prompt_debugging/server.py
new file mode 100644
index 00000000..55ef7420
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/server.py
@@ -0,0 +1,174 @@
+r"""Server for sequence salience with a left-to-right language model.
+
+To use with the Gemma, Llama, or Mistral models, install the latest versions of
+Keras, KerasNLP, and/or HuggingFace Transformers:
+
+  pip install keras>=3.1.0 keras-nlp>=0.9.0 transformers>=4.38.0
+
+To run with the default configuration (Gemma on TensorFlow via Keras):
+
+  python3 -m lit_nlp.examples.prompt_debugging.server -- \
+    --models=gemma:gemma_1.1_instruct_2b_en \
+    --alsologtostderr
+
+MODELS:
+
+We strongly recommend a GPU or other accelerator to run this server with LLMs.
+The table below shows the model names and presets for common models. Use these
+to parameterize the --models flag with comma-separated `{model}:{preset}`
+strings, and remember the number of models loaded will be limited by the memory
+available on your accelerator.
+
+| Model   | dl_framework | dl_backend=tensorflow Preset | dl_backend=torch Preset              |
+| ------- | ------------ | ---------------------------- | ------------------------------------ |
+| Gemma   | kerasnlp     | gemma_1.1_instruct_7b_en     | gemma_1.1_instruct_7b_en             |
+| Gemma   | transformers | Unavailable                  | google/gemma-1.1-7b-it               |
+| Llama 2 | kerasnlp     | llama2_instruct_7b_en        | llama2_instruct_7b_en                |
+| Llama 2 | transformers | Unavailable                  | meta-llama/Llama-2-7b-hf             |
+| Mistral | kerasnlp     | mistral_instruct_7b_en       | mistral_instruct_7b_en               |
+| Mistral | transformers | Unavailable                  | mistralai/Mistral-7B-Instruct-v0.2   |
+
+Additional model presets can be found at the following locations, though
+compatibility with the LIT model wrappers is not guaranteed:
+
+* KerasNLP: https://keras.io/api/keras_nlp/models/
+* HuggingFace Transformers: https://huggingface.co/models
+
+DATASETS:
+
+By default this includes a small set of sample prompts. You can load your own
+examples using the --datasets flag or through the "Configure" menu in the UI.
+"""
+
+from collections.abc import Sequence
+import sys
+from typing import Optional
+
+from absl import app
+from absl import flags
+from absl import logging
+from lit_nlp import dev_server
+from lit_nlp import server_flags
+from lit_nlp.examples.prompt_debugging import datasets
+from lit_nlp.examples.prompt_debugging import layouts
+from lit_nlp.examples.prompt_debugging import models
+
+
+# The following flags enable command line configuration datasets.
+_DATASETS = flags.DEFINE_list(
+    "datasets",
+    datasets.DEFAULT_DATASETS,
+    "Datasets to load, as <name>:<path>. Format should be either .jsonl where"
+    " each record contains 'prompt' and optional 'target' and 'source' fields,"
+    " or a plain text file with one prompt per line.",
+)
+
+_MAX_EXAMPLES = flags.DEFINE_integer(
+    "max_examples",
+    datasets.DEFAULT_MAX_EXAMPLES,
+    (
+        "Maximum number of examples to load from each evaluation set. Set to"
+        " None to load the full set."
+    ),
+)
+
+# The following flags enable command line configuration of models.
+_BATCH_SIZE = flags.DEFINE_integer(
+    "batch_size",
+    models.DEFAULT_BATCH_SIZE,
+    "The number of examples to process per batch.",
+)
+
+_DL_FRAMEWORK = flags.DEFINE_enum(
+    "dl_framework",
+    models.DEFAULT_DL_FRAMEWORK,
+    ("kerasnlp", "transformers"),
+    "The deep learning framework that loads and runs the model on the backend."
+    " This server will attempt to load all models specified by the --models"
+    " flag with the configured framework, incompatibilities will result in"
+    " errors.",
+)
+
+_DL_RUNTIME = flags.DEFINE_enum(
+    "dl_runtime",
+    models.DEFAULT_DL_RUNTIME,
+    # TODO(b/333373960): Add "jax" once JAX salience is supported.
+    ("tensorflow", "torch"),
+    "The deep learning backend framework that the model runs on. All models"
+    " loaded by this server will use the same backend, incompatibilities will"
+    " result in errors.",
+)
+
+_MODELS = flags.DEFINE_list(
+    "models",
+    models.DEFAULT_MODELS,
+    "Models to load, as <name>:<path>. Path can be a URL, a local file path, or"
+    " the name of a preset for the configured Deep Learning framework (either"
+    " KerasNLP or HuggingFace Transformers; see --dl_framework for more). This"
+    " demo is tested with Gemma, GPT2, Llama, and Mistral on all supported"
+    " --dl_framework values. Other models should work, but adjustments might be"
+    " needed on their tokenizers (e.g., to define custom pad_token"
+    " when eos_token is not available to use as pad_token).",
+)
+
+_PRECISION = flags.DEFINE_enum(
+    "precision",
+    models.DEFAULT_PRECISION,
+    ("bfloat16", "float32"),
+    "Floating point precision for the models, only `bfloat16` and `float32` are"
+    " supported at this time.",
+)
+
+_FLAGS = flags.FLAGS
+_FLAGS.set_default("development_demo", True)
+_FLAGS.set_default("page_title", "LM Prompt Debugging")
+_FLAGS.set_default("default_layout", layouts.THREE_PANEL)
+
+_SPLASH_SCREEN_DOC = """
+# Language Model Salience
+
+To begin, select an example, then click the segment(s) (tokens, words, etc.)
+of the output that you would like to explain. Preceding segments(s) will be
+highlighted according to their importance to the selected target segment(s),
+with darker colors indicating a greater influence (salience) of that segment on
+the model's likelihood of the target segment.
+"""
+
+
+def get_wsgi_app() -> Optional[dev_server.LitServerType]:
+  """Return WSGI app for container-hosted demos."""
+  _FLAGS.set_default("server_type", "external")
+  _FLAGS.set_default("demo_mode", True)
+  # Parse flags without calling app.run(main), to avoid conflict with
+  # gunicorn command line flags.
+  unused = flags.FLAGS(sys.argv, known_only=True)
+  if unused:
+    logging.info("lm_demo:get_wsgi_app() called with unused args: %s", unused)
+  return main([])
+
+
+def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  lit_demo = dev_server.Server(
+      models=models.get_models(
+          models_config=_MODELS.value,
+          dl_framework=_DL_FRAMEWORK.value,
+          dl_runtime=_DL_RUNTIME.value,
+          precision=_PRECISION.value,
+          batch_size=_BATCH_SIZE.value,
+      ),
+      datasets=datasets.get_datasets(
+          datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value
+      ),
+      layouts=layouts.PROMPT_DEBUGGING_LAYOUTS,
+      dataset_loaders=datasets.get_dataset_loaders(),
+      onboard_start_doc=_SPLASH_SCREEN_DOC,
+      **server_flags.get_flags(),
+  )
+  return lit_demo.serve()
+
+
+if __name__ == "__main__":
+  app.run(main)
diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py
new file mode 100644
index 00000000..1ab121f6
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py
@@ -0,0 +1,572 @@
+"""Wrapper for HuggingFace models in LIT.
+
+Includes BERT masked LM, GPT-2, and T5.
+
+This wrapper loads a model into memory and implements the a number of helper
+functions to predict a batch of examples and extract information such as
+hidden states and attention.
+"""
+from collections.abc import Sequence
+import enum
+import functools
+
+from absl import logging
+from lit_nlp.api import model as lit_model
+from lit_nlp.api import types as lit_types
+from lit_nlp.lib import file_cache
+from lit_nlp.lib import utils
+import numpy as np
+import transformers
+
+# pylint: disable=g-import-not-at-top
+# pytype: disable=import-error
+try:
+  import tensorflow as tf
+except (ModuleNotFoundError, ImportError):
+  logging.warning("TensorFlow is not available.")
+
+try:
+  import torch
+except (ModuleNotFoundError, ImportError):
+  logging.warning("PyTorch is not available.")
+# pytype: enable=import-error
+# pylint: enable=g-import-not-at-top
+
+
+_DEFAULT_MAX_LENGTH = 1024
+_PYTORCH = "torch"
+_TENSORFLOW = "tensorflow"
+# HuggingFace uses two letter abbreviations for pytorch and tensorflow.
+_HF_PYTORCH = "pt"
+_HF_TENSORFLOW = "tf"
+
+
+@enum.unique
+class MLFramework(enum.Enum):
+  """The supported deep learning frameworks."""
+
+  PT = _PYTORCH
+  TF = _TENSORFLOW
+
+
+class HFBaseModel(lit_model.BatchedModel):
+  """Base class for HF generative, salience, tokenizer model wrappers."""
+
+  # Enum str values for entries in MLFramework, used for init_spec and logging.
+  _ML_FRAMEWORK_VALUES = [framework.value for framework in MLFramework]
+
+  @property
+  def num_layers(self):
+    return self.model.config.n_layer
+
+  @classmethod
+  def init_spec(cls) -> lit_model.Spec:
+    return {
+        "model_name_or_path": lit_types.String(default="gpt2"),
+        "batch_size": lit_types.Integer(default=6, min_val=1, max_val=64),
+        "framework": lit_types.CategoryLabel(vocab=cls._ML_FRAMEWORK_VALUES),
+    }
+
+  def __init__(
+      self,
+      model_name_or_path="gpt2",
+      batch_size=6,
+      framework=_PYTORCH,
+      model=None,
+      tokenizer=None,
+  ):
+    """Constructor for HF base model wrappers.
+
+    Note: args "model" and "tokenizer" take priority if both are specified.
+    Otherwise, "model_name_or_path" is used to initialize the model and
+    tokenizer.
+
+    This class supports common HF transformer models such as GPT2, Llama,
+    Mistral, etc.
+
+    Args:
+      model_name_or_path: gpt2, gpt2-medium, gpt2-large, distilgpt2,
+        meta-llama/Llama-2-7b-hf, mistralai/Mistral-7B-v0.1, etc.
+      batch_size: the number of items to process per `predict_minibatch` call.
+      framework: the deep learning framework, only "tensorflow" and "torch"
+        are supported.
+      model: an initialized transformer model.
+      tokenizer: an initialized tokenizer.
+    """
+    super().__init__()
+
+    if model is not None and tokenizer is not None:
+      self.model = model
+      self.tokenizer = tokenizer
+      # Check if the HF model object's framework is supported here.
+      if model.framework == _HF_PYTORCH:
+        self.framework = MLFramework.PT
+      elif model.framework == _HF_TENSORFLOW:
+        self.framework = MLFramework.TF
+      else:
+        raise ValueError(
+            f"The HuggingFace model framework `{model.framework}` is not"
+            " supported."
+        )
+    else:
+      # Normally path is a directory; if it's an archive file, download and
+      # extract to the transformers cache.
+      if model_name_or_path.endswith(".tar.gz"):
+        model_name_or_path = file_cache.cached_path(
+            model_name_or_path, extract_compressed_file=True
+        )
+
+      # Note: we need to left-pad for generation to work properly.
+      # Other modes such as scoring and salience should handle this as well;
+      # see example in HFSalienceModel._postprocess().
+      self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+          model_name_or_path,
+          use_fast=False,
+          padding_side="left",
+          model_max_length=_DEFAULT_MAX_LENGTH,
+      )
+      # Set this after init, as if pad_token= is passed to
+      # AutoTokenizer.from_pretrained() above it will create a new token with
+      # with id = max_vocab_length and cause out-of-bounds errors in
+      # the embedding lookup.
+      if framework == _PYTORCH:
+        auto_model = transformers.AutoModelForCausalLM
+        self.framework = MLFramework.PT
+      elif framework == _TENSORFLOW:
+        auto_model = transformers.TFAutoModelForCausalLM
+        self.framework = MLFramework.TF
+      else:
+        raise ValueError(
+            f"The provided value `{framework}` for arg `framework` is not"
+            f" supported, please choose from {self._ML_FRAMEWORK_VALUES}."
+        )
+      self.model = auto_model.from_pretrained(
+          model_name_or_path,
+          output_hidden_states=True,
+          output_attentions=False,
+      )
+    if self.framework == MLFramework.PT:
+      self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+      self.model = self.model.to(self.device)
+    self.embedding_table = self.model.get_input_embeddings()
+    self.tokenizer.pad_token = self.tokenizer.eos_token
+    self.batch_size = batch_size
+
+  @property
+  def pad_left(self):
+    return self.tokenizer.padding_side == "left"
+
+  @classmethod
+  def from_loaded(cls, existing: "HFBaseModel", *args, **kw):
+    """Share weights and underlying HF model with another instance."""
+    return cls(model=existing.model, tokenizer=existing.tokenizer, *args, **kw)
+
+  def clean_subword_token(self, tok):
+    # For GPT2 tokenizer.
+    tok = tok.replace("Ċ", "\n")  # newlines
+    tok = tok.replace("Ġ", "▁")  # start of word -> magic underscore
+    # For SentencePiece Tokenizer.
+    tok = tok.replace("<0x0A>", "\n")  # newlines
+    return tok
+
+  def ids_to_clean_tokens(self, ids: Sequence[int]) -> list[str]:
+    tokens = self.tokenizer.convert_ids_to_tokens(ids)
+    return [self.clean_subword_token(t) for t in tokens]
+
+  def max_minibatch_size(self) -> int:
+    # The BatchedModel base class handles batching automatically in the
+    # implementation of predict(), and uses this value as the batch size.
+    return self.batch_size
+
+  def input_spec(self):
+    return {
+        "prompt": lit_types.TextSegment(),
+        "target": lit_types.TextSegment(required=False),
+    }
+
+
+class HFGenerativeModel(HFBaseModel):
+  """Wrapper for a HF Transformer model that generates texts.
+
+  This class loads a tokenizer and model using the Huggingface library and
+  provides the LIT-required functions to generate text responses given input
+  prompts.
+
+  Note that the default model generation config is used such that the response
+  is produced using multinomial sampling.
+  """
+
+  @classmethod
+  def init_spec(cls) -> lit_model.Spec:
+    return super().init_spec() | {
+        "max_new_tokens": lit_types.Integer(default=50, min_val=1, max_val=500)
+    }
+
+  def __init__(self, *args, max_new_tokens=50, **kw):
+    """Constructor for HFGenerativeModel.
+
+    Args:
+      *args: as to HFBaseModel.__init__
+      max_new_tokens: the maximum number of new tokens to generate.
+      **kw: as to HFBaseModel.__init__
+    """
+    super().__init__(*args, **kw)
+    self.max_new_tokens = max_new_tokens
+
+  def _postprocess(self, preds):
+    """Post-process single-example preds. Operates on numpy arrays."""
+    # TODO(b/324957491): return actual decoder scores for each generation.
+    # GeneratedTextCandidates should be a list[(text, score)]
+    preds["response"] = [(preds["response"], 1.0)]
+    ntok_in = preds.pop("ntok_in")
+    embs = preds.pop("embs")
+    # Mean-pool over input tokens.
+    preds["prompt_embeddings"] = np.mean(
+        embs[-(self.max_new_tokens + ntok_in) : -self.max_new_tokens], axis=0
+    )
+    # Mean-pool over output (generated) tokens.
+    # TODO(b/324957491): slice this to only "real" output tokens,
+    # if generation length < max generation length.
+    preds["response_embeddings"] = np.mean(embs[-self.max_new_tokens :], axis=0)
+
+    return preds
+
+  ##
+  # LIT API implementations
+  def predict_minibatch(self, inputs):
+    prompts = [ex["prompt"] for ex in inputs]
+    encoded_inputs = self.tokenizer(
+        prompts,
+        return_tensors=_HF_PYTORCH
+        if self.framework == MLFramework.PT
+        else _HF_TENSORFLOW,
+        add_special_tokens=True,
+        padding="longest",
+        truncation="longest_first",
+    )
+    if self.framework == MLFramework.PT:
+      encoded_inputs = encoded_inputs.to(self.device)
+
+    outputs = self.model.generate(
+        encoded_inputs["input_ids"],
+        attention_mask=encoded_inputs["attention_mask"],
+        max_new_tokens=self.max_new_tokens,
+    )
+
+    responses = self.tokenizer.batch_decode(
+        outputs[:, -self.max_new_tokens :], skip_special_tokens=True
+    )
+
+    if self.framework == MLFramework.PT:
+      with torch.no_grad():
+        # Input embeddings: <float>[batch_size, num_tokens, emb_dim]
+        embeddings = self.embedding_table(outputs)
+
+      batched_outputs = {
+          "embs": embeddings.cpu().to(torch.float),
+          "ntok_in": (
+              torch.sum(encoded_inputs["attention_mask"], axis=1)
+              .cpu()
+              .to(torch.int)
+          ),
+      }
+    else:
+      embeddings = self.embedding_table(outputs)
+      batched_outputs = {
+          "embs": embeddings,
+          "ntok_in": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1),
+          # TODO(b/324957491): compute ntok_out if < max_output_tokens ?
+      }
+
+    # Convert to numpy for post-processing.
+    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
+    detached_outputs["response"] = responses
+    # Split up batched outputs, then post-process each example.
+    unbatched_outputs = utils.unbatch_preds(detached_outputs)
+    return map(self._postprocess, unbatched_outputs)
+
+  def output_spec(self) -> lit_types.Spec:
+    return {
+        "response": lit_types.GeneratedTextCandidates(parent="target"),
+        "prompt_embeddings": lit_types.Embeddings(required=False),
+        "response_embeddings": lit_types.Embeddings(required=False),
+    }
+
+
+class HFSalienceModel(HFBaseModel):
+  """Wrapper for a HF Transformer model that computes input (token) salience."""
+
+  def _left_pad_target_masks(self, seq_length, target_masks):
+    """Pads target masks (from left) to the desired sequence length.
+
+    Args:
+      seq_length: desired length of the padded masks.
+      target_masks: list(array_like) of binary (0/1) masks for each input.
+
+    Returns:
+      Numpy array of the padded masks at the desired sequence length.
+    """
+    # It doesn't make sense to interpret the first token, since it is not ever
+    # predicted. But we need to ensure that the mask[0] is zero, so it doesn't
+    # cause problems when 'rolled' to the last position below.
+    modified_masks = [[0] + list(mask[1:]) for mask in target_masks]
+    pad_fn = functools.partial(
+        utils.pad1d,
+        min_len=seq_length,
+        max_len=seq_length,
+        pad_val=0,
+        pad_left=self.pad_left,
+    )
+    padded_target_masks = np.stack(
+        [pad_fn(mask) for mask in modified_masks],
+        axis=0,
+    )
+    return padded_target_masks
+
+  def _pred_tf(self, encoded_inputs, target_masks):
+    """Predicts one batch of tokenized text using TF.
+
+    Also performs some batch-level post-processing in TF.
+    Single-example postprocessing is done in _postprocess(), and operates on
+    numpy arrays.
+
+    Args:
+      encoded_inputs: output of self.tokenizer()
+      target_masks: list(array_like) of binary (0/1) masks for each input
+
+    Returns:
+      payload: Dictionary with items described above, each as single Tensor.
+    """
+    input_ids = encoded_inputs["input_ids"]
+
+    # <tf.int32>[batch_size, num_tokens]; ignore the last one in each row.
+    target_ids = tf.roll(input_ids, shift=-1, axis=1)
+    ##
+    # Process target masks
+    padded_target_masks = tf.constant(
+        self._left_pad_target_masks(target_ids.shape[1], target_masks),
+        dtype=tf.bool,
+    )
+    # Shift masks back so they align with target_ids.
+    loss_mask = tf.roll(padded_target_masks, shift=-1, axis=1)
+
+    with tf.GradientTape(watch_accessed_variables=False) as tape:
+      # We need to run the embedding layer ourselves so we can trace it.
+      # See here for how the model normally does this:
+      # https://github.com/huggingface/transformers/blob/v4.29.2/src/transformers/models/gpt2/modeling_tf_gpt2.py#L450
+      embs = self.embedding_table(input_ids)
+      tape.watch(embs)
+
+      out = self.model(
+          input_ids=None,
+          inputs_embeds=embs,
+          attention_mask=encoded_inputs["attention_mask"],
+      )
+
+      loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
+          from_logits=True, reduction="none"
+      )
+      # <tf.float>[batch_size, num_tokens]
+      per_token_loss = loss_fn(target_ids, out.logits)
+      masked_loss = per_token_loss * tf.cast(loss_mask, per_token_loss.dtype)
+
+    grads = tape.gradient(
+        masked_loss, embs
+    )  # <tf.float>[batch_size, num_tokens, hdim]
+
+    grad_l2 = tf.norm(grads, axis=2)  # <tf.float>[batch_size, num_tokens]
+    grad_dot_input = tf.reduce_sum(
+        grads * embs, axis=2
+    )  # <tf.float>[batch_size, num_tokens]
+
+    batched_outputs = {
+        "input_ids": input_ids,
+        "attention_mask": encoded_inputs["attention_mask"],
+        # Gradients are already aligned to input tokens.
+        "grad_l2": grad_l2,
+        "grad_dot_input": grad_dot_input,
+        # Shift token loss to align with (input) tokens.
+        # "token_loss": tf.roll(per_token_loss, shift=1, axis=1),
+    }
+
+    return batched_outputs
+
+  def _pred_pt(self, encoded_inputs, target_masks):
+    """Predicts one batch of tokenized text using PyTorch.
+
+    Also performs some batch-level post-processing in PyTorch.
+    Single-example postprocessing is done in _postprocess(), and operates on
+    numpy arrays.
+
+    Args:
+      encoded_inputs: output of self.tokenizer()
+      target_masks: list(array_like) of binary (0/1) masks for each input
+
+    Returns:
+      payload: Dictionary with items described above, each as single Tensor.
+    """
+    encoded_inputs = encoded_inputs.to(self.device)
+    input_ids = encoded_inputs["input_ids"]
+    attention_mask = encoded_inputs["attention_mask"]
+
+    # [batch_size, num_tokens]; ignore the last one in each row.
+    target_ids = torch.roll(input_ids, shifts=-1, dims=1).to(self.device)
+    ##
+    # Process target masks
+    padded_target_masks = torch.tensor(
+        self._left_pad_target_masks(target_ids.shape[1], target_masks)
+    ).bool()
+    loss_mask = torch.roll(padded_target_masks, shifts=-1, dims=1).to(
+        self.device
+    )
+
+    embs = self.embedding_table(input_ids)
+    outs = self.model(
+        input_ids=None,
+        inputs_embeds=embs,
+        attention_mask=attention_mask,
+    )
+    loss_func = torch.nn.CrossEntropyLoss(reduction="none")
+    # Need to reshape outs.logits from [batch_size, num_tokens, vocab_size]
+    # to [batch_size, vocab_size, num_tokens] so the last dimension matches that
+    # of target_ids with dimension [batch_size, num_tokens].
+    per_token_loss = loss_func(outs.logits.permute(0, 2, 1), target_ids)
+    masked_loss = per_token_loss * loss_mask
+
+    # returned gradients are wrapped in a single item tuple.
+    grads = torch.autograd.grad(
+        masked_loss, embs, grad_outputs=torch.ones_like(masked_loss)
+    )[0]
+
+    # Remove the grad function from embs.
+    embs = embs.detach()
+    grad_l2 = torch.norm(grads, dim=2)  # [batch_size, num_tokens]
+    grad_dot_input = torch.sum(grads * embs, axis=2)  # [batch_size, num_tokens]
+
+    batched_outputs = {
+        "input_ids": input_ids.cpu().to(torch.int),
+        "attention_mask": attention_mask.cpu().to(torch.int),
+        # Gradients are already aligned to input tokens.
+        "grad_l2": grad_l2.cpu().to(torch.float),
+        "grad_dot_input": grad_dot_input.cpu().to(torch.float),
+        # Shift token loss to align with (input) tokens.
+        # "token_loss": torch.roll(per_token_loss, shifts=1, dims=1),
+    }
+
+    return batched_outputs
+
+  def _postprocess(self, preds):
+    """Post-process single-example preds. Operates on numpy arrays."""
+    # Be sure to cast to bool, otherwise this will select integer positions 0, 1
+    # rather than acting as a boolean mask.
+    mask = preds.pop("attention_mask").astype(bool)
+    ids = preds.pop("input_ids")[mask]
+    preds["tokens"] = self.ids_to_clean_tokens(ids)
+    for key in utils.find_spec_keys(self.output_spec(), lit_types.TokenScores):
+      preds[key] = preds[key][mask]
+    # First token (usually <s>) is not actually predicted, so return 0 for loss.
+    # preds["token_loss"][0] = 0
+
+    return preds
+
+  # LIT API implementations
+  def predict_minibatch(self, inputs):
+    """Predict on a single minibatch of examples."""
+    # Preprocess inputs.
+    texts = [ex["prompt"] + ex.get("target", "") for ex in inputs]
+    encoded_inputs = self.tokenizer(
+        texts,
+        return_tensors=_HF_PYTORCH
+        if self.framework == MLFramework.PT
+        else _HF_TENSORFLOW,
+        add_special_tokens=True,
+        padding="longest",
+        truncation="longest_first",
+    )
+    target_masks = [ex.get("target_mask", []) for ex in inputs]
+
+    # Get the predictions.
+    if self.framework == MLFramework.PT:
+      batched_outputs = self._pred_pt(encoded_inputs, target_masks)
+    else:
+      batched_outputs = self._pred_tf(encoded_inputs, target_masks)
+
+    # Convert to numpy for post-processing.
+    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
+    # Split up batched outputs, then post-process each example.
+    unbatched_outputs = utils.unbatch_preds(detached_outputs)
+    return map(self._postprocess, unbatched_outputs)
+
+  def input_spec(self):
+    return super().input_spec() | {
+        "target_mask": lit_types.TokenScores(align="", required=False),
+    }
+
+  def output_spec(self) -> lit_types.Spec:
+    return {
+        "tokens": lit_types.Tokens(parent=""),  # all tokens
+        "grad_l2": lit_types.TokenScores(align="tokens"),
+        "grad_dot_input": lit_types.TokenScores(align="tokens"),
+        # "token_loss": lit_types.TokenScores(align="tokens"),
+    }
+
+
+class HFTokenizerModel(HFBaseModel):
+  """Wrapper to run only the tokenizer.
+
+  Should exactly match tokens from HFSalienceModel.
+  """
+
+  def _postprocess(self, preds):
+    """Post-process single-example preds. Operates on numpy arrays."""
+    # Be sure to cast to bool, otherwise this will select intger positions 0, 1
+    # rather than acting as a boolean mask.
+    mask = preds.pop("attention_mask").astype(bool)
+    ids = preds.pop("input_ids")[mask]
+    preds["tokens"] = self.ids_to_clean_tokens(ids)
+    return preds
+
+  # LIT API implementations
+  def predict_minibatch(self, inputs):
+    """Predict on a single minibatch of examples."""
+    # Preprocess inputs.
+    texts = [ex["prompt"] + ex.get("target", "") for ex in inputs]
+    encoded_inputs = self.tokenizer(
+        texts,
+        return_tensors=_HF_PYTORCH
+        if self.framework == MLFramework.PT
+        else _HF_TENSORFLOW,
+        add_special_tokens=True,
+        padding="longest",
+        truncation="longest_first",
+    )
+    batched_outputs = {
+        "input_ids": encoded_inputs["input_ids"],
+        "attention_mask": encoded_inputs["attention_mask"],
+    }
+    # Convert to numpy for post-processing.
+    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
+    # Split up batched outputs, then post-process each example.
+    unbatched_outputs = utils.unbatch_preds(detached_outputs)
+    return map(self._postprocess, unbatched_outputs)
+
+  def output_spec(self) -> lit_types.Spec:
+    return {
+        "tokens": lit_types.Tokens(parent=""),  # all tokens
+    }
+
+
+def initialize_model_group_for_salience(
+    name, *args, max_new_tokens=512, **kw
+) -> dict[str, lit_model.Model]:
+  """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'."""
+  generation_model = HFGenerativeModel(
+      *args, **kw, max_new_tokens=max_new_tokens
+  )
+  salience_model = HFSalienceModel.from_loaded(generation_model)
+  tokenizer_model = HFTokenizerModel.from_loaded(generation_model)
+  return {
+      name: generation_model,
+      f"_{name}_salience": salience_model,
+      f"_{name}_tokenizer": tokenizer_model,
+  }
diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py
new file mode 100644
index 00000000..a00ed981
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py
@@ -0,0 +1,41 @@
+from absl.testing import absltest
+from absl.testing import parameterized
+from lit_nlp.examples.prompt_debugging import transformers_lms
+
+
+class TransformersLMSGeneration(parameterized.TestCase):
+  """Test that model classes can predict."""
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="tensorflow",
+          framework=transformers_lms.MLFramework.TF.value,
+          model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz",
+      ),
+      dict(
+          testcase_name="pytorch",
+          framework=transformers_lms.MLFramework.PT.value,
+          model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2-pt.tar.gz",
+      ),
+  )
+  def test_gpt2_generation(self, framework, model_path):
+    model = transformers_lms.HFGenerativeModel(
+        model_name_or_path=model_path, framework=framework
+    )
+    model_in = [{"prompt": "Today is"}, {"prompt": "What is the color of"}]
+    model_out = list(model.predict(model_in))
+
+    # Sanity-check output vs output spec.
+    self.assertLen(model_out, 2)
+    for key in model.output_spec().keys():
+      self.assertIn(key, model_out[0].keys())
+
+    # Check that the embedding dimension is the same for prompt and response.
+    self.assertEqual(
+        model_out[0]["prompt_embeddings"].shape,
+        model_out[0]["response_embeddings"].shape,
+    )
+
+
+if __name__ == "__main__":
+  absltest.main()
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 39fafcb4..1e6f3410 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -440,7 +440,7 @@ left-to-right ("causal") language models:
 
 The UI supports multiple options for analysis, including:
 
-*   Select from pre-defined target sequences, or explain generations from the
+*   Select from predefined target sequences, or explain generations from the
     model.
 *   Different salience methods, including [Gradient Norm](#gradient-norm) and
     [Gradient-dot-Input](#gradient-dot-input).
@@ -448,7 +448,7 @@ The UI supports multiple options for analysis, including:
     to words, sentences, lines, or paragraphs. Quickly switch between different
     views to refine your analysis to different parts of a prompt.
 *   Display density options to enable working with longer sequences, such as
-    document text, few-shot eaxmples, or chain-of-thought prompts.
+    document text, few-shot examples, or chain-of-thought prompts.
 
 For a walkthrough of how to use sequence salience to debug LLMs, check out the
 Responsible Generative AI Toolkit at
@@ -474,11 +474,11 @@ using either KerasNLP or Transformers.
 *   LIT-for-Gemma Colab:
     [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb)
 *   Demo binary:
-    [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py
 *   KerasNLP model wrappers:
-    [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py)
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py
 *   Transformers model wrappers:
-    [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py
 
 ## Salience Clustering
 
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 89bb4acb..8617519d 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -69,7 +69,8 @@ https://pair-code.github.io/lit/tutorials/sentiment
 
 ### Gemma <!-- DO NOT REMOVE {#gemma .demo-header} -->
 
-**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+**Code:**
+[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)
 
 *   Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
 *   Interactively debug LLM prompts using
diff --git a/website/src/tutorials/sequence-salience.md b/website/src/tutorials/sequence-salience.md
index 371b1271..29a99d34 100644
--- a/website/src/tutorials/sequence-salience.md
+++ b/website/src/tutorials/sequence-salience.md
@@ -1,20 +1,20 @@
 ---
-title: Prompt Engineering with Sequence Salience
+title: Prompt Debugging with Sequence Salience
 layout: layouts/tutorial.liquid
 
 hero-image: /assets/images/sample-banner.png
-hero-title: "Prompt Engineering with Sequence Salience"
-hero-copy: "Learn to use LIT's Sequence Salience module for prompt engineering."
+hero-title: "Prompt Debugging with Sequence Salience"
+hero-copy: "Learn to use LIT's Sequence Salience module for prompt debugging."
 
 bc-anchor-category: "analysis"
 bc-category-title: "Analysis"
-bc-title: "Prompt Engineering with Sequence Salience"
+bc-title: "Prompt Debugging with Sequence Salience"
 
 time: "20 minutes"
-takeaways: "Learn to use LIT's Sequence Salience module for prompt engineering."
+takeaways: "Learn to use LIT's Sequence Salience module for prompt debugging."
 ---
 
-## Prompt Engineering with Sequence Salience
+## Prompt Debugging with Sequence Salience
 
 {%  include partials/link-out,
     link: "https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb",

From fc7b0d0624f6cc8e456ac0a1d75a4149927bef2f Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Mon, 10 Jun 2024 10:47:33 -0700
Subject: [PATCH 18/47] Move the glue demo to a self-contained directory.

PiperOrigin-RevId: 641953632
---
 .github/workflows/ci.yml                      |   4 +-
 lit_nlp/components/ablation_flip_int_test.py  |   6 +-
 lit_nlp/components/hotflip_int_test.py        |   2 +-
 lit_nlp/components/tcav_int_test.py           |   2 +-
 lit_nlp/components/thresholder_int_test.py    |   2 +-
 lit_nlp/examples/blank_slate_demo.py          |  16 +-
 lit_nlp/examples/custom_module/potato_demo.py |   6 +-
 .../{datasets/glue.py => glue/data.py}        |   0
 .../examples/{glue_demo.py => glue/demo.py}   |  43 ++--
 .../model_int_test.py}                        |  22 +-
 .../{models/glue_models.py => glue/models.py} | 213 ++++++++++------
 .../models_test.py}                           | 165 ++++++------
 lit_nlp/examples/lm_demo.py                   |  15 +-
 .../notebooks/LIT_components_example.ipynb    |   8 +-
 .../notebooks/LIT_sentiment_classifier.ipynb  |   8 +-
 lit_nlp/examples/quickstart_sst_demo.py       |  82 ------
 lit_nlp/examples/simple_pytorch_demo.py       | 197 ---------------
 lit_nlp/examples/simple_tf2_demo.py           | 184 --------------
 lit_nlp/examples/sst_pytorch_demo.py          | 237 ------------------
 lit_nlp/examples/tools/glue_trainer.py        |  16 +-
 pyproject.toml                                |   3 +
 website/sphinx_src/components.md              |   8 +-
 website/sphinx_src/docker.md                  |  12 +-
 website/sphinx_src/faq.md                     |   6 +-
 24 files changed, 329 insertions(+), 928 deletions(-)
 rename lit_nlp/examples/{datasets/glue.py => glue/data.py} (100%)
 rename lit_nlp/examples/{glue_demo.py => glue/demo.py} (87%)
 rename lit_nlp/examples/{models/glue_models_int_test.py => glue/model_int_test.py} (82%)
 rename lit_nlp/examples/{models/glue_models.py => glue/models.py} (84%)
 rename lit_nlp/examples/{models/glue_models_test.py => glue/models_test.py} (61%)
 delete mode 100644 lit_nlp/examples/quickstart_sst_demo.py
 delete mode 100644 lit_nlp/examples/simple_pytorch_demo.py
 delete mode 100644 lit_nlp/examples/simple_tf2_demo.py
 delete mode 100644 lit_nlp/examples/sst_pytorch_demo.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 270b0666..2422e3b5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -47,7 +47,9 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install Python dependencies
       run: python -m pip install -r requirements.txt
-    - name: Test Python library
+    - name: Install LIT package
+      run: python -m pip install -e .
+    - name: Test LIT
       run: |
         python -m pip install pytest
         pytest -v
diff --git a/lit_nlp/components/ablation_flip_int_test.py b/lit_nlp/components/ablation_flip_int_test.py
index 7d4ee332..10cf9f9e 100644
--- a/lit_nlp/components/ablation_flip_int_test.py
+++ b/lit_nlp/components/ablation_flip_int_test.py
@@ -19,7 +19,7 @@
 from absl.testing import absltest
 from lit_nlp.api import types
 from lit_nlp.components import ablation_flip
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 import numpy as np
 
 
@@ -66,12 +66,12 @@ def setUp(self):
     self.classification_model = glue_models.SST2Model(BERT_TINY_PATH)
     self.classification_config = {ablation_flip.PREDICTION_KEY: 'probas'}
 
-    # Clasification model with the 'sentence' field marked as
+    # Classification model with the 'sentence' field marked as
     # non-required.
     self.classification_model_non_required_field = SST2ModelNonRequiredField(
         BERT_TINY_PATH)
 
-    # Clasification model with a counter to count number of predict calls.
+    # Classification model with a counter to count number of predict calls.
     # TODO(ataly): Consider setting up a Mock object to count number of
     # predict calls.
     self.classification_model_with_predict_counter = (
diff --git a/lit_nlp/components/hotflip_int_test.py b/lit_nlp/components/hotflip_int_test.py
index b97eed41..c5d7758b 100644
--- a/lit_nlp/components/hotflip_int_test.py
+++ b/lit_nlp/components/hotflip_int_test.py
@@ -18,7 +18,7 @@
 from absl.testing import parameterized
 from lit_nlp.components import hotflip
 # TODO(lit-dev): Move glue_models out of lit_nlp/examples
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 import numpy as np
 
 from lit_nlp.lib import file_cache
diff --git a/lit_nlp/components/tcav_int_test.py b/lit_nlp/components/tcav_int_test.py
index 592311e5..041a6a84 100644
--- a/lit_nlp/components/tcav_int_test.py
+++ b/lit_nlp/components/tcav_int_test.py
@@ -20,7 +20,7 @@
 from absl.testing import parameterized
 from lit_nlp.api import dataset as lit_dataset
 from lit_nlp.components import tcav
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.lib import caching  # for hash id fn
 from lit_nlp.lib import testing_utils
 
diff --git a/lit_nlp/components/thresholder_int_test.py b/lit_nlp/components/thresholder_int_test.py
index 3c0c9ca9..9775b79c 100644
--- a/lit_nlp/components/thresholder_int_test.py
+++ b/lit_nlp/components/thresholder_int_test.py
@@ -19,7 +19,7 @@
 from lit_nlp.api import dataset as lit_dataset
 from lit_nlp.api import types as lit_types
 from lit_nlp.components import thresholder
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.lib import caching  # for hash id fn
 
 
diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index 32642a74..470f57a9 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -1,4 +1,4 @@
-r"""An blank demo ready to load models and datasets.
+r"""A blank demo ready to load models and datasets.
 
 The currently supported models and datasets are:
 - classification model on SST-2, with the Stanford Sentiment Treebank dataset.
@@ -30,9 +30,9 @@
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.datasets import glue
 from lit_nlp.examples.datasets import lm
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import data as glue_data
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.examples.models import pretrained_lms
 from lit_nlp.examples.penguin import data as penguin_data
 from lit_nlp.examples.penguin import model as penguin_model
@@ -99,9 +99,9 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
   dataset_loaders: lit_app.DatasetLoadersMap = {}
 
   # glue demo dataset loaders.
-  dataset_loaders["sst2"] = (glue.SST2Data, glue.SST2Data.init_spec())
-  dataset_loaders["stsb"] = (glue.STSBData, glue.STSBData.init_spec())
-  dataset_loaders["mnli"] = (glue.MNLIData, glue.MNLIData.init_spec())
+  dataset_loaders["sst2"] = (glue_data.SST2Data, glue_data.SST2Data.init_spec())
+  dataset_loaders["stsb"] = (glue_data.STSBData, glue_data.STSBData.init_spec())
+  dataset_loaders["mnli"] = (glue_data.MNLIData, glue_data.MNLIData.init_spec())
 
   # penguin demo dataset loaders.
   dataset_loaders["penguin"] = (
@@ -111,8 +111,8 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
 
   # lm demo dataset loaders.
   dataset_loaders["sst (lm)"] = (
-      glue.SST2DataForLM,
-      glue.SST2DataForLM.init_spec(),
+      glue_data.SST2DataForLM,
+      glue_data.SST2DataForLM.init_spec(),
   )
   dataset_loaders["imdb (lm)"] = (
       classification.IMDBData,
diff --git a/lit_nlp/examples/custom_module/potato_demo.py b/lit_nlp/examples/custom_module/potato_demo.py
index bbaeb141..b9a50594 100644
--- a/lit_nlp/examples/custom_module/potato_demo.py
+++ b/lit_nlp/examples/custom_module/potato_demo.py
@@ -23,8 +23,8 @@
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.api import layout
-from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import data as glue_data
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.lib import file_cache
 
 # NOTE: additional flags defined in server_flags.py
@@ -84,7 +84,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
         model, extract_compressed_file=True)
 
   models = {"sst": glue_models.SST2Model(model)}
-  datasets = {"sst_dev": glue.SST2Data("validation")}
+  datasets = {"sst_dev": glue_data.SST2Data("validation")}
 
   # Start the LIT server. See server_flags.py for server options.
   lit_demo = dev_server.Server(
diff --git a/lit_nlp/examples/datasets/glue.py b/lit_nlp/examples/glue/data.py
similarity index 100%
rename from lit_nlp/examples/datasets/glue.py
rename to lit_nlp/examples/glue/data.py
diff --git a/lit_nlp/examples/glue_demo.py b/lit_nlp/examples/glue/demo.py
similarity index 87%
rename from lit_nlp/examples/glue_demo.py
rename to lit_nlp/examples/glue/demo.py
index 83b0474f..8fe9d703 100644
--- a/lit_nlp/examples/glue_demo.py
+++ b/lit_nlp/examples/glue/demo.py
@@ -1,14 +1,15 @@
 r"""Example demo loading a handful of GLUE models.
 
 For a quick-start set of models, run:
-  python -m lit_nlp.examples.glue_demo \
+  blaze run -c opt --config=cuda examples/glue:demo -- \
     --quickstart --port=5432
 
 To run with the 'normal' defaults, including full-size BERT models:
-  python -m lit_nlp.examples.glue_demo --port=5432
+  blaze run -c opt --config=cuda examples/glue:demo -- --port=5432
 
 Then navigate to localhost:5432 to access the demo UI.
 """
+
 from collections.abc import Sequence
 import sys
 from typing import Optional
@@ -19,8 +20,8 @@
 from lit_nlp import app as lit_app
 from lit_nlp import dev_server
 from lit_nlp import server_flags
-from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import data as glue_data
+from lit_nlp.examples.glue import models as glue_models
 
 # NOTE: additional flags defined in server_flags.py
 
@@ -29,8 +30,10 @@
 FLAGS.set_default("development_demo", True)
 
 _QUICKSTART = flags.DEFINE_bool(
-    "quickstart", False,
-    "Quick-start mode, loads smaller models and a subset of the full data.")
+    "quickstart",
+    False,
+    "Quick-start mode, loads smaller models and a subset of the full data.",
+)
 
 _MODELS = flags.DEFINE_list(
     "models",
@@ -50,9 +53,12 @@
 )
 
 _MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples", None, "Maximum number of examples to load into LIT. "
+    "max_examples",
+    None,
+    "Maximum number of examples to load into LIT. "
     "Note: MNLI eval set is 10k examples, so will take a while to run and may "
-    "be slow on older machines. Set --max_examples=200 for a quick start.")
+    "be slow on older machines. Set --max_examples=200 for a quick start.",
+)
 
 MODELS_BY_TASK = {
     "sst2": glue_models.SST2Model,
@@ -123,24 +129,33 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
     # split = 'validation' will also work, but this will cause TDFS to download
     # the entire dataset which can be very slow.
     split = "https://storage.googleapis.com/what-if-tool-resources/lit-data/sst2.validation.csv"
-    datasets["sst_dev"] = glue.SST2Data(split)
-    dataset_loaders["sst2"] = (glue.SST2Data, glue.SST2Data.init_spec())
+    datasets["sst_dev"] = glue_data.SST2Data(split)
+    dataset_loaders["sst2"] = (
+        glue_data.SST2Data,
+        glue_data.SST2Data.init_spec(),
+    )
 
   if "stsb" in tasks_to_load:
     logging.info("Loading data for STS-B task.")
     # split = 'validation' will also work, but this will cause TDFS to download
     # the entire dataset which can be very slow.
     split = "https://storage.googleapis.com/what-if-tool-resources/lit-data/stsb.validation.csv"
-    datasets["stsb_dev"] = glue.STSBData(split)
-    dataset_loaders["stsb"] = (glue.STSBData, glue.STSBData.init_spec())
+    datasets["stsb_dev"] = glue_data.STSBData(split)
+    dataset_loaders["stsb"] = (
+        glue_data.STSBData,
+        glue_data.STSBData.init_spec(),
+    )
 
   if "mnli" in tasks_to_load:
     logging.info("Loading data for MultiNLI task.")
     # split = 'validation_matched' will also work, but this will cause TDFS to
     # download the entire dataset which can be very slow.
     split = "https://storage.googleapis.com/what-if-tool-resources/lit-data/mnli.validation_matched.csv"
-    datasets["mnli_dev"] = glue.MNLIData(split)
-    dataset_loaders["mnli"] = (glue.MNLIData, glue.MNLIData.init_spec())
+    datasets["mnli_dev"] = glue_data.MNLIData(split)
+    dataset_loaders["mnli"] = (
+        glue_data.MNLIData,
+        glue_data.MNLIData.init_spec(),
+    )
 
   # Truncate datasets if --max_examples is set.
   if _MAX_EXAMPLES.value is not None:
diff --git a/lit_nlp/examples/models/glue_models_int_test.py b/lit_nlp/examples/glue/model_int_test.py
similarity index 82%
rename from lit_nlp/examples/models/glue_models_int_test.py
rename to lit_nlp/examples/glue/model_int_test.py
index fcac0d42..366a9428 100644
--- a/lit_nlp/examples/models/glue_models_int_test.py
+++ b/lit_nlp/examples/glue/model_int_test.py
@@ -1,8 +1,8 @@
-r"""Integration tests for lit_nlp.examples.models.glue_models.
+r"""Integration tests for lit_nlp.examples.glue.models.
 
 Test locally with:
 
-blaze test //third_party/py/lit_nlp/examples/models:integration_tests \
+blaze test //third_party/py/lit_nlp/examples/glue:integration_tests \
     --guitar_cluster=LOCAL \
     --test_output=streamed \
     --guitar_detach
@@ -11,7 +11,7 @@
 from typing import Any
 from absl.testing import absltest
 from absl.testing import parameterized
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.lib import file_cache
 
 
@@ -24,7 +24,8 @@ def __init__(self, *args: Any, **kwargs: Any):
     model_path = "https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz"  # pylint: disable=line-too-long
     if model_path.endswith(".tar.gz"):
       model_path = file_cache.cached_path(
-          model_path, extract_compressed_file=True)
+          model_path, extract_compressed_file=True
+      )
     self.sst2_model = glue_models.SST2Model(model_path)
 
   @parameterized.named_parameters(
@@ -48,17 +49,14 @@ def __init__(self, *args: Any, **kwargs: Any):
       # Common multiple cases
       dict(
           testcase_name="no_attention_or_embeddings",
-          config={
-              "output_attention": False,
-              "output_embeddings": False
-          },
+          config={"output_attention": False, "output_embeddings": False},
       ),
       dict(
           testcase_name="no_attention_or_embeddings_or_gradients",
           config={
               "compute_grads": False,
               "output_attention": False,
-              "output_embeddings": False
+              "output_embeddings": False,
           },
       ),
   )
@@ -66,13 +64,14 @@ def test_sst2_model_predict(self, config: dict[str, bool]):
     # Configure model.
     if config:
       self.sst2_model.config = glue_models.GlueModelConfig(
-          # Include the SST-2 defaut config options
+          # Include the SST-2 default config options
           text_a_name="sentence",
           text_b_name=None,
           labels=["0", "1"],
           null_label_idx=0,
           # Add the output-affecting config options
-          **config)
+          **config
+      )
 
     # Run prediction to ensure no failure.
     model_in = [{"sentence": "test sentence"}]
@@ -83,5 +82,6 @@ def test_sst2_model_predict(self, config: dict[str, bool]):
     for key in self.sst2_model.output_spec().keys():
       self.assertIn(key, model_out[0])
 
+
 if __name__ == "__main__":
   absltest.main()
diff --git a/lit_nlp/examples/models/glue_models.py b/lit_nlp/examples/glue/models.py
similarity index 84%
rename from lit_nlp/examples/models/glue_models.py
rename to lit_nlp/examples/glue/models.py
index a70cb1f0..9f14fe83 100644
--- a/lit_nlp/examples/models/glue_models.py
+++ b/lit_nlp/examples/glue/models.py
@@ -1,4 +1,5 @@
 """Wrapper for fine-tuned HuggingFace models in LIT."""
+
 # TODO(b/261736863): Update to PEP 585 typings, consider using f-strings, and
 # make common substrings into module CONSTANTS.
 
@@ -28,6 +29,7 @@
 @attr.s(auto_attribs=True, kw_only=True)
 class GlueModelConfig(object):
   """Config options for a GlueModel."""
+
   # Preprocessing options
   max_seq_length: int = 128
   inference_batch_size: int = 32
@@ -79,7 +81,6 @@ class GlueModel(lit_model.BatchedModel):
 
   This is a full-featured implementation, which includes embeddings, attention,
   gradients, as well as support for the different input and output types above.
-  For a more minimal example, see ../simple_tf2_demo.py.
   """
 
   def _verify_num_layers(self, hidden_states: Sequence[Any]):
@@ -102,9 +103,7 @@ def is_regression(self) -> bool:
 
   # TODO(b/254110131): Move file_cache.cached_path() call inside this __init__
   # function to reduce boilerplate in other locations (e.g., TCAV tests).
-  def __init__(self,
-               model_name_or_path="bert-base-uncased",
-               **config_kw):
+  def __init__(self, model_name_or_path="bert-base-uncased", **config_kw):
     self.config = GlueModelConfig(**config_kw)
     self._load_model(model_name_or_path)
     self._lock = threading.Lock()
@@ -119,9 +118,11 @@ def _load_model(self, model_name_or_path):
       )
 
     self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path)
+        model_name_or_path
+    )
     self.vocab = self.tokenizer.convert_ids_to_tokens(
-        range(len(self.tokenizer)))
+        range(len(self.tokenizer))
+    )
     model_config = transformers.AutoConfig.from_pretrained(
         model_name_or_path,
         num_labels=1 if self.is_regression else len(self.config.labels),
@@ -131,12 +132,14 @@ def _load_model(self, model_name_or_path):
     self.model = model_utils.load_pretrained(
         transformers.TFAutoModelForSequenceClassification,
         model_name_or_path,
-        config=model_config)
+        config=model_config,
+    )
 
   def _get_tokens(self, ex: JsonDict, field_name: str) -> list[str]:
     with self._lock:
-      return (ex.get("tokens_" + field_name) or
-              self.tokenizer.tokenize(ex[field_name]))
+      return ex.get("tokens_" + field_name) or self.tokenizer.tokenize(
+          ex[field_name]
+      )
 
   def _preprocess(self, inputs: Iterable[JsonDict]) -> dict[str, tf.Tensor]:
     # Use pretokenized input if available.
@@ -152,20 +155,26 @@ def _preprocess(self, inputs: Iterable[JsonDict]) -> dict[str, tf.Tensor]:
         self.tokenizer,
         tokens_a,
         tokens_b,
-        max_length=self.config.max_seq_length)
+        max_length=self.config.max_seq_length,
+    )
     return encoded_input  # pytype: disable=bad-return-type
 
   def _make_dataset(self, inputs: Iterable[JsonDict]) -> tf.data.Dataset:
     """Make a tf.data.Dataset from inputs in LIT format."""
     encoded_input = self._preprocess(inputs)
     if self.is_regression:
-      labels = tf.constant([ex[self.config.label_name] for ex in inputs],
-                           dtype=tf.float32)
+      labels = tf.constant(
+          [ex[self.config.label_name] for ex in inputs], dtype=tf.float32
+      )
     else:
-      labels = tf.constant([
-          self.config.labels.index(ex[self.config.label_name]) for ex in inputs
-      ],
-                           dtype=tf.int64)
+      indexes = []
+      if self.config.labels is not None:
+        for ex in inputs:
+          indexes.append(self.config.labels.index(ex[self.config.label_name]))
+      labels = tf.constant(
+          indexes,
+          dtype=tf.int64,
+      )
     # encoded_input is actually a transformers.BatchEncoding
     # object, which tf.data.Dataset doesn't like. Convert to a regular dict.
     return tf.data.Dataset.from_tensor_slices((dict(encoded_input), labels))
@@ -180,13 +189,18 @@ def train(
       keras_callbacks=None,
   ):
     """Run fine-tuning."""
-    train_dataset = self._make_dataset(train_inputs).shuffle(128).batch(
-        batch_size).repeat(-1)
+    train_dataset = (
+        self._make_dataset(train_inputs)
+        .shuffle(128)
+        .batch(batch_size)
+        .repeat(-1)
+    )
     # Use larger batch for validation since inference is about 1/2 memory usage
     # of backprop.
     eval_batch_size = 2 * batch_size
     validation_dataset = self._make_dataset(validation_inputs).batch(
-        eval_batch_size)
+        eval_batch_size
+    )
 
     # Prepare model for training.
     opt = tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08)
@@ -207,7 +221,8 @@ def train(
         validation_data=validation_dataset,
         validation_steps=validation_steps,
         callbacks=keras_callbacks,
-        verbose=2)
+        verbose=2,
+    )
     return history
 
   def save(self, path: str):
@@ -248,7 +263,8 @@ def _postprocess(self, output: dict[str, Any]):
     """Per-example postprocessing, on NumPy output."""
     ntok = output.pop("ntok")
     output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-        output.pop("input_ids")[:ntok])
+        output.pop("input_ids")[:ntok]
+    )
 
     # Tokens for each segment, individually.
     slicer_a, slicer_b = self._segment_slicers(output["tokens"])
@@ -258,21 +274,25 @@ def _postprocess(self, output: dict[str, Any]):
 
     # Embeddings for each segment, individually.
     if self.config.output_embeddings:
-      output["input_embs_" + self.config.text_a_name] = (
-          output["input_embs"][slicer_a])
+      output["input_embs_" + self.config.text_a_name] = output["input_embs"][
+          slicer_a
+      ]
       if self.config.text_b_name:
-        output["input_embs_" + self.config.text_b_name] = (
-            output["input_embs"][slicer_b])
+        output["input_embs_" + self.config.text_b_name] = output["input_embs"][
+            slicer_b
+        ]
 
     # Gradients for each segment, individually.
     if self.config.compute_grads:
       # Gradients for the CLS token.
       output["cls_grad"] = output["input_emb_grad"][0]
-      output["token_grad_" +
-             self.config.text_a_name] = output["input_emb_grad"][slicer_a]
+      output["token_grad_" + self.config.text_a_name] = output[
+          "input_emb_grad"
+      ][slicer_a]
       if self.config.text_b_name:
-        output["token_grad_" +
-               self.config.text_b_name] = output["input_emb_grad"][slicer_b]
+        output["token_grad_" + self.config.text_b_name] = output[
+            "input_emb_grad"
+        ][slicer_b]
 
       # TODO(b/294613507): remove output[self.config.label_name] once TCAV
       # is updated.
@@ -302,8 +322,9 @@ def _postprocess(self, output: dict[str, Any]):
 
     return output
 
-  def _scatter_embs(self, passed_input_embs, input_embs, batch_indices,
-                    offsets):
+  def _scatter_embs(
+      self, passed_input_embs, input_embs, batch_indices, offsets
+  ):
     """Scatters custom passed embeddings into the default model embeddings.
 
     Args:
@@ -331,9 +352,10 @@ def _scatter_embs(self, passed_input_embs, input_embs, batch_indices,
     # values that should be scattered in, i.e. one for each of the
     # (scatter_batch_size * num_tokens) word embeddings.
     scatter_indices = []
-    for (batch_index, sentence_embs, offset) in zip(batch_indices,
-                                                    filtered_embs, offsets):
-      for (token_index, _) in enumerate(sentence_embs):
+    for batch_index, sentence_embs, offset in zip(
+        batch_indices, filtered_embs, offsets
+    ):
+      for token_index, _ in enumerate(sentence_embs):
         scatter_indices.append([batch_index, token_index + offset])
 
     # Scatters passed word embeddings into embeddings gathered from tokens.
@@ -351,26 +373,36 @@ def scatter_all_embeddings(self, inputs, input_embs):
       The default model embeddings with scattered custom embeddings.
     """
     # Gets batch indices of any word embeddings that were passed for text_a.
-    passed_input_embs_a = [ex.get("input_embs_" + self.config.text_a_name)
-                           for ex in inputs]
-    batch_indices_a = [index for (index, emb) in enumerate(
-        passed_input_embs_a) if emb is not None]
+    passed_input_embs_a = [
+        ex.get("input_embs_" + self.config.text_a_name) for ex in inputs
+    ]
+    batch_indices_a = [
+        index
+        for (index, emb) in enumerate(passed_input_embs_a)
+        if emb is not None
+    ]
 
     # If word embeddings were passed in for text_a, scatter them into the
     # embeddings, gathered from the input ids. 1 is passed in as the offset
     # for each, since text_a starts at index 1, after the [CLS] token.
     if batch_indices_a:
       input_embs = self._scatter_embs(
-          passed_input_embs_a, input_embs, batch_indices_a,
-          offsets=np.ones(len(batch_indices_a), dtype=np.int64))
+          passed_input_embs_a,
+          input_embs,
+          batch_indices_a,
+          offsets=np.ones(len(batch_indices_a), dtype=np.int64),
+      )
 
     if self.config.text_b_name:
       # Gets batch indices of any word embeddings that were passed for text_b.
-      passed_input_embs_b = [ex.get("input_embs_" + self.config.text_b_name)
-                             for ex in inputs]
+      passed_input_embs_b = [
+          ex.get("input_embs_" + self.config.text_b_name) for ex in inputs
+      ]
       batch_indices_b = [
-          index for (index, emb) in enumerate(passed_input_embs_b)
-          if emb is not None]
+          index
+          for (index, emb) in enumerate(passed_input_embs_b)
+          if emb is not None
+      ]
 
       # If word embeddings were also passed in for text_b, scatter them into the
       # embeddings gathered from the input ids. The offsets are the [lengths
@@ -378,11 +410,15 @@ def scatter_all_embeddings(self, inputs, input_embs):
       # [CLS] [text_a tokens] [SEP]. (This assumes that text_b embeddings
       # will only be passed together with text_a embeddings.)
       if batch_indices_b:
-        lengths = np.array([len(embed) for embed in passed_input_embs_a
-                            if embed is not None])
+        lengths = np.array(
+            [len(embed) for embed in passed_input_embs_a if embed is not None]
+        )
         input_embs = self._scatter_embs(
-            passed_input_embs_b, input_embs, batch_indices_b,
-            offsets=(lengths + 2))
+            passed_input_embs_b,
+            input_embs,
+            batch_indices_b,
+            offsets=(lengths + 2),
+        )
     return input_embs
 
   def get_target_scores(self, inputs: Iterable[JsonDict], scores):
@@ -401,10 +437,12 @@ def get_target_scores(self, inputs: Iterable[JsonDict], scores):
         for (i, ex) in enumerate(inputs)
     ]
     # Convert the class names to indices if needed.
-    grad_idxs = [
-        self.config.labels.index(label) if isinstance(label, str) else label
-        for label in grad_classes
-    ]
+    grad_idxs = []
+    for label in grad_classes:
+      if isinstance(label, str) and self.config.labels is not None:
+        grad_idxs.append(self.config.labels.index(label))
+      else:
+        grad_idxs.append(label)
     # list of tuples (batch idx, label idx)
     gather_indices = list(enumerate(grad_idxs))
     # <tf.float32>[batch_size]
@@ -427,7 +465,8 @@ def predict_minibatch(self, inputs: Iterable[JsonDict]):
     # Use watch_accessed_variables to save memory by having the tape do nothing
     # if we don't need gradients.
     with tf.GradientTape(
-        watch_accessed_variables=self.config.compute_grads) as tape:
+        watch_accessed_variables=self.config.compute_grads
+    ) as tape:
       encoded_input = self._preprocess(inputs)
 
       # Gathers word embeddings from BERT model embedding layer using input ids
@@ -451,7 +490,8 @@ def predict_minibatch(self, inputs: Iterable[JsonDict]):
           training=False,
           output_hidden_states=True,
           output_attentions=True,
-          return_dict=True)
+          return_dict=True,
+      )
 
       batched_outputs = {
           "input_ids": encoded_input["input_ids"],
@@ -466,21 +506,25 @@ def predict_minibatch(self, inputs: Iterable[JsonDict]):
 
         # <float32>[batch_size, num_tokens, 1]
         token_mask = tf.expand_dims(
-            tf.cast(encoded_input["attention_mask"], tf.float32), axis=2)
+            tf.cast(encoded_input["attention_mask"], tf.float32), axis=2
+        )
         # <float32>[batch_size, 1]
         denom = tf.reduce_sum(token_mask, axis=1)
         for i, layer_output in enumerate(out.hidden_states):
           # layer_output is <float32>[batch_size, num_tokens, emb_dim]
           # average over tokens to get <float32>[batch_size, emb_dim]
-          batched_outputs[f"layer_{i}/avg_emb"] = tf.reduce_sum(
-              layer_output * token_mask, axis=1) / denom
+          batched_outputs[f"layer_{i}/avg_emb"] = (
+              tf.reduce_sum(layer_output * token_mask, axis=1) / denom
+          )
 
       if self.config.output_attention:
         if len(out.attentions) != self.model.config.num_hidden_layers:
-          raise ValueError("Unexpected size of attentions. Should be the same "
-                           "size as the number of hidden layers. Expected "
-                           f"{self.model.config.num_hidden_layers}, got "
-                           f"{len(out.attentions)}.")
+          raise ValueError(
+              "Unexpected size of attentions. Should be the same "
+              "size as the number of hidden layers. Expected "
+              f"{self.model.config.num_hidden_layers}, got "
+              f"{len(out.attentions)}."
+          )
         for i, layer_attention in enumerate(out.attentions):
           batched_outputs[f"layer_{i+1}/attention"] = layer_attention
 
@@ -511,7 +555,11 @@ def predict_minibatch(self, inputs: Iterable[JsonDict]):
           scalar_targets, input_embs
       )
 
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
+    detached_outputs = {
+        k: v.numpy()
+        for k, v in batched_outputs.items()
+        if v is not None
+    }
     # Sequence of dicts, one per example.
     unbatched_outputs = utils.unbatch_preds(detached_outputs)
     return map(self._postprocess, unbatched_outputs)
@@ -520,24 +568,28 @@ def input_spec(self) -> Spec:
     ret = {}
     ret[self.config.text_a_name] = lit_types.TextSegment()
     ret["tokens_" + self.config.text_a_name] = lit_types.Tokens(
-        parent=self.config.text_a_name, required=False)
+        parent=self.config.text_a_name, required=False
+    )
 
     if self.config.text_b_name:
       ret[self.config.text_b_name] = lit_types.TextSegment()
       ret["tokens_" + self.config.text_b_name] = lit_types.Tokens(
-          parent=self.config.text_b_name, required=False)
+          parent=self.config.text_b_name, required=False
+      )
 
     if self.is_regression:
       ret[self.config.label_name] = lit_types.Scalar(required=False)
     else:
       ret[self.config.label_name] = lit_types.CategoryLabel(
-          required=False, vocab=self.config.labels)
+          required=False, vocab=self.config.labels
+      )
 
     if self.config.output_embeddings:
       # The input_embs_ fields are used for Integrated Gradients.
       text_a_embs = "input_embs_" + self.config.text_a_name
       ret[text_a_embs] = lit_types.TokenEmbeddings(
-          align="tokens", required=False)
+          align="tokens", required=False
+      )
       if self.config.text_b_name:
         text_b_embs = "input_embs_" + self.config.text_b_name
         ret[text_b_embs] = lit_types.TokenEmbeddings(
@@ -548,17 +600,20 @@ def input_spec(self) -> Spec:
   def output_spec(self) -> Spec:
     ret = {"tokens": lit_types.Tokens()}
     ret["tokens_" + self.config.text_a_name] = lit_types.Tokens(
-        parent=self.config.text_a_name)
+        parent=self.config.text_a_name
+    )
     if self.config.text_b_name:
       ret["tokens_" + self.config.text_b_name] = lit_types.Tokens(
-          parent=self.config.text_b_name)
+          parent=self.config.text_b_name
+      )
     if self.is_regression:
       ret["score"] = lit_types.RegressionScore(parent=self.config.label_name)
     else:
       ret["probas"] = lit_types.MulticlassPreds(
           parent=self.config.label_name,
           vocab=self.config.labels,
-          null_idx=self.config.null_label_idx)
+          null_idx=self.config.null_label_idx,
+      )
 
     if self.config.output_embeddings:
       ret["cls_emb"] = lit_types.Embeddings()
@@ -568,11 +623,13 @@ def output_spec(self) -> Spec:
 
       # The input_embs_ fields are used for Integrated Gradients.
       ret["input_embs_" + self.config.text_a_name] = lit_types.TokenEmbeddings(
-          align="tokens_" + self.config.text_a_name)
+          align="tokens_" + self.config.text_a_name
+      )
       if self.config.text_b_name:
         text_b_embs = "input_embs_" + self.config.text_b_name
-        ret[text_b_embs] = lit_types.TokenEmbeddings(align="tokens_" +
-                                                     self.config.text_b_name)
+        ret[text_b_embs] = lit_types.TokenEmbeddings(
+            align="tokens_" + self.config.text_b_name
+        )
 
     # Gradients, if requested.
     if self.config.compute_grads:
@@ -604,7 +661,8 @@ def output_spec(self) -> Spec:
       # Attention heads, one field for each layer.
       for i in range(self.model.config.num_hidden_layers):
         ret[f"layer_{i+1}/attention"] = lit_types.AttentionHeads(
-            align_in="tokens", align_out="tokens")
+            align_in="tokens", align_out="tokens"
+        )
     return ret
 
 
@@ -618,7 +676,8 @@ def __init__(self, *args, **kw):
         text_b_name=None,
         labels=["0", "1"],
         null_label_idx=0,
-        **kw)
+        **kw,
+    )
 
 
 class MNLIModel(GlueModel):
@@ -630,7 +689,8 @@ def __init__(self, *args, **kw):
         text_a_name="premise",
         text_b_name="hypothesis",
         labels=["entailment", "neutral", "contradiction"],
-        **kw)
+        **kw,
+    )
 
 
 class STSBModel(GlueModel):
@@ -642,7 +702,8 @@ def __init__(self, *args, **kw):
         text_a_name="sentence1",
         text_b_name="sentence2",
         labels=None,
-        **kw)
+        **kw,
+    )
 
   def input_spec(self):
     ret = super().input_spec()
diff --git a/lit_nlp/examples/models/glue_models_test.py b/lit_nlp/examples/glue/models_test.py
similarity index 61%
rename from lit_nlp/examples/models/glue_models_test.py
rename to lit_nlp/examples/glue/models_test.py
index 1ce1ddbf..68d9b61a 100644
--- a/lit_nlp/examples/models/glue_models_test.py
+++ b/lit_nlp/examples/glue/models_test.py
@@ -3,7 +3,7 @@
 from absl.testing import absltest
 from absl.testing import parameterized
 import attr
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 import numpy as np
 
 
@@ -60,10 +60,7 @@ class GlueModelsTest(parameterized.TestCase):
       # Common multiple cases
       dict(
           testcase_name="no_attention_or_embeddings",
-          config={
-              "output_attention": False,
-              "output_embeddings": False
-          },
+          config={"output_attention": False, "output_embeddings": False},
           expect_attention=False,
           expect_embs=False,
           expect_grads=True,
@@ -73,18 +70,23 @@ class GlueModelsTest(parameterized.TestCase):
           config={
               "compute_grads": False,
               "output_attention": False,
-              "output_embeddings": False
+              "output_embeddings": False,
           },
           expect_attention=False,
           expect_embs=False,
           expect_grads=False,
       ),
   )
-  def test_spec_affecting_config_options(self, config: dict[str, bool],
-                                         expect_attention: bool,
-                                         expect_embs: bool, expect_grads: bool):
+  def test_spec_affecting_config_options(
+      self,
+      config: dict[str, bool],
+      expect_attention: bool,
+      expect_embs: bool,
+      expect_grads: bool,
+  ):
     model = GlueModelForTesting(
-        model_name_or_path="bert-base-uncased", **config)
+        model_name_or_path="bert-base-uncased", **config
+    )
     input_spec = model.input_spec()
     output_spec = model.output_spec()
 
@@ -139,27 +141,34 @@ def test_spec_affecting_config_options(self, config: dict[str, bool],
 
   def test_scatter_all_embeddings_single_input(self):
     glue_model = GlueModelForTesting(
-        model_name_or_path="bert-base-uncased",
-        text_a_name="sentence1")
+        model_name_or_path="bert-base-uncased", text_a_name="sentence1"
+    )
     emb_size = 10
     # We'll inject zeros for the embeddings of 'hi',
     # while special tokens get vectors of 1s.
     embs_a = np.zeros((1, emb_size))
     input_embs = np.ones((1, 3, emb_size))
     # Scatter embs_a into input_embs
-    result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
-                                                 "input_embs_sentence1": embs_a,
-                                                 }], input_embs)
-    target = [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]
+    result = glue_model.scatter_all_embeddings(
+        [{
+            "sentence1": "hi",
+            "input_embs_sentence1": embs_a,
+        }],
+        input_embs,
+    )
+    target = [[
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ]]
     np.testing.assert_almost_equal(result, target)
 
   def test_scatter_all_embeddings_both_inputs(self):
     glue_model = GlueModelForTesting(
         model_name_or_path="bert-base-uncased",
         text_a_name="sentence1",
-        text_b_name="sentence2")
+        text_b_name="sentence2",
+    )
     emb_size = 10
     # Inject zeros at positions corresponding to real tokens
     # in each segment. Special tokens get vectors of 1s.
@@ -167,74 +176,86 @@ def test_scatter_all_embeddings_both_inputs(self):
     embs_b = np.zeros((3, emb_size))
     input_embs = np.ones((1, 7, emb_size))
     # Scatter embs_a and embs_b into input_embs
-    result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
-                                                 "input_embs_sentence1": embs_a,
-                                                 "sentence2": "how are you",
-                                                 "input_embs_sentence2": embs_b
-                                                 }], input_embs)
-    target = [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]
+    result = glue_model.scatter_all_embeddings(
+        [{
+            "sentence1": "hi",
+            "input_embs_sentence1": embs_a,
+            "sentence2": "how are you",
+            "input_embs_sentence2": embs_b,
+        }],
+        input_embs,
+    )
+    target = [[
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ]]
     np.testing.assert_almost_equal(result, target)
 
   def test_scatter_all_embeddings_multi_batch(self):
     glue_model = GlueModelForTesting(
-        model_name_or_path="bert-base-uncased",
-        text_a_name="sentence1")
+        model_name_or_path="bert-base-uncased", text_a_name="sentence1"
+    )
     emb_size = 4
     embs_a = np.zeros((1, emb_size))
     embs_b = np.zeros((2, emb_size))
     input_embs = np.ones((2, 4, emb_size))
     # Scatter embs_a and embs_b into input_embs
-    result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
-                                                 "input_embs_sentence1": embs_a,
-                                                 },
-                                                {"sentence1": "hi there",
-                                                 "input_embs_sentence1": embs_b,
-                                                 }], input_embs)
-    target = [[[1, 1, 1, 1],
-               [0, 0, 0, 0],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1]],
-              [[1, 1, 1, 1],
-               [0, 0, 0, 0],
-               [0, 0, 0, 0],
-               [1, 1, 1, 1]]]
+    result = glue_model.scatter_all_embeddings(
+        [
+            {
+                "sentence1": "hi",
+                "input_embs_sentence1": embs_a,
+            },
+            {
+                "sentence1": "hi there",
+                "input_embs_sentence1": embs_b,
+            },
+        ],
+        input_embs,
+    )
+    target = [
+        [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]],
+        [[1, 1, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1]],
+    ]
     np.testing.assert_almost_equal(result, target)
 
     # Scatter only embs_a into input_embs
-    result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
-                                                 "input_embs_sentence1": embs_a,
-                                                 },
-                                                {"sentence1": "hi there"
-                                                 }], input_embs)
-    target = [[[1, 1, 1, 1],
-               [0, 0, 0, 0],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1]],
-              [[1, 1, 1, 1],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1]]]
+    result = glue_model.scatter_all_embeddings(
+        [
+            {
+                "sentence1": "hi",
+                "input_embs_sentence1": embs_a,
+            },
+            {"sentence1": "hi there"},
+        ],
+        input_embs,
+    )
+    target = [
+        [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]],
+        [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
+    ]
     np.testing.assert_almost_equal(result, target)
 
     # Scatter only embs_b into input_embs
-    result = glue_model.scatter_all_embeddings([{"sentence1": "hi"},
-                                                {"sentence1": "hi there",
-                                                 "input_embs_sentence1": embs_b,
-                                                 }], input_embs)
-    target = [[[1, 1, 1, 1],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1],
-               [1, 1, 1, 1]],
-              [[1, 1, 1, 1],
-               [0, 0, 0, 0],
-               [0, 0, 0, 0],
-               [1, 1, 1, 1]]]
+    result = glue_model.scatter_all_embeddings(
+        [
+            {"sentence1": "hi"},
+            {
+                "sentence1": "hi there",
+                "input_embs_sentence1": embs_b,
+            },
+        ],
+        input_embs,
+    )
+    target = [
+        [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
+        [[1, 1, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1]],
+    ]
     np.testing.assert_almost_equal(result, target)
 
 
diff --git a/lit_nlp/examples/lm_demo.py b/lit_nlp/examples/lm_demo.py
index e6b3d422..5af2156b 100644
--- a/lit_nlp/examples/lm_demo.py
+++ b/lit_nlp/examples/lm_demo.py
@@ -18,15 +18,14 @@
 from absl import app
 from absl import flags
 from absl import logging
-
 from lit_nlp import app as lit_app
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.api import layout
 from lit_nlp.components import word_replacer
 from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.datasets import glue
 from lit_nlp.examples.datasets import lm
+from lit_nlp.examples.glue import data as glue_data
 from lit_nlp.examples.models import pretrained_lms
 
 # NOTE: additional flags defined in server_flags.py
@@ -46,7 +45,8 @@
 )
 
 _TOP_K = flags.DEFINE_integer(
-    "top_k", 10, "Rank to which the output distribution is pruned.")
+    "top_k", 10, "Rank to which the output distribution is pruned."
+)
 
 _MAX_EXAMPLES = flags.DEFINE_integer(
     "max_examples",
@@ -130,7 +130,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
 
   datasets = {
       # Single sentences from movie reviews (SST dev set).
-      "sst_dev": glue.SST2Data("validation").remap({"sentence": "text"}),
+      "sst_dev": glue_data.SST2Data("validation").remap({"sentence": "text"}),
       # Longer passages from movie reviews (IMDB dataset, test split).
       "imdb_train": classification.IMDBData("test"),
       # Empty dataset, if you just want to type sentences into the UI.
@@ -138,7 +138,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
   }
 
   dataset_loaders: lit_app.DatasetLoadersMap = {
-      "sst_dev": (glue.SST2DataForLM, glue.SST2DataForLM.init_spec()),
+      "sst_dev": (glue_data.SST2DataForLM, glue_data.SST2DataForLM.init_spec()),
       "imdb_train": (
           classification.IMDBData,
           classification.IMDBData.init_spec(),
@@ -154,14 +154,15 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
   if _LOAD_BWB.value:
     # A few sentences from the Billion Word Benchmark (Chelba et al. 2013).
     datasets["bwb"] = lm.BillionWordBenchmark(
-        "train", max_examples=_MAX_EXAMPLES.value)
+        "train", max_examples=_MAX_EXAMPLES.value
+    )
     dataset_loaders["bwb"] = (
         lm.BillionWordBenchmark,
         lm.BillionWordBenchmark.init_spec(),
     )
 
   for name in datasets:
-    datasets[name] = datasets[name].slice[:_MAX_EXAMPLES.value]
+    datasets[name] = datasets[name].slice[: _MAX_EXAMPLES.value]
     logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
 
   generators = {"word_replacer": word_replacer.WordReplacer()}
diff --git a/lit_nlp/examples/notebooks/LIT_components_example.ipynb b/lit_nlp/examples/notebooks/LIT_components_example.ipynb
index 81da1a29..3a73924f 100644
--- a/lit_nlp/examples/notebooks/LIT_components_example.ipynb
+++ b/lit_nlp/examples/notebooks/LIT_components_example.ipynb
@@ -26,8 +26,8 @@
     "import pandas as pd\n",
     "\n",
     "from lit_nlp import notebook\n",
-    "from lit_nlp.examples.datasets import glue\n",
-    "from lit_nlp.examples.models import glue_models\n",
+    "from lit_nlp.examples.glue import data\n",
+    "from lit_nlp.examples.glue import models\n",
     "\n",
     "# Hide INFO and lower logs. Comment this out for debugging.\n",
     "from absl import logging\n",
@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "sst_data = glue.SST2Data('validation')\n",
+    "sst_data = data.SST2Data('validation')\n",
     "sst_data.spec()"
    ]
   },
@@ -130,7 +130,7 @@
     "!mkdir sst2_tiny\n",
     "!tar -xvf sst2_tiny.tar.gz -C sst2_tiny\n",
     "\n",
-    "sentiment_model = glue_models.SST2Model('./sst2_tiny')\n",
+    "sentiment_model = models.SST2Model('./sst2_tiny')\n",
     "sentiment_model.input_spec(), sentiment_model.output_spec()"
    ]
   },
diff --git a/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb b/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
index 516a6e3b..cab395fe 100644
--- a/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
+++ b/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
@@ -25,8 +25,8 @@
    "outputs": [],
    "source": [
     "from lit_nlp import notebook\n",
-    "from lit_nlp.examples.datasets import glue\n",
-    "from lit_nlp.examples.models import glue_models\n",
+    "from lit_nlp.examples.glue import data\n",
+    "from lit_nlp.examples.glue import models\n",
     "\n",
     "# Hide INFO and lower logs. Comment this out for debugging.\n",
     "from absl import logging\n",
@@ -55,8 +55,8 @@
    "outputs": [],
    "source": [
     "# Create the LIT widget with the model and dataset to analyze.\n",
-    "datasets = {'sst_dev': glue.SST2Data('validation')}\n",
-    "models = {'sst_tiny': glue_models.SST2Model('./')}\n",
+    "datasets = {'sst_dev': data.SST2Data('validation')}\n",
+    "models = {'sst_tiny': models.SST2Model('./')}\n",
     "\n",
     "widget = notebook.LitWidget(models, datasets, port=8890)"
    ]
diff --git a/lit_nlp/examples/quickstart_sst_demo.py b/lit_nlp/examples/quickstart_sst_demo.py
deleted file mode 100644
index 0276d1b8..00000000
--- a/lit_nlp/examples/quickstart_sst_demo.py
+++ /dev/null
@@ -1,82 +0,0 @@
-r"""Quick-start demo for a sentiment analysis model.
-
-This demo fine-tunes a small Transformer (BERT-tiny) on the Stanford Sentiment
-Treebank (SST-2), and starts a LIT server.
-
-To run locally:
-  python -m lit_nlp.examples.quickstart_sst_demo --port=5432
-
-Training should take less than 5 minutes on a single GPU. Once you see the
-ASCII-art LIT logo, navigate to localhost:5432 to access the demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-import tempfile
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.models import glue_models
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_ENCODER_NAME = flags.DEFINE_string(
-    "encoder_name", "google/bert_uncased_L-2_H-128_A-2",
-    "Encoder name to use for fine-tuning. See https://huggingface.co/models.")
-
-_MODEL_PATH = flags.DEFINE_string("model_path", None,
-                                  "Path to save trained model.")
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info(
-        "quickstart_sst_demo:get_wsgi_app() called with unused "
-        "args: %s", unused)
-  return main([])
-
-
-def run_finetuning(train_path):
-  """Fine-tune a transformer model."""
-  train_data = glue.SST2Data("train")
-  val_data = glue.SST2Data("validation")
-  model = glue_models.SST2Model(_ENCODER_NAME.value)
-  model.train(train_data.examples, validation_inputs=val_data.examples)
-  model.save(train_path)
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  model_path = _MODEL_PATH.value or tempfile.mkdtemp()
-  logging.info("Working directory: %s", model_path)
-  run_finetuning(model_path)
-
-  # Load our trained model.
-  models = {"sst": glue_models.SST2Model(model_path)}
-  datasets = {"sst_dev": glue.SST2Data("validation")}
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/simple_pytorch_demo.py b/lit_nlp/examples/simple_pytorch_demo.py
deleted file mode 100644
index cde642db..00000000
--- a/lit_nlp/examples/simple_pytorch_demo.py
+++ /dev/null
@@ -1,197 +0,0 @@
-r"""Code example for a custom model, using PyTorch.
-
-This demo shows how to use a custom model with LIT, in just a few lines of code.
-We'll use a transformers model, with a minimal amount of code to implement the
-LIT API. Compared to models/glue_models.py, this has fewer features, but the
-code is more readable.
-
-This demo is equivalent in functionality to simple_tf2_demo.py, but uses PyTorch
-instead of TensorFlow 2. The models behave identically as far as LIT is
-concerned, and the implementation is quite similar - to see changes, run:
-  git diff --no-index simple_tf2_demo.py simple_pytorch_demo.py
-
-The transformers library can load weights from either,
-so you can use any saved model compatible with the underlying model class
-(AutoModelForSequenceClassification). To train something for this demo, you can:
-- Use quickstart_sst_demo.py, and set --model_path to somewhere durable
-- Or: Use tools/glue_trainer.py
-- Or: Use any fine-tuning code that works with transformers, such as
-https://github.com/huggingface/transformers#quick-tour-of-the-fine-tuningusage-scripts
-
-To run locally:
-  python -m lit_nlp.examples.simple_pytorch_demo \
-      --port=5432 --model_path=/path/to/saved/model
-
-Then navigate to localhost:5432 to access the demo UI.
-
-NOTE: this demo still uses TensorFlow Datasets (which depends on TensorFlow) to
-load the data. However, the output of glue.SST2Data is just NumPy arrays and
-plain Python data, and you can easily replace this with a different library or
-directly loading from CSV.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-# Use the regular GLUE data loaders, because these are very simple already.
-from lit_nlp.examples.datasets import glue
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-
-import torch
-import transformers
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path",
-    "https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz",
-    "Path to trained model, in standard transformers format, e.g. as "
-    "saved by model.save_pretrained() and tokenizer.save_pretrained()"
-)
-
-SequenceClassifierOutput = (
-    transformers.modeling_outputs.SequenceClassifierOutput
-)
-
-
-def _from_pretrained(cls, *args, **kw):
-  """Load a transformers model in PyTorch, with fallback to TF2/Keras weights."""
-  try:
-    return cls.from_pretrained(*args, **kw)
-  except OSError as e:
-    logging.warning("Caught OSError loading model: %s", e)
-    logging.warning(
-        "Re-trying to convert from TensorFlow checkpoint (from_tf=True)")
-    return cls.from_pretrained(*args, from_tf=True, **kw)
-
-
-class SimpleSentimentModel(lit_model.BatchedModel):
-  """Simple sentiment analysis model."""
-
-  LABELS = ["0", "1"]  # negative, positive
-
-  def __init__(self, model_name_or_path):
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path)
-    model_config = transformers.AutoConfig.from_pretrained(
-        model_name_or_path,
-        num_labels=2,
-        output_hidden_states=True,
-        output_attentions=True,
-    )
-    # This is a just a regular PyTorch model.
-    self.model = _from_pretrained(
-        transformers.AutoModelForSequenceClassification,
-        model_name_or_path,
-        config=model_config)
-    self.model.eval()
-
-  ##
-  # LIT API implementation
-  def max_minibatch_size(self):
-    # This tells lit_model.BatchedModel.predict() how to batch inputs to
-    # predict_minibatch().
-    # Alternately, you can just override predict() and handle batching yourself.
-    return 32
-
-  def predict_minibatch(self, inputs):
-    # Preprocess to ids and masks, and make the input batch.
-    encoded_input = self.tokenizer.batch_encode_plus(
-        [ex["sentence"] for ex in inputs],
-        return_tensors="pt",
-        add_special_tokens=True,
-        max_length=128,
-        padding="longest",
-        truncation="longest_first")
-
-    # Check and send to cuda (GPU) if available
-    if torch.cuda.is_available():
-      self.model.cuda()
-      for tensor in encoded_input:
-        encoded_input[tensor] = encoded_input[tensor].cuda()
-    # Run a forward pass.
-    with torch.no_grad():  # remove this if you need gradients.
-      out: SequenceClassifierOutput = self.model(**encoded_input)
-
-    # Post-process outputs.
-    batched_outputs = {
-        "probas": torch.nn.functional.softmax(out.logits, dim=-1),
-        "input_ids": encoded_input["input_ids"],
-        "ntok": torch.sum(encoded_input["attention_mask"], dim=1),
-        "cls_emb": out.hidden_states[-1][:, 0],  # last layer, first token
-    }
-    # Return as NumPy for further processing.
-    detached_outputs = {k: v.cpu().numpy() for k, v in batched_outputs.items()}
-    # Unbatch outputs so we get one record per input example.
-    for output in utils.unbatch_preds(detached_outputs):
-      ntok = output.pop("ntok")
-      output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-          output.pop("input_ids")[1:ntok - 1])
-      yield output
-
-  def input_spec(self) -> lit_types.Spec:
-    return {
-        "sentence": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS, required=False)
-    }
-
-  def output_spec(self) -> lit_types.Spec:
-    return {
-        "tokens": lit_types.Tokens(),
-        "probas": lit_types.MulticlassPreds(parent="label", vocab=self.LABELS,
-                                            null_idx=0),
-        "cls_emb": lit_types.Embeddings()
-    }
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info(
-        "simple_pytorch_demo:get_wsgi_app() called with unused "
-        "args: %s", unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Normally path is a directory; if it's an archive file, download and
-  # extract to the transformers cache.
-  model_path = _MODEL_PATH.value
-  if model_path.endswith(".tar.gz"):
-    model_path = file_cache.cached_path(
-        model_path, extract_compressed_file=True)
-
-  # Load the model we defined above.
-  models = {"sst": SimpleSentimentModel(model_path)}
-  # Load SST-2 validation set from TFDS.
-  datasets = {"sst_dev": glue.SST2Data("validation")}
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/simple_tf2_demo.py b/lit_nlp/examples/simple_tf2_demo.py
deleted file mode 100644
index c606958b..00000000
--- a/lit_nlp/examples/simple_tf2_demo.py
+++ /dev/null
@@ -1,184 +0,0 @@
-r"""Code example for a custom model, using TensorFlow 2.
-
-This demo shows how to use a custom model with LIT, in just a few lines of code.
-We'll use a transformers model, with a minimal amount of code to implement the
-LIT API. Compared to models/glue_models.py, this has fewer features, but the
-code is more readable.
-
-This demo is equivalent in functionality to simple_pytorch_demo.py, but uses
-TensorFlow 2 instead of PyTorch. The models behave identically as far as LIT is
-concerned, and the implementation is quite similar - to see changes, run:
-  git diff --no-index simple_pytorch_demo.py simple_tf2_demo.py
-
-This uses the same underlying model class
-(transformers.TFAutoModelForSequenceClassification) as models/glue_models.py, so
-you can load from the same weights. To train something for this demo, you can:
-- Use quickstart_sst_demo.py, and set --model_path to somewhere durable
-- Or: Use tools/glue_trainer.py
-- Or: Use any fine-tuning code that works with transformers, such as
-https://github.com/huggingface/transformers#quick-tour-of-the-fine-tuningusage-scripts
-
-To run locally:
-  python -m lit_nlp.examples.simple_tf2_demo \
-      --port=5432 --model_path=/path/to/saved/model
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-# Use the regular GLUE data loaders, because these are very simple already.
-from lit_nlp.examples.datasets import glue
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-
-import tensorflow as tf
-import transformers
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path",
-    "https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz",
-    "Path to trained model, in standard transformers format, e.g. as "
-    "saved by model.save_pretrained() and tokenizer.save_pretrained()"
-)
-
-TFSequenceClassifierOutput = (
-    transformers.modeling_tf_outputs.TFSequenceClassifierOutput
-)
-
-
-def _from_pretrained(cls, *args, **kw):
-  """Load a transformers model in TF2, with fallback to PyTorch weights."""
-  try:
-    return cls.from_pretrained(*args, **kw)
-  except OSError as e:
-    logging.warning("Caught OSError loading model: %s", e)
-    logging.warning(
-        "Re-trying to convert from PyTorch checkpoint (from_pt=True)")
-    return cls.from_pretrained(*args, from_pt=True, **kw)
-
-
-class SimpleSentimentModel(lit_model.BatchedModel):
-  """Simple sentiment analysis model."""
-
-  LABELS = ["0", "1"]  # negative, positive
-
-  def __init__(self, model_name_or_path):
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path)
-    model_config = transformers.AutoConfig.from_pretrained(
-        model_name_or_path,
-        num_labels=2,
-        output_hidden_states=True,
-        output_attentions=True,
-    )
-    # This is a just a regular Keras model.
-    self.model = _from_pretrained(
-        transformers.TFAutoModelForSequenceClassification,
-        model_name_or_path,
-        config=model_config)
-
-  ##
-  # LIT API implementation
-  def max_minibatch_size(self):
-    # This tells lit_model.BatchedModel.predict() how to batch inputs to
-    # predict_minibatch().
-    # Alternately, you can just override predict() and handle batching yourself.
-    return 32
-
-  def predict_minibatch(self, inputs):
-    # Preprocess to ids and masks, and make the input batch.
-    encoded_input = self.tokenizer.batch_encode_plus(
-        [ex["sentence"] for ex in inputs],
-        return_tensors="tf",
-        add_special_tokens=True,
-        max_length=128,
-        padding="longest",
-        truncation="longest_first")
-
-    # Run a forward pass.
-    out: TFSequenceClassifierOutput = self.model(encoded_input, training=False)
-
-    # Post-process outputs.
-    batched_outputs = {
-        "probas": tf.nn.softmax(out.logits, axis=-1),
-        "input_ids": encoded_input["input_ids"],
-        "ntok": tf.reduce_sum(encoded_input["attention_mask"], axis=1),
-        "cls_emb": out.hidden_states[-1][:, 0],  # last layer, first token
-    }
-    # Return as NumPy for further processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    # Unbatch outputs so we get one record per input example.
-    for output in utils.unbatch_preds(detached_outputs):
-      ntok = output.pop("ntok")
-      output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-          output.pop("input_ids")[1:ntok - 1])
-      yield output
-
-  def input_spec(self) -> lit_types.Spec:
-    return {
-        "sentence": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS, required=False)
-    }
-
-  def output_spec(self) -> lit_types.Spec:
-    return {
-        "tokens": lit_types.Tokens(),
-        "probas": lit_types.MulticlassPreds(parent="label", vocab=self.LABELS,
-                                            null_idx=0),
-        "cls_emb": lit_types.Embeddings()
-    }
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("simplet_tf2_demo:get_wsgi_app() called with unused args: %s",
-                 unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Normally path is a directory; if it's an archive file, download and
-  # extract to the transformers cache.
-  model_path = _MODEL_PATH.value
-  if model_path.endswith(".tar.gz"):
-    model_path = file_cache.cached_path(
-        model_path, extract_compressed_file=True)
-
-  # Load the model we defined above.
-  models = {"sst": SimpleSentimentModel(model_path)}
-  # Load SST-2 validation set from TFDS.
-  datasets = {"sst_dev": glue.SST2Data("validation")}
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/sst_pytorch_demo.py b/lit_nlp/examples/sst_pytorch_demo.py
deleted file mode 100644
index dede8a61..00000000
--- a/lit_nlp/examples/sst_pytorch_demo.py
+++ /dev/null
@@ -1,237 +0,0 @@
-r"""Code example for a custom model, using PyTorch.
-
-This demo shows how to use a custom model with LIT, in just a few lines of code.
-We'll use a transformers model, with a minimal amount of code to implement the
-LIT API. Compared to models/glue_models.py, this has fewer features, but the
-code is more readable.
-This demo is similar in functionality to simple_tf2_demo.py, but uses PyTorch
-instead of TensorFlow 2.
-The transformers library can load weights from either,
-so you can use any saved model compatible with the underlying model class
-(AutoModelForSequenceClassification). To train something for this demo, you can:
-- Use quickstart_sst_demo.py, and set --model_path to somewhere durable
-- Or: Use tools/glue_trainer.py
-- Or: Use any fine-tuning code that works with transformers, such as
-https://github.com/huggingface/transformers#quick-tour-of-the-fine-tuningusage-scripts
-To run locally:
-  python -m lit_nlp.examples.simple_pytorch_demo \
-      --port=5432 --model_path=/path/to/saved/model
-Then navigate to localhost:5432 to access the demo UI.
-NOTE: this demo still uses TensorFlow Datasets (which depends on TensorFlow) to
-load the data. However, the output of glue.SST2Data is just NumPy arrays and
-plain Python data, and you can easily replace this with a different library or
-directly loading from CSV.
-"""
-
-from collections.abc import Sequence
-import re
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.datasets import glue
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-import torch
-import transformers
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODEL_PATH = flags.DEFINE_string(
-    "model_path", None,
-    "Path to trained model, in standard transformers format, e.g. as "
-    "saved by model.save_pretrained() and tokenizer.save_pretrained()"
-)
-
-SequenceClassifierOutput = (
-    transformers.modeling_outputs.SequenceClassifierOutput
-)
-
-
-def _from_pretrained(cls, *args, **kw):
-  """Load a transformers model in PyTorch, with fallback to TF2/Keras weights."""
-  try:
-    return cls.from_pretrained(*args, **kw)
-  except OSError as e:
-    logging.warning("Caught OSError loading model: %s", e)
-    logging.warning(
-        "Re-trying to convert from TensorFlow checkpoint (from_tf=True)")
-    return cls.from_pretrained(*args, from_tf=True, **kw)
-
-
-class SimpleSentimentModel(lit_model.BatchedModel):
-  """Simple sentiment analysis model."""
-
-  LABELS = ["0", "1"]  # negative, positive
-  compute_grads: bool = True  # if True, compute and return gradients.
-
-  def __init__(self, model_name_or_path):
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path)
-    model_config = transformers.AutoConfig.from_pretrained(
-        model_name_or_path,
-        num_labels=2,
-        output_hidden_states=True,
-        output_attentions=True,
-    )
-    # This is a just a regular PyTorch model.
-    self.model = _from_pretrained(
-        transformers.AutoModelForSequenceClassification,
-        model_name_or_path,
-        config=model_config)
-    self.model.eval()
-
-  ##
-  # LIT API implementation
-  def max_minibatch_size(self):
-    # This tells lit_model.BatchedModel.predict() how to batch inputs to
-    # predict_minibatch().
-    # Alternately, you can just override predict() and handle batching yourself.
-    return 32
-
-  def predict_minibatch(self, inputs):
-
-    # Preprocess to ids and masks, and make the input batch.
-    encoded_input = self.tokenizer.batch_encode_plus(
-        [ex["sentence"] for ex in inputs],
-        return_tensors="pt",
-        add_special_tokens=True,
-        max_length=128,
-        padding="longest",
-        truncation="longest_first")
-
-    # Check and send to cuda (GPU) if available
-    if torch.cuda.is_available():
-      self.model.cuda()
-      for tensor in encoded_input:
-        encoded_input[tensor] = encoded_input[tensor].cuda()
-
-    # Run a forward pass.
-    with torch.set_grad_enabled(self.compute_grads):
-      out: SequenceClassifierOutput = self.model(**encoded_input)
-
-    # Post-process outputs.
-    batched_outputs = {
-        "probas": torch.nn.functional.softmax(out.logits, dim=-1),
-        "input_ids": encoded_input["input_ids"],
-        "ntok": torch.sum(encoded_input["attention_mask"], dim=1),
-        "cls_emb": out.hidden_states[-1][:, 0],  # last layer, first token
-    }
-
-    # Add attention layers to batched_outputs
-    assert len(out.attentions) == self.model.config.num_hidden_layers
-    for i, layer_attention in enumerate(out.attentions):
-      batched_outputs[f"layer_{i}/attention"] = layer_attention
-
-    # Request gradients after the forward pass.
-    # Note: hidden_states[0] includes position and segment encodings, as well as
-    # subword embeddings.
-    if self.compute_grads:
-      # <torch.float32>[batch_size, num_tokens, emb_dim]
-      scalar_pred_for_gradients = torch.max(
-          batched_outputs["probas"], dim=1, keepdim=False, out=None)[0]
-      batched_outputs["input_emb_grad"] = torch.autograd.grad(
-          scalar_pred_for_gradients,
-          out.hidden_states[0],
-          grad_outputs=torch.ones_like(scalar_pred_for_gradients))[0]
-
-    # Post-process outputs.
-    # Return as NumPy for further processing.
-    detached_outputs = {
-        k: v.cpu().detach().numpy() for k, v in batched_outputs.items()}
-
-    # Unbatch outputs so we get one record per input example.
-    for output in utils.unbatch_preds(detached_outputs):
-      ntok = output.pop("ntok")
-      output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-          output.pop("input_ids")[:ntok])
-
-      # set token gradients
-      if self.compute_grads:
-        output["token_grad_sentence"] = output["input_emb_grad"][:ntok]
-
-      # Process attention.
-      for key in output:
-        if not re.match(r"layer_(\d+)/attention", key):
-          continue
-        # Select only real tokens, since most of this matrix is padding.
-        # <float32>[num_heads, max_seq_length, max_seq_length]
-        # -> <float32>[num_heads, num_tokens, num_tokens]
-        output[key] = output[key][:, :ntok, :ntok].transpose((0, 2, 1))
-        # Make a copy of this array to avoid memory leaks, since NumPy otherwise
-        # keeps a pointer around that prevents the source array from being GCed.
-        output[key] = output[key].copy()
-      yield output
-
-  def input_spec(self) -> lit_types.Spec:
-    return {
-        "sentence": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS, required=False)
-    }
-
-  def output_spec(self) -> lit_types.Spec:
-    ret = {
-        "tokens": lit_types.Tokens(),
-        "probas": lit_types.MulticlassPreds(parent="label", vocab=self.LABELS,
-                                            null_idx=0),
-        "cls_emb": lit_types.Embeddings()
-    }
-    # Gradients, if requested.
-    if self.compute_grads:
-      ret["token_grad_sentence"] = lit_types.TokenGradients(
-          align="tokens")
-
-    # Attention heads, one field for each layer.
-    for i in range(self.model.config.num_hidden_layers):
-      ret[f"layer_{i}/attention"] = lit_types.AttentionHeads(
-          align_in="tokens", align_out="tokens")
-    return ret
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("sst_pytorch_demo:get_wsgi_app() called with unused args: %s",
-                 unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Normally path is a directory; if it's an archive file, download and
-  # extract to the transformers cache.
-  model_path = _MODEL_PATH.value
-  if model_path.endswith(".tar.gz"):
-    model_path = file_cache.cached_path(
-        model_path, extract_compressed_file=True)
-
-  # Load the model we defined above.
-  models = {"sst": SimpleSentimentModel(model_path)}
-  # Load SST-2 validation set from TFDS.
-  datasets = {"sst_dev": glue.SST2Data("validation")}
-
-  # Start the LIT server. See server_flags.py for server options.
-  lit_demo = dev_server.Server(models, datasets, **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/tools/glue_trainer.py b/lit_nlp/examples/tools/glue_trainer.py
index e217f8fb..6e57af58 100644
--- a/lit_nlp/examples/tools/glue_trainer.py
+++ b/lit_nlp/examples/tools/glue_trainer.py
@@ -25,8 +25,8 @@
 from absl import flags
 from absl import logging
 
-from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import data as glue_data
+from lit_nlp.examples.glue import models as glue_models
 from lit_nlp.lib import serialize
 import tensorflow as tf
 
@@ -118,16 +118,16 @@ def main(argv: Sequence[str]) -> None:
   # TODO(lit-dev): add remaining GLUE tasks? These three cover all the major
   # features (single segment, two segment, classification, regression).
   if _TASK.value == "sst2":
-    train_data = glue.SST2Data("train")
-    val_data = glue.SST2Data("validation")
+    train_data = glue_data.SST2Data("train")
+    val_data = glue_data.SST2Data("validation")
     model = glue_models.SST2Model(_ENCODER_NAME.value)
   elif _TASK.value == "mnli":
-    train_data = glue.MNLIData("train")
-    val_data = glue.MNLIData("validation_matched")
+    train_data = glue_data.MNLIData("train")
+    val_data = glue_data.MNLIData("validation_matched")
     model = glue_models.MNLIModel(_ENCODER_NAME.value)
   elif _TASK.value == "stsb":
-    train_data = glue.STSBData("train")
-    val_data = glue.STSBData("validation")
+    train_data = glue_data.STSBData("train")
+    val_data = glue_data.STSBData("validation")
     model = glue_models.STSBModel(_ENCODER_NAME.value)
   else:
     raise ValueError(f"Unrecognized task name: '{_TASK.value:s}'")
diff --git a/pyproject.toml b/pyproject.toml
index 97e19939..13a7a967 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -103,3 +103,6 @@ test = [
 [tool.setuptools.packages.find]
 include = ["lit_nlp*"]
 exclude = ["*_test"]
+
+[tool.pytest.ini_options]
+addopts = "--import-mode=importlib"
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 1e6f3410..e4ef2044 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -106,8 +106,6 @@ LIT supports many features for classification tasks, including common metrics,
 confusion matrices, and custom thresholding via the UI. Classification is
 implemented with the `MulticlassPreds` and `CategoryLabel` types.
 
-*   For a basic example on a binary sentiment task, see
-    [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py).
 *   Models should define a `MulticlassPreds` field in their output spec with the
     `vocab=` attribute as the set of class labels, and for each example should
     return a vector of probabilities for each class.
@@ -138,7 +136,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the
     which contains numerical targets, and the model's `RegressionScore` field
     should set `parent=` to the name of this field.
 *   For an example, see the STS-B textual similarity task in
-    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
+    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 ### Multi-label classification
 
@@ -210,7 +208,7 @@ and otherwise to different parts of the input.
     [Dataset class documentation](./api.md#datasets) and the corresponding
     [Model](./api.md#models).
 *   For a more involved code example including per-token gradients, see
-    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
+    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 ### Tabular data
 
@@ -358,7 +356,7 @@ An example spec would look like:
 
 For a more concrete example that also supports multiple segments with separate
 gradients, see our
-[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py),
+[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py),
 or contact the LIT team for assistance.
 
 ### LIME
diff --git a/website/sphinx_src/docker.md b/website/sphinx_src/docker.md
index dae9c90b..f83a6f14 100644
--- a/website/sphinx_src/docker.md
+++ b/website/sphinx_src/docker.md
@@ -1,6 +1,6 @@
 # Running LIT in a Docker container
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-07-10' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-04' } *-->
 
 Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
@@ -23,12 +23,14 @@ the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py).
 You can find a reference implementation in
-[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or
+[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) or
 [`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
 
-Use the following shell commands to build the default Docker image for LIT from
-the provided `Dockerfile`, and then run a container from that image. Comments
-are provided in-line to help explain what each step does.
+Use the following shell
+[.github/workflows/ci.ymlcommands](https://github.com/PAIR-code/lit/blob/main/lit_nlp/.github/workflows/ci.ymlcommands) to build the
+default Docker image for LIT from the provided `Dockerfile`, and then run a
+container from that image. Comments are provided in-line to help explain what
+each step does.
 
 ```shell
 # Build the docker image using the -t argument to name the image. Remember to
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index 395dd90d..7ebc1ae1 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -1,6 +1,6 @@
 # Frequently Asked Questions
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-19' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -171,7 +171,5 @@ official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT's `Dataset` objects to manage training data along with standard
 training APIs (such as Keras' `model.fit()`). See
-[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py)
-and/or
-[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
+[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
 for examples.

From 2475b3bb677c8685ab9a291c490783ae2ccce5b8 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 12 Jun 2024 09:22:31 -0700
Subject: [PATCH 19/47] LIT: Remove legacy Language Model demo.

PiperOrigin-RevId: 642637693
---
 lit_nlp/examples/blank_slate_demo.py          |  25 --
 lit_nlp/examples/datasets/__init__.py         |  13 -
 lit_nlp/examples/datasets/classification.py   |  59 ---
 lit_nlp/examples/datasets/lm.py               | 131 -------
 lit_nlp/examples/lm_demo.py                   | 182 ----------
 lit_nlp/examples/models/pretrained_lms.py     | 335 ------------------
 .../models/pretrained_lms_int_test.py         |  36 --
 lit_nlp/examples/prompt_debugging/datasets.py | 126 ++++++-
 lit_nlp/examples/prompt_debugging/models.py   |   2 +-
 .../prompt_examples.jsonl                     |   0
 website/sphinx_src/api.md                     |  24 +-
 website/sphinx_src/demos.md                   |  15 -
 website/sphinx_src/docker.md                  |   3 +-
 website/sphinx_src/frontend_development.md    |   4 +-
 14 files changed, 120 insertions(+), 835 deletions(-)
 delete mode 100644 lit_nlp/examples/datasets/__init__.py
 delete mode 100644 lit_nlp/examples/datasets/classification.py
 delete mode 100644 lit_nlp/examples/datasets/lm.py
 delete mode 100644 lit_nlp/examples/lm_demo.py
 delete mode 100644 lit_nlp/examples/models/pretrained_lms.py
 delete mode 100644 lit_nlp/examples/models/pretrained_lms_int_test.py
 rename lit_nlp/examples/{datasets => prompt_debugging}/prompt_examples.jsonl (100%)

diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index 470f57a9..a4c66c4d 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -29,11 +29,8 @@
 from lit_nlp import app as lit_app
 from lit_nlp import dev_server
 from lit_nlp import server_flags
-from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.datasets import lm
 from lit_nlp.examples.glue import data as glue_data
 from lit_nlp.examples.glue import models as glue_models
-from lit_nlp.examples.models import pretrained_lms
 from lit_nlp.examples.penguin import data as penguin_data
 from lit_nlp.examples.penguin import model as penguin_model
 
@@ -85,16 +82,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       penguin_model.PenguinModel.init_spec(),
   )
 
-  # lm demo model loaders.
-  model_loaders["bert"] = (
-      pretrained_lms.BertMLM,
-      pretrained_lms.BertMLM.init_spec(),
-  )
-  model_loaders["gpt2"] = (
-      pretrained_lms.GPT2LanguageModel,
-      pretrained_lms.GPT2LanguageModel.init_spec(),
-  )
-
   datasets = {}
   dataset_loaders: lit_app.DatasetLoadersMap = {}
 
@@ -114,18 +101,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       glue_data.SST2DataForLM,
       glue_data.SST2DataForLM.init_spec(),
   )
-  dataset_loaders["imdb (lm)"] = (
-      classification.IMDBData,
-      classification.IMDBData.init_spec(),
-  )
-  dataset_loaders["plain text sentences (lm)"] = (
-      lm.PlaintextSents,
-      lm.PlaintextSents.init_spec(),
-  )
-  dataset_loaders["bwb (lm)"] = (
-      lm.BillionWordBenchmark,
-      lm.BillionWordBenchmark.init_spec(),
-  )
 
   # Start the LIT server. See server_flags.py for server options.
   lit_demo = dev_server.Server(
diff --git a/lit_nlp/examples/datasets/__init__.py b/lit_nlp/examples/datasets/__init__.py
deleted file mode 100644
index c6334245..00000000
--- a/lit_nlp/examples/datasets/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/lit_nlp/examples/datasets/classification.py b/lit_nlp/examples/datasets/classification.py
deleted file mode 100644
index 0d1e1df3..00000000
--- a/lit_nlp/examples/datasets/classification.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Text classification datasets, including single- and two-sentence tasks."""
-from typing import Optional
-
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-import tensorflow_datasets as tfds
-
-
-def load_tfds(*args, **kw):
-  """Load from TFDS."""
-  # Materialize to NumPy arrays.
-  # This also ensures compatibility with TF1.x non-eager mode, which doesn't
-  # support direct iteration over a tf.data.Dataset.
-  return list(
-      tfds.as_numpy(tfds.load(*args, download=True, try_gcs=True, **kw)))
-
-
-class IMDBData(lit_dataset.Dataset):
-  """IMDB reviews dataset; see http://ai.stanford.edu/~amaas/data/sentiment/."""
-
-  LABELS = ["0", "1"]
-  AVAILABLE_SPLITS = ["test", "train", "unsupervised"]
-
-  def __init__(
-      self, split="test", max_seq_len=500, max_examples: Optional[int] = None
-  ):
-    """Dataset constructor, loads the data into memory."""
-    raw_examples = load_tfds("imdb_reviews", split=split)
-    self._examples = []  # populate this with data records
-    for record in raw_examples[:max_examples]:
-      # format and truncate from the end to max_seq_len tokens.
-      truncated_text = " ".join(
-          record["text"]
-          .decode("utf-8")
-          .replace("<br />", "")
-          .split()[-max_seq_len:]
-      )
-      self._examples.append({
-          "text": truncated_text,
-          "label": self.LABELS[record["label"]],
-      })
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        "split": lit_types.CategoryLabel(vocab=cls.AVAILABLE_SPLITS),
-        "max_seq_len": lit_types.Integer(default=500, min_val=1, max_val=1024),
-        "max_examples": lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
-
-  def spec(self) -> lit_types.Spec:
-    """Dataset spec, which should match the model"s input_spec()."""
-    return {
-        "text": lit_types.TextSegment(),
-        "label": lit_types.CategoryLabel(vocab=self.LABELS),
-    }
-
diff --git a/lit_nlp/examples/datasets/lm.py b/lit_nlp/examples/datasets/lm.py
deleted file mode 100644
index d2292f44..00000000
--- a/lit_nlp/examples/datasets/lm.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""Language modeling datasets."""
-
-import copy
-import json
-import os
-import glob
-from typing import Optional
-
-from absl import logging
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-import tensorflow_datasets as tfds
-
-SAMPLE_DATA_DIR = os.path.dirname(__file__)
-
-
-class PlaintextSents(lit_dataset.Dataset):
-  """Load sentences from a flat text file."""
-
-  def __init__(
-      self,
-      path_or_glob: str,
-      skiplines: int = 0,
-      max_examples: Optional[int] = None,
-      field_name: str = 'text',
-  ):
-    self.field_name = field_name
-    self._examples = self.load_datapoints(path_or_glob, skiplines=skiplines)[
-        :max_examples
-    ]
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    default_path = ''
-
-    return {
-        'path_or_glob': lit_types.String(
-            default=default_path, required=False
-        ),
-        'skiplines': lit_types.Integer(default=0, max_val=25),
-        'max_examples': lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
-
-  def load_datapoints(self, path_or_glob: str, skiplines: int = 0):
-    examples = []
-    for path in glob.glob(path_or_glob):
-      with open(path) as fd:
-        for i, line in enumerate(fd):
-          if i < skiplines:  # skip header lines, if necessary
-            continue
-          line = line.strip()
-          if line:  # skip blank lines, these are usually document breaks
-            examples.append({self.field_name: line})
-    return examples
-
-  def load(self, path: str):
-    return lit_dataset.Dataset(base=self, examples=self.load_datapoints(path))
-
-  def spec(self) -> lit_types.Spec:
-    """Should match MLM's input_spec()."""
-    return {self.field_name: lit_types.TextSegment()}
-
-
-class PromptExamples(lit_dataset.Dataset):
-  """Prompt examples for modern LMs."""
-
-  SAMPLE_DATA_PATH = os.path.join(SAMPLE_DATA_DIR, 'prompt_examples.jsonl')
-
-  def load_datapoints(self, path: str):
-    if not path:
-      logging.warn(
-          'Empty path to PromptExamples.load_datapoints(). Returning empty'
-          ' dataset.'
-      )
-      return []
-
-    default_ex_values = {
-        k: copy.deepcopy(field_spec.default)
-        for k, field_spec in self.spec().items()
-    }
-
-    examples = []
-    with open(path) as fd:
-      for line in fd:
-        examples.append(default_ex_values | json.loads(line))
-
-    return examples
-
-  def __init__(self, path: str):
-    self._examples = self.load_datapoints(path)
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        'source': lit_types.CategoryLabel(),
-        'prompt': lit_types.TextSegment(),
-        'target': lit_types.TextSegment(),
-    }
-
-  def load(self, path: str):
-    return lit_dataset.Dataset(base=self, examples=self.load_datapoints(path))
-
-
-class BillionWordBenchmark(lit_dataset.Dataset):
-  """Billion Word Benchmark (lm1b); see http://www.statmt.org/lm-benchmark/."""
-
-  AVAILABLE_SPLITS = ['test', 'train']
-
-  def __init__(self, split: str = 'train', max_examples: Optional[int] = None):
-    ds = tfds.load('lm1b', split=split)
-    if max_examples is not None:
-      # Normally we can just slice the resulting dataset, but lm1b is very large
-      # so we can use ds.take() to only load a portion of it.
-      ds = ds.take(max_examples)
-    raw_examples = list(tfds.as_numpy(ds))
-    self._examples = [{
-        'text': ex['text'].decode('utf-8')
-    } for ex in raw_examples]
-
-  @classmethod
-  def init_spec(cls) -> lit_types.Spec:
-    return {
-        'split': lit_types.CategoryLabel(vocab=cls.AVAILABLE_SPLITS),
-        'max_examples': lit_types.Integer(
-            default=1000, min_val=0, max_val=10_000, required=False
-        ),
-    }
-
-  def spec(self) -> lit_types.Spec:
-    return {'text': lit_types.TextSegment()}
diff --git a/lit_nlp/examples/lm_demo.py b/lit_nlp/examples/lm_demo.py
deleted file mode 100644
index 5af2156b..00000000
--- a/lit_nlp/examples/lm_demo.py
+++ /dev/null
@@ -1,182 +0,0 @@
-r"""Example demo loading pre-trained language models.
-
-Currently supports the following model types:
-- BERT (bert-*) as a masked language model
-- GPT-2 (gpt2* or distilgpt2) as a left-to-right language model
-
-To run locally:
-  python -m lit_nlp.examples.lm_demo \
-      --models=bert-base-uncased --port=5432
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-from lit_nlp import app as lit_app
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.components import word_replacer
-from lit_nlp.examples.datasets import classification
-from lit_nlp.examples.datasets import lm
-from lit_nlp.examples.glue import data as glue_data
-from lit_nlp.examples.models import pretrained_lms
-
-# NOTE: additional flags defined in server_flags.py
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default("development_demo", True)
-
-_MODELS = flags.DEFINE_list(
-    "models",
-    [
-        "bert-base-uncased:https://storage.googleapis.com/what-if-tool-resources/lit-models/bert-base-uncased.tar.gz",
-        "gpt2:https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz",
-    ],
-    "Models to load, as <name>:<path>. Currently supports variants of BERT and"
-    " GPT-2.",
-)
-
-_TOP_K = flags.DEFINE_integer(
-    "top_k", 10, "Rank to which the output distribution is pruned."
-)
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples",
-    1000,
-    (
-        "Maximum number of examples to load from each evaluation set. Set to"
-        " None to load the full set."
-    ),
-)
-
-_LOAD_BWB = flags.DEFINE_bool(
-    "load_bwb",
-    False,
-    (
-        "If true, will load examples from the Billion Word Benchmark dataset."
-        " This may download a lot of data the first time you run it, so disable"
-        " by default for the quick-start example."
-    ),
-)
-
-# Custom frontend layout; see api/layout.py
-modules = layout.LitModuleName
-LM_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        "Main": [
-            modules.EmbeddingsModule,
-            modules.DataTableModule,
-            modules.DatapointEditorModule,
-        ]
-    },
-    lower={
-        "Predictions": [
-            modules.LanguageModelPredictionModule,
-            modules.ConfusionMatrixModule,
-        ],
-        "Counterfactuals": [modules.GeneratorModule],
-    },
-    description="Custom layout for language models.",
-)
-
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {"lm": LM_LAYOUT}
-
-# You can also change this via URL param e.g. localhost:5432/?layout=default
-FLAGS.set_default("default_layout", "lm")
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  """Return WSGI app for container-hosted demos."""
-  FLAGS.set_default("server_type", "external")
-  FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info("lm_demo:get_wsgi_app() called with unused args: %s", unused)
-  return main([])
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  ##
-  # Load models, according to the --models flag.
-  models = {}
-  for model_string in _MODELS.value:
-    # Only split on the first ':', because path may be a URL
-    # containing 'https://'
-    model_name, path = model_string.split(":", 1)
-    logging.info("Loading model '%s' from '%s'", model_name, path)
-    if model_name.startswith("bert-"):
-      models[model_name] = pretrained_lms.BertMLM(path, top_k=_TOP_K.value)
-    elif model_name.startswith("gpt2") or model_name in ["distilgpt2"]:
-      models[model_name] = pretrained_lms.GPT2LanguageModel(
-          path, top_k=_TOP_K.value
-      )
-    else:
-      raise ValueError(
-          f"Unsupported model name '{model_name}' from path '{path}'"
-      )
-
-  datasets = {
-      # Single sentences from movie reviews (SST dev set).
-      "sst_dev": glue_data.SST2Data("validation").remap({"sentence": "text"}),
-      # Longer passages from movie reviews (IMDB dataset, test split).
-      "imdb_train": classification.IMDBData("test"),
-      # Empty dataset, if you just want to type sentences into the UI.
-      "blank": lm.PlaintextSents(""),
-  }
-
-  dataset_loaders: lit_app.DatasetLoadersMap = {
-      "sst_dev": (glue_data.SST2DataForLM, glue_data.SST2DataForLM.init_spec()),
-      "imdb_train": (
-          classification.IMDBData,
-          classification.IMDBData.init_spec(),
-      ),
-      "plain_text_sentences": (
-          lm.PlaintextSents,
-          lm.PlaintextSents.init_spec(),
-      ),
-  }
-
-  # Guard this with a flag, because TFDS will download and process 1.67 GB
-  # of data if you haven't loaded `lm1b` before.
-  if _LOAD_BWB.value:
-    # A few sentences from the Billion Word Benchmark (Chelba et al. 2013).
-    datasets["bwb"] = lm.BillionWordBenchmark(
-        "train", max_examples=_MAX_EXAMPLES.value
-    )
-    dataset_loaders["bwb"] = (
-        lm.BillionWordBenchmark,
-        lm.BillionWordBenchmark.init_spec(),
-    )
-
-  for name in datasets:
-    datasets[name] = datasets[name].slice[: _MAX_EXAMPLES.value]
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-
-  generators = {"word_replacer": word_replacer.WordReplacer()}
-
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      generators=generators,
-      layouts=CUSTOM_LAYOUTS,
-      dataset_loaders=dataset_loaders,
-      **server_flags.get_flags(),
-  )
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/models/pretrained_lms.py b/lit_nlp/examples/models/pretrained_lms.py
deleted file mode 100644
index f9194e11..00000000
--- a/lit_nlp/examples/models/pretrained_lms.py
+++ /dev/null
@@ -1,335 +0,0 @@
-"""Wrapper for HuggingFace models in LIT.
-
-Includes BERT masked LM, GPT-2, and T5.
-
-This wrapper loads a model into memory and implements the a number of helper
-functions to predict a batch of examples and extract information such as
-hidden states and attention.
-"""
-
-import re
-
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import model_utils
-from lit_nlp.lib import file_cache
-from lit_nlp.lib import utils
-import numpy as np
-import tensorflow as tf
-import transformers
-
-
-class BertMLM(lit_model.BatchedModel):
-  """BERT masked LM using Huggingface Transformers and TensorFlow 2."""
-
-  MASK_TOKEN = "[MASK]"
-
-  @property
-  def max_seq_length(self):
-    return self.model.config.max_position_embeddings
-
-  @classmethod
-  def init_spec(cls) -> lit_model.Spec:
-    return {
-        "model_name_or_path": lit_types.String(default="bert-base-uncased"),
-        "top_k": lit_types.Integer(default=10, min_val=1, max_val=25),
-    }
-
-  def __init__(self, model_name_or_path="bert-base-uncased", top_k=10):
-    super().__init__()
-
-    # Normally path is a directory; if it's an archive file, download and
-    # extract to the transformers cache.
-    if model_name_or_path.endswith(".tar.gz"):
-      model_name_or_path = file_cache.cached_path(
-          model_name_or_path, extract_compressed_file=True
-      )
-
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path, use_fast=False
-    )
-    # TODO(lit-dev): switch to TFBertForPreTraining to get the next-sentence
-    # prediction head as well.
-    self.model = model_utils.load_pretrained(
-        transformers.TFBertForMaskedLM,
-        model_name_or_path,
-        output_hidden_states=True,
-        output_attentions=True,
-    )
-    self.top_k = top_k
-
-  # TODO(lit-dev): break this out as a helper function, write some tests,
-  # and de-duplicate code with the other text generation functions.
-  def _get_topk_tokens(
-      self, scores: np.ndarray
-  ) -> list[list[tuple[str, float]]]:
-    """Convert raw scores to top-k token predictions."""
-    # scores is [num_tokens, vocab_size]
-    # Find the vocab indices of top k predictions, at each token.
-    # np.argpartition is faster than a full argsort for k << V,
-    # but we need to sort the output after slicing (see below).
-    index_array = np.argpartition(scores, -self.top_k, axis=1)[:, -self.top_k :]
-    # These are each [num_tokens, tok_k]
-    top_tokens = [
-        self.tokenizer.convert_ids_to_tokens(idxs) for idxs in index_array
-    ]
-    top_scores = np.take_along_axis(scores, index_array, axis=1)
-    # Convert to a list of lists of (token, score) pairs,
-    # where inner lists are sorted in descending order of score.
-    return [
-        sorted(list(zip(toks, scores)), key=lambda ab: -ab[1])
-        for toks, scores in zip(top_tokens, top_scores)
-    ]
-    # TODO(lit-dev): consider returning indices and a vocab, since repeating
-    # strings is slow and redundant.
-
-  def _postprocess(self, output: dict[str, np.ndarray]):
-    """Postprocess, modifying output dict in-place."""
-    # Slice to remove padding, omitting initial [CLS] and final [SEP]
-    slicer = slice(1, output.pop("ntok") - 1)
-    output["tokens"] = self.tokenizer.convert_ids_to_tokens(
-        output.pop("input_ids")[slicer]
-    )
-    probas = output.pop("probas")
-
-    # Predictions at every position, regardless of masking.
-    output["pred_tokens"] = self._get_topk_tokens(probas[slicer])  # pytype: disable=container-type-mismatch
-
-    return output
-
-  ##
-  # LIT API implementations
-  def max_minibatch_size(self) -> int:
-    # The lit.Model base class handles batching automatically in the
-    # implementation of predict(), and uses this value as the batch size.
-    return 8
-
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    # If input has a 'tokens' field, use that. Otherwise tokenize the text.
-    tokenized_texts = [
-        ex.get("tokens") or self.tokenizer.tokenize(ex["text"]) for ex in inputs
-    ]
-    encoded_input = model_utils.batch_encode_pretokenized(
-        self.tokenizer, tokenized_texts
-    )
-
-    # out.logits is a single tensor
-    #    <float32>[batch_size, num_tokens, vocab_size]
-    # out.hidden_states is a list of num_layers + 1 tensors, each
-    #    <float32>[batch_size, num_tokens, h_dim]
-    out: transformers.modeling_tf_outputs.TFMaskedLMOutput = self.model(
-        encoded_input
-    )
-    batched_outputs = {
-        "probas": tf.nn.softmax(out.logits, axis=-1).numpy(),
-        "input_ids": encoded_input["input_ids"].numpy(),
-        "ntok": tf.reduce_sum(encoded_input["attention_mask"], axis=1).numpy(),
-        # last layer, first token
-        "cls_emb": out.hidden_states[-1][:, 0].numpy(),
-    }
-    # List of dicts, one per example.
-    unbatched_outputs = utils.unbatch_preds(batched_outputs)
-    # Postprocess to remove padding and decode predictions.
-    return map(self._postprocess, unbatched_outputs)
-
-  def load(self, model_name_or_path):
-    """Dynamically load a new BertMLM model given a model name."""
-    return BertMLM(model_name_or_path, self.top_k)
-
-  def input_spec(self):
-    return {
-        "text": lit_types.TextSegment(),
-        "tokens": lit_types.Tokens(mask_token="[MASK]", required=False),
-    }
-
-  def output_spec(self):
-    return {
-        "tokens": lit_types.Tokens(parent="text"),
-        "pred_tokens": lit_types.TokenTopKPreds(align="tokens"),
-        "cls_emb": lit_types.Embeddings(),
-    }
-
-
-# TODO(lit-dev): merge with below, inherit from HFBaseModel.
-class GPT2LanguageModel(lit_model.BatchedModel):
-  """Wrapper for a Huggingface Transformers GPT-2 model.
-
-  This class loads a tokenizer and model using the Huggingface library and
-  provides the LIT-required functions plus additional helper functions to
-  convert and clean tokens and to compute the top_k predictions from logits.
-  """
-
-  @property
-  def num_layers(self):
-    return self.model.config.n_layer
-
-  @classmethod
-  def init_spec(cls) -> lit_model.Spec:
-    return {
-        "model_name_or_path": lit_types.String(default="gpt2"),
-        "top_k": lit_types.Integer(default=10, min_val=1, max_val=25),
-    }
-
-  def __init__(self, model_name_or_path="gpt2", top_k=10):
-    """Constructor for GPT2LanguageModel.
-
-    Args:
-      model_name_or_path: gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2,
-        etc.
-      top_k: How many predictions to prune.
-    """
-    super().__init__()
-
-    # Normally path is a directory; if it's an archive file, download and
-    # extract to the transformers cache.
-    if model_name_or_path.endswith(".tar.gz"):
-      model_name_or_path = file_cache.cached_path(
-          model_name_or_path, extract_compressed_file=True
-      )
-
-    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path, use_fast=False
-    )
-    # Set this after init, as if pad_token= is passed to
-    # AutoTokenizer.from_pretrained() above it will create a new token with
-    # with id = max_vocab_length and cause out-of-bounds errors in
-    # the embedding lookup.
-    self.tokenizer.pad_token = self.tokenizer.eos_token
-    self.model = transformers.TFGPT2LMHeadModel.from_pretrained(
-        model_name_or_path, output_hidden_states=True, output_attentions=True
-    )
-    self.top_k = top_k
-
-  @staticmethod
-  def clean_bpe_token(tok):
-    if not tok.startswith("Ġ"):
-      return "_" + tok
-    else:
-      return tok.replace("Ġ", "")
-
-  def ids_to_clean_tokens(self, ids):
-    tokens = self.tokenizer.convert_ids_to_tokens(ids)
-    return [self.clean_bpe_token(t) for t in tokens]
-
-  def _pred(self, encoded_inputs):
-    """Predicts one batch of tokenized text.
-
-    Also performs some batch-level post-processing in TF.
-    Single-example postprocessing is done in _postprocess(), and operates on
-    numpy arrays.
-
-    Each prediction has the following returns:
-    logits: tf.Tensor (batch_size, sequence_length, config.vocab_size).
-    past: list[tf.Tensor] of length config.n_layers with each tensor shape
-             (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
-    states: Tuple of tf.Tensor (one for embeddings + one for each layer),
-            with shape (batch_size, sequence_length, hidden_size).
-    attentions: Tuple of tf.Tensor (one for each layer) with shape
-                (batch_size, num_heads, sequence_length, sequence_length)
-    Within this function, we combine each Tuple/List into a single Tensor.
-
-    Args:
-      encoded_inputs: output of self.tokenizer()
-
-    Returns:
-      payload: Dictionary with items described above, each as single Tensor.
-    """
-    out: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast = self.model(
-        encoded_inputs["input_ids"]
-    )
-
-    model_probs = tf.nn.softmax(out.logits, axis=-1)
-    top_k = tf.math.top_k(model_probs, k=self.top_k, sorted=True, name=None)
-    batched_outputs = {
-        "input_ids": encoded_inputs["input_ids"],
-        "ntok": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1),
-        "top_k_indices": top_k.indices,
-        "top_k_probs": top_k.values,
-    }
-
-    # Convert representations for each layer from tuples to single Tensor.
-    for i in range(len(out.attentions)):
-      batched_outputs[f"layer_{i+1:d}_attention"] = out.attentions[i]
-    for i in range(len(out.hidden_states)):
-      batched_outputs[f"layer_{i:d}_avg_embedding"] = tf.math.reduce_mean(
-          out.hidden_states[i], axis=1
-      )
-
-    return batched_outputs
-
-  def _postprocess(self, preds):
-    """Post-process single-example preds. Operates on numpy arrays."""
-    ntok = preds.pop("ntok")
-    ids = preds.pop("input_ids")[:ntok]
-    preds["tokens"] = self.ids_to_clean_tokens(ids)
-
-    # Decode predicted top-k tokens.
-    # token_topk_preds will be a list[list[(word, prob)]]
-    # Initialize prediction for 0th token as N/A.
-    token_topk_preds = [[("N/A", 1.0)]]
-    pred_ids = preds.pop("top_k_indices")[:ntok]  # <int>[num_tokens, k]
-    pred_probs = preds.pop("top_k_probs")[:ntok]  # <float32>[num_tokens, k]
-    for token_pred_ids, token_pred_probs in zip(pred_ids, pred_probs):
-      token_pred_words = self.ids_to_clean_tokens(token_pred_ids)
-      token_topk_preds.append(list(zip(token_pred_words, token_pred_probs)))
-    preds["pred_tokens"] = token_topk_preds
-
-    # Process attention.
-    for key in preds:
-      if not re.match(r"layer_(\d+)/attention", key):
-        continue
-      # Select only real tokens, since most of this matrix is padding.
-      # <float32>[num_heads, max_seq_length, max_seq_length]
-      # -> <float32>[num_heads, num_tokens, num_tokens]
-      preds[key] = preds[key][:, :ntok, :ntok].transpose((0, 2, 1))
-      # Make a copy of this array to avoid memory leaks, since NumPy otherwise
-      # keeps a pointer around that prevents the source array from being GCed.
-      preds[key] = preds[key].copy()
-
-    return preds
-
-  ##
-  # LIT API implementations
-  def max_minibatch_size(self) -> int:
-    # The BatchedModel base class handles batching automatically in the
-    # implementation of predict(), and uses this value as the batch size.
-    return 6
-
-  def predict_minibatch(self, inputs):
-    """Predict on a single minibatch of examples."""
-    # Preprocess inputs.
-    texts = [ex["text"] for ex in inputs]
-    encoded_inputs = self.tokenizer(
-        texts,
-        return_tensors="tf",
-        add_special_tokens=True,
-        padding="longest",
-        truncation="longest_first",
-    )
-
-    # Get the predictions.
-    batched_outputs = self._pred(encoded_inputs)
-    # Convert to numpy for post-processing.
-    detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()}
-    # Split up batched outputs, then post-process each example.
-    unbatched_outputs = utils.unbatch_preds(detached_outputs)
-    return map(self._postprocess, unbatched_outputs)
-
-  def input_spec(self):
-    return {"text": lit_types.TextSegment()}
-
-  def output_spec(self):
-    spec = {
-        # the "parent" keyword tells LIT which field in the input spec we should
-        # compare this to when computing metrics.
-        "pred_tokens": lit_types.TokenTopKPreds(align="tokens"),
-        "tokens": lit_types.Tokens(parent="text"),  # all tokens
-    }
-    # Add attention and embeddings from each layer.
-    for i in range(self.num_layers):
-      spec[f"layer_{i+1:d}_attention"] = lit_types.AttentionHeads(
-          align_in="tokens", align_out="tokens"
-      )
-      spec[f"layer_{i:d}_avg_embedding"] = lit_types.Embeddings()
-    return spec
diff --git a/lit_nlp/examples/models/pretrained_lms_int_test.py b/lit_nlp/examples/models/pretrained_lms_int_test.py
deleted file mode 100644
index 187a335c..00000000
--- a/lit_nlp/examples/models/pretrained_lms_int_test.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Integration tests for pretrained_lms."""
-
-from absl.testing import absltest
-from lit_nlp.examples.models import pretrained_lms
-
-
-class PretrainedLmsIntTest(absltest.TestCase):
-  """Test that model classes can predict."""
-
-  def test_bertmlm(self):
-    # Run prediction to ensure no failure.
-    model_path = "https://storage.googleapis.com/what-if-tool-resources/lit-models/bert-base-uncased.tar.gz"
-    model = pretrained_lms.BertMLM(model_path)
-    model_in = [{"text": "test text", "tokens": ["test", "[MASK]"]}]
-    model_out = list(model.predict(model_in))
-
-    # Sanity-check entries exist in output.
-    self.assertLen(model_out, 1)
-    self.assertIn("pred_tokens", model_out[0])
-    self.assertIn("cls_emb", model_out[0])
-
-  def test_gpt2(self):
-    # Run prediction to ensure no failure.
-    model_path = "https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz"
-    model = pretrained_lms.GPT2LanguageModel(model_path)
-    model_in = [{"text": "test text"}, {"text": "longer test text"}]
-    model_out = list(model.predict(model_in))
-
-    # Sanity-check output vs output spec.
-    self.assertLen(model_out, 2)
-    for key in model.output_spec().keys():
-      self.assertIn(key, model_out[0].keys())
-
-
-if __name__ == "__main__":
-  absltest.main()
diff --git a/lit_nlp/examples/prompt_debugging/datasets.py b/lit_nlp/examples/prompt_debugging/datasets.py
index 4681c951..22157704 100644
--- a/lit_nlp/examples/prompt_debugging/datasets.py
+++ b/lit_nlp/examples/prompt_debugging/datasets.py
@@ -1,25 +1,115 @@
 """Methods for configuring prompt debugging datasets."""
 
 from collections.abc import Mapping, Sequence
+import copy
 import functools
+import json
+import os
 import re
 from typing import Optional
 
 from absl import logging
 from lit_nlp import app as lit_app
 from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.examples.datasets import lm as lm_data
+from lit_nlp.api import types as lit_types
 
-
-DEFAULT_DATASETS = ["sample_prompts"]
+SAMPLE_DATA_DIR = os.path.dirname(__file__)
+DEFAULT_DATASETS = ['sample_prompts']
 DEFAULT_MAX_EXAMPLES = 1000
 
+
+class PlaintextSents(lit_dataset.Dataset):
+  """Load sentences from a flat text file."""
+
+  def __init__(
+      self,
+      path_or_glob: str,
+      skiplines: int = 0,
+      max_examples: Optional[int] = None,
+      field_name: str = 'text',
+  ):
+    self.field_name = field_name
+    self._examples = self.load_datapoints(path_or_glob, skiplines=skiplines)[
+        :max_examples
+    ]
+
+  @classmethod
+  def init_spec(cls) -> lit_types.Spec:
+    default_path = ''
+
+    return {
+        'path_or_glob': lit_types.String(default=default_path, required=False),
+        'skiplines': lit_types.Integer(default=0, max_val=25),
+        'max_examples': lit_types.Integer(
+            default=1000, min_val=0, max_val=10_000, required=False
+        ),
+    }
+
+  def load_datapoints(self, path_or_glob: str, skiplines: int = 0):
+    examples = []
+    for path in glob.glob(path_or_glob):
+      with open(path) as fd:
+        for i, line in enumerate(fd):
+          if i < skiplines:  # skip header lines, if necessary
+            continue
+          line = line.strip()
+          if line:  # skip blank lines, these are usually document breaks
+            examples.append({self.field_name: line})
+    return examples
+
+  def load(self, path: str):
+    return lit_dataset.Dataset(base=self, examples=self.load_datapoints(path))
+
+  def spec(self) -> lit_types.Spec:
+    """Should match MLM's input_spec()."""
+    return {self.field_name: lit_types.TextSegment()}
+
+
+class PromptExamples(lit_dataset.Dataset):
+  """Prompt examples for modern LMs."""
+
+  SAMPLE_DATA_PATH = os.path.join(SAMPLE_DATA_DIR, 'prompt_examples.jsonl')
+
+  def load_datapoints(self, path: str):
+    if not path:
+      logging.warn(
+          'Empty path to PromptExamples.load_datapoints(). Returning empty'
+          ' dataset.'
+      )
+      return []
+
+    default_ex_values = {
+        k: copy.deepcopy(field_spec.default)
+        for k, field_spec in self.spec().items()
+    }
+
+    examples = []
+    with open(path) as fd:
+      for line in fd:
+        examples.append(default_ex_values | json.loads(line))
+
+    return examples
+
+  def __init__(self, path: str):
+    self._examples = self.load_datapoints(path)
+
+  def spec(self) -> lit_types.Spec:
+    return {
+        'source': lit_types.CategoryLabel(),
+        'prompt': lit_types.TextSegment(),
+        'target': lit_types.TextSegment(),
+    }
+
+  def load(self, path: str):
+    return lit_dataset.Dataset(base=self, examples=self.load_datapoints(path))
+
+
 _plaintext_prompts = functools.partial(  # pylint: disable=invalid-name
-    lm_data.PlaintextSents, field_name="prompt"
+    PlaintextSents, field_name='prompt'
 )
 # Hack: normally dataset loaders are a class object which has a __name__,
 # rather than a functools.partial
-_plaintext_prompts.__name__ = "PlaintextSents"
+_plaintext_prompts.__name__ = 'PlaintextSents'
 
 
 def get_datasets(
@@ -44,22 +134,22 @@ def get_datasets(
 
   datasets: dict[str, lit_dataset.Dataset] = {}
   for dataset_string in datasets_config:
-    if dataset_string == "sample_prompts":
-      dataset_name = "sample_prompts"
-      path = lm_data.PromptExamples.SAMPLE_DATA_PATH
+    if dataset_string == 'sample_prompts':
+      dataset_name = 'sample_prompts'
+      path = PromptExamples.SAMPLE_DATA_PATH
     else:
       # Only split on the first ':', because path may be a URL
       # containing 'https://'
-      dataset_name, path = dataset_string.split(":", 1)
+      dataset_name, path = dataset_string.split(':', 1)
     logging.info("Loading dataset '%s' from '%s'", dataset_name, path)
 
-    if path.endswith(".jsonl"):
-      datasets[dataset_name] = lm_data.PromptExamples(path)
+    if path.endswith('.jsonl'):
+      datasets[dataset_name] = PromptExamples(path)
     # .txt or .txt-#####-of-#####
-    elif path.endswith(".txt") or re.match(r".*\.txt-\d{5}-of-\d{5}$", path):
+    elif path.endswith('.txt') or re.match(r'.*\.txt-\d{5}-of-\d{5}$', path):
       datasets[dataset_name] = _plaintext_prompts(path)
     else:
-      raise ValueError(f"Unsupported dataset format for {dataset_string}")
+      raise ValueError(f'Unsupported dataset format for {dataset_string}')
 
   for name in datasets:
     datasets[name] = datasets[name].slice[:max_examples]
@@ -70,12 +160,12 @@ def get_datasets(
 
 def get_dataset_loaders() -> lit_app.DatasetLoadersMap:
   return {
-      "jsonl_examples": (
-          lm_data.PromptExamples,
-          lm_data.PromptExamples.init_spec(),
+      'jsonl_examples': (
+          PromptExamples,
+          PromptExamples.init_spec(),
       ),
-      "plaintext_inputs": (
+      'plaintext_inputs': (
           _plaintext_prompts,
-          lm_data.PlaintextSents.init_spec(),
+          PlaintextSents.init_spec(),
       ),
   }
diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py
index 6f2347f0..2c72c9b6 100644
--- a/lit_nlp/examples/prompt_debugging/models.py
+++ b/lit_nlp/examples/prompt_debugging/models.py
@@ -9,7 +9,7 @@
 from lit_nlp.lib import file_cache
 
 
-DEFAULT_BATCH_SIZE = 4
+DEFAULT_BATCH_SIZE = 1
 DEFAULT_DL_FRAMEWORK = "kerasnlp"
 DEFAULT_DL_RUNTIME = "tensorflow"
 DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"],
diff --git a/lit_nlp/examples/datasets/prompt_examples.jsonl b/lit_nlp/examples/prompt_debugging/prompt_examples.jsonl
similarity index 100%
rename from lit_nlp/examples/datasets/prompt_examples.jsonl
rename to lit_nlp/examples/prompt_debugging/prompt_examples.jsonl
diff --git a/website/sphinx_src/api.md b/website/sphinx_src/api.md
index e734be39..59c3be20 100644
--- a/website/sphinx_src/api.md
+++ b/website/sphinx_src/api.md
@@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see
 *   A field that appears in _both_ the model's input and output specs is assumed
     to represent the same value. This pattern is used for model-based input
     manipulation. For example, a
-    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
+    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
     might output `'tokens': lit_types.Tokens(...)`, and accept as (optional)
     input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability
     component could take output from the former, swap one or more tokens (e.g.
@@ -886,22 +886,16 @@ You can specify custom web app layouts from Python via the `layouts=` attribute.
 The value should be a `Mapping[str, LitCanonicalLayout]`, such as:
 
 ```python
-LM_LAYOUT = layout.LitCanonicalLayout(
+PENGUIN_LAYOUT = layout.LitCanonicalLayout(
     upper={
-        "Main": [
-            modules.EmbeddingsModule,
+        'Main': [
+            modules.DiveModule,
             modules.DataTableModule,
             modules.DatapointEditorModule,
         ]
     },
-    lower={
-        "Predictions": [
-            modules.LanguageModelPredictionModule,
-            modules.ConfusionMatrixModule,
-        ],
-        "Counterfactuals": [modules.GeneratorModule],
-    },
-    description="Custom layout for language models.",
+    lower=layout.STANDARD_LAYOUT.lower,
+    description='Custom layout for the Palmer Penguins demo.',
 )
 ```
 
@@ -912,14 +906,12 @@ lit_demo = dev_server.Server(
     models,
     datasets,
     # other args...
-    layouts={"lm": LM_LAYOUT},
+    layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT},
+    default_layout='penguins',
     **server_flags.get_flags())
 return lit_demo.serve()
 ```
 
-For a full example, see
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
-
 You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 8617519d..89812af9 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -82,21 +82,6 @@ Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
 Generative AI Toolkit.
 
-## Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} -->
-
-### BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \
-**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py)
-
-*   Compare multiple BERT and GPT-2 models side-by-side on a variety of
-    plain-text corpora.
-*   LM visualization supports different modes:
-    *   BERT masked language model: click-to-mask, and query model at that
-        position.
-    *   GPT-2 shows left-to-right hypotheses for each target token.
-*   Embedding projector to show latent space of the model.
-
 --------------------------------------------------------------------------------
 
 ## Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} -->
diff --git a/website/sphinx_src/docker.md b/website/sphinx_src/docker.md
index f83a6f14..c6b501d4 100644
--- a/website/sphinx_src/docker.md
+++ b/website/sphinx_src/docker.md
@@ -23,8 +23,7 @@ the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py).
 You can find a reference implementation in
-[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) or
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
+[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 Use the following shell
 [.github/workflows/ci.ymlcommands](https://github.com/PAIR-code/lit/blob/main/lit_nlp/.github/workflows/ci.ymlcommands) to build the
diff --git a/website/sphinx_src/frontend_development.md b/website/sphinx_src/frontend_development.md
index a51aed81..6f399a43 100644
--- a/website/sphinx_src/frontend_development.md
+++ b/website/sphinx_src/frontend_development.md
@@ -76,8 +76,8 @@ pre-configured layouts in
 
 You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT
 instance by defining one or more `LitCanonicalLayout` instances and passing them
-to the server. For an example, see `CUSTOM_LAYOUTS` in
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
+to the server. For an example, see
+[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py).
 
 Note: The pre-configured layouts are added to every `LitApp` instance using
 [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where

From a59641c014b17409e8e5cfdac1cc1e6916d6da15 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 12 Jun 2024 11:18:35 -0700
Subject: [PATCH 20/47] LIT: Move TyDI data into examples/tydi

PiperOrigin-RevId: 642676042
---
 .../{datasets/question_answering.py => tydi/data.py}  |  0
 lit_nlp/examples/tydi/demo.py                         | 11 +++++------
 2 files changed, 5 insertions(+), 6 deletions(-)
 rename lit_nlp/examples/{datasets/question_answering.py => tydi/data.py} (100%)

diff --git a/lit_nlp/examples/datasets/question_answering.py b/lit_nlp/examples/tydi/data.py
similarity index 100%
rename from lit_nlp/examples/datasets/question_answering.py
rename to lit_nlp/examples/tydi/data.py
diff --git a/lit_nlp/examples/tydi/demo.py b/lit_nlp/examples/tydi/demo.py
index 02739c5b..307ad8a3 100644
--- a/lit_nlp/examples/tydi/demo.py
+++ b/lit_nlp/examples/tydi/demo.py
@@ -12,12 +12,11 @@
 
 from absl import app
 from absl import flags
-
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.components import word_replacer
-from lit_nlp.examples.datasets import question_answering
-from lit_nlp.examples.tydi import model
+from lit_nlp.examples.tydi import data as tydi_data
+from lit_nlp.examples.tydi import model as tydi_model
 
 # NOTE: additional flags defined in server_flags.py
 _FLAGS = flags.FLAGS
@@ -55,10 +54,10 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
     # Ignore path prefix, if using /path/to/<model_name> to load from a
     # specific directory rather than the default shortcut.
     model_name = os.path.basename(model_name_or_path)
-    models[model_name] = model.TyDiModel(model_name=model_name_or_path)
+    models[model_name] = tydi_model.TyDiModel(model_name=model_name_or_path)
 
   max_examples: int = _MAX_EXAMPLES.value
-  dataset_defs: tuple[tuple[str, str]] = (
+  dataset_defs: tuple[tuple[str, str], ...] = (
       ("TyDiQA-Multilingual", "validation"),
       ("TyDiQA-English", "validation-en"),
       ("TyDiQA-Finnish", "validation-fi"),
@@ -71,7 +70,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       ("TyDiQA-Telugu", "validation-te"),
   )
   datasets = {
-      name: question_answering.TyDiQA(split=split, max_examples=max_examples)
+      name: tydi_data.TyDiQA(split=split, max_examples=max_examples)
       for name, split in dataset_defs
   }
 

From 0d8c0d948480e0835fd3f451b95b7ec306b6409d Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 12 Jun 2024 11:40:04 -0700
Subject: [PATCH 21/47] LIT: Avoid equivalent shuffles in Scrambler

PiperOrigin-RevId: 642683304
---
 lit_nlp/components/scrambler.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/lit_nlp/components/scrambler.py b/lit_nlp/components/scrambler.py
index 046fb859..ddc5d623 100644
--- a/lit_nlp/components/scrambler.py
+++ b/lit_nlp/components/scrambler.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 """Simple scrambling test generator."""
 
+import copy
 import random
 from typing import Optional
 
@@ -30,8 +31,12 @@
 
 def _scramble(val: str) -> str:
   words = val.split(' ')
-  random.shuffle(words)
-  return ' '.join(words)
+  shuffled = copy.deepcopy(words)
+  while True:
+    random.shuffle(shuffled)
+    if shuffled != words:
+      break
+  return ' '.join(shuffled)
 
 
 class Scrambler(lit_components.Generator):

From 1ed82d4e81ff6a6ff5146b6198e35444960d326b Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Thu, 13 Jun 2024 12:01:45 -0700
Subject: [PATCH 22/47] LIT: Remove TFX model wrapper.

PiperOrigin-RevId: 643077309
---
 lit_nlp/examples/models/tfx_model.py      | 73 -----------------------
 lit_nlp/examples/models/tfx_model_test.py | 49 ---------------
 2 files changed, 122 deletions(-)
 delete mode 100644 lit_nlp/examples/models/tfx_model.py
 delete mode 100644 lit_nlp/examples/models/tfx_model_test.py

diff --git a/lit_nlp/examples/models/tfx_model.py b/lit_nlp/examples/models/tfx_model.py
deleted file mode 100644
index 29df2ab9..00000000
--- a/lit_nlp/examples/models/tfx_model.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Wrapper for using TFX-generated models within LIT."""
-from collections.abc import Iterator
-
-import attr
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-import tensorflow as tf
-import tensorflow_text as tf_text  # pylint: disable=unused-import
-
-_SERVING_DEFAULT_SIGNATURE = 'serving_default'
-
-
-@attr.s(auto_attribs=True)
-class TFXModelConfig(object):
-  """Configuration object for TFX Models."""
-  path: str
-  input_spec: lit_types.Spec
-  output_spec: lit_types.Spec
-  signature: str = _SERVING_DEFAULT_SIGNATURE
-
-
-# TODO(b/188036366): Revisit the assumed mapping between input values and
-# TF.Examples.
-def _inputs_to_serialized_example(input_dict: lit_types.JsonDict):
-  """Converts the input dictionary to a serialized tf example."""
-  feature_dict = {}
-  for k, v in input_dict.items():
-    if not isinstance(v, list):
-      v = [v]
-    if isinstance(v[0], int):
-      feature_dict[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
-    elif isinstance(v[0], float):
-      feature_dict[k] = tf.train.Feature(float_list=tf.train.FloatList(value=v))
-    else:
-      feature_dict[k] = tf.train.Feature(
-          bytes_list=tf.train.BytesList(value=[bytes(i, 'utf-8') for i in v]))
-  result = tf.train.Example(features=tf.train.Features(feature=feature_dict))
-  return result.SerializeToString()
-
-
-class TFXModel(lit_model.BatchedModel):
-  """Wrapper for querying a TFX-generated SavedModel."""
-
-  def __init__(self, config: TFXModelConfig):
-    self._model = tf.saved_model.load(config.path)
-    self._signature = config.signature
-    self._input_spec = config.input_spec
-    self._output_spec = config.output_spec
-
-  def predict_minibatch(  # pytype: disable=signature-mismatch  # overriding-return-type-checks
-      self, inputs: list[lit_types.JsonDict]
-  ) -> Iterator[lit_types.JsonDict]:
-    for i in inputs:
-      filtered_inputs = {k: v for k, v in i.items() if k in self._input_spec}
-      result = self._model.signatures[self._signature](
-          tf.constant([_inputs_to_serialized_example(filtered_inputs)]))
-      result = {
-          k: tf.squeeze(v).numpy().tolist()
-          for k, v in result.items()
-          if k in self._output_spec
-      }
-      for k, v in result.items():
-        # If doing Multiclass Prediction for a Binary Classifier.
-        if (isinstance(self._output_spec[k], lit_types.MulticlassPreds) and
-            not isinstance(v, list)):
-          result[k] = [1 - v, v]
-      yield result
-
-  def input_spec(self) -> lit_types.Spec:
-    return self._input_spec
-
-  def output_spec(self) -> lit_types.Spec:
-    return self._output_spec
diff --git a/lit_nlp/examples/models/tfx_model_test.py b/lit_nlp/examples/models/tfx_model_test.py
deleted file mode 100644
index e7b977c9..00000000
--- a/lit_nlp/examples/models/tfx_model_test.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Tests for lit_nlp.components.tfx_model."""
-import tempfile
-
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import tfx_model
-import tensorflow as tf
-
-
-class TfxModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(TfxModelTest, self).setUp()
-    self._path = tempfile.mkdtemp()
-    input_layer = tf.keras.layers.Input(
-        shape=(1,), dtype=tf.string, name='example'
-    )
-    parsed_input = tf.io.parse_example(
-        tf.reshape(input_layer, [-1]),
-        {'input_0': tf.io.FixedLenFeature([1], dtype=tf.float32)})
-    output_layer = tf.keras.layers.Dense(
-        1, name='output_0')(
-            parsed_input['input_0'])
-    model = tf.keras.Model(input_layer, output_layer)
-    model.compile(
-        optimizer=tf.keras.optimizers.Adam(lr=.001),
-        loss=tf.keras.losses.binary_crossentropy)
-    model.save(self._path)
-
-  def testTfxModel(self):
-    input_spec = {'input_0': lit_types.Scalar()}
-    output_spec = {
-        'output_0':
-            lit_types.MulticlassPreds(vocab=['0', '1'], parent='input_0')
-    }
-    config = tfx_model.TFXModelConfig(self._path, input_spec, output_spec)
-    lit_model = tfx_model.TFXModel(config)
-    result = list(lit_model.predict([{'input_0': 0.5}]))
-    self.assertLen(result, 1)
-    result = result[0]
-    self.assertListEqual(list(result.keys()), ['output_0'])
-    self.assertLen(result['output_0'], 2)
-    self.assertIsInstance(result['output_0'][0], float)
-    self.assertIsInstance(result['output_0'][1], float)
-    self.assertDictEqual(lit_model.input_spec(), input_spec)
-    self.assertDictEqual(lit_model.output_spec(), output_spec)
-
-
-if __name__ == '__main__':
-  tf.test.main()

From 7d5ef5831427de71416c096a6dbcd46ea064457e Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Thu, 13 Jun 2024 12:31:04 -0700
Subject: [PATCH 23/47] LIT: Move model_utils to glue demo directory.

PiperOrigin-RevId: 643086573
---
 lit_nlp/examples/{models => glue}/model_utils.py    |  0
 .../examples/{models => glue}/model_utils_test.py   |  2 +-
 lit_nlp/examples/glue/models.py                     |  2 +-
 .../testdata/bert_tokenizer/config.json             |  0
 .../testdata/bert_tokenizer/special_tokens_map.json |  0
 .../testdata/bert_tokenizer/tokenizer_config.json   |  0
 .../testdata/bert_tokenizer/vocab.txt               |  0
 lit_nlp/examples/models/__init__.py                 | 13 -------------
 8 files changed, 2 insertions(+), 15 deletions(-)
 rename lit_nlp/examples/{models => glue}/model_utils.py (100%)
 rename lit_nlp/examples/{models => glue}/model_utils_test.py (99%)
 rename lit_nlp/examples/{models => glue}/testdata/bert_tokenizer/config.json (100%)
 rename lit_nlp/examples/{models => glue}/testdata/bert_tokenizer/special_tokens_map.json (100%)
 rename lit_nlp/examples/{models => glue}/testdata/bert_tokenizer/tokenizer_config.json (100%)
 rename lit_nlp/examples/{models => glue}/testdata/bert_tokenizer/vocab.txt (100%)
 delete mode 100644 lit_nlp/examples/models/__init__.py

diff --git a/lit_nlp/examples/models/model_utils.py b/lit_nlp/examples/glue/model_utils.py
similarity index 100%
rename from lit_nlp/examples/models/model_utils.py
rename to lit_nlp/examples/glue/model_utils.py
diff --git a/lit_nlp/examples/models/model_utils_test.py b/lit_nlp/examples/glue/model_utils_test.py
similarity index 99%
rename from lit_nlp/examples/models/model_utils_test.py
rename to lit_nlp/examples/glue/model_utils_test.py
index d3e238d9..27747a78 100644
--- a/lit_nlp/examples/models/model_utils_test.py
+++ b/lit_nlp/examples/glue/model_utils_test.py
@@ -2,7 +2,7 @@
 import os
 
 from absl.testing import absltest
-from lit_nlp.examples.models import model_utils
+from lit_nlp.examples.glue import model_utils
 import numpy as np
 import transformers
 
diff --git a/lit_nlp/examples/glue/models.py b/lit_nlp/examples/glue/models.py
index 9f14fe83..d8c3219b 100644
--- a/lit_nlp/examples/glue/models.py
+++ b/lit_nlp/examples/glue/models.py
@@ -12,7 +12,7 @@
 import attr
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import model_utils
+from lit_nlp.examples.glue import model_utils
 from lit_nlp.lib import file_cache
 from lit_nlp.lib import utils
 import numpy as np
diff --git a/lit_nlp/examples/models/testdata/bert_tokenizer/config.json b/lit_nlp/examples/glue/testdata/bert_tokenizer/config.json
similarity index 100%
rename from lit_nlp/examples/models/testdata/bert_tokenizer/config.json
rename to lit_nlp/examples/glue/testdata/bert_tokenizer/config.json
diff --git a/lit_nlp/examples/models/testdata/bert_tokenizer/special_tokens_map.json b/lit_nlp/examples/glue/testdata/bert_tokenizer/special_tokens_map.json
similarity index 100%
rename from lit_nlp/examples/models/testdata/bert_tokenizer/special_tokens_map.json
rename to lit_nlp/examples/glue/testdata/bert_tokenizer/special_tokens_map.json
diff --git a/lit_nlp/examples/models/testdata/bert_tokenizer/tokenizer_config.json b/lit_nlp/examples/glue/testdata/bert_tokenizer/tokenizer_config.json
similarity index 100%
rename from lit_nlp/examples/models/testdata/bert_tokenizer/tokenizer_config.json
rename to lit_nlp/examples/glue/testdata/bert_tokenizer/tokenizer_config.json
diff --git a/lit_nlp/examples/models/testdata/bert_tokenizer/vocab.txt b/lit_nlp/examples/glue/testdata/bert_tokenizer/vocab.txt
similarity index 100%
rename from lit_nlp/examples/models/testdata/bert_tokenizer/vocab.txt
rename to lit_nlp/examples/glue/testdata/bert_tokenizer/vocab.txt
diff --git a/lit_nlp/examples/models/__init__.py b/lit_nlp/examples/models/__init__.py
deleted file mode 100644
index c6334245..00000000
--- a/lit_nlp/examples/models/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.

From 992823b027fca8c60edabe837248a508ac04da22 Mon Sep 17 00:00:00 2001
From: Bin Du <dubin@google.com>
Date: Mon, 17 Jun 2024 09:41:12 -0700
Subject: [PATCH 24/47] Minor updates on the model support in doc string post
 demo cleanup.

PiperOrigin-RevId: 644039366
---
 lit_nlp/examples/blank_slate_demo.py                  | 6 ------
 lit_nlp/examples/prompt_debugging/transformers_lms.py | 2 +-
 2 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index a4c66c4d..c82ffde1 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -6,12 +6,6 @@
 - classification model on MultiNLI, with the MultiNLI dataset.
 - TensorFlow Keras model for penguin classification, with the Penguin tabular
   dataset from TFDS.
-- T5 models using HuggingFace Transformers and Keras, with the English CNNDM
-  summarization dataset and the WMT '14 machine-translation dataset.
-- BERT (bert-*) as a masked language model and GPT-2 (gpt2* or distilgpt2) as a
-  left-to-right language model, with the Stanford Sentiment Treebank dataset,
-  the IMDB reviews dataset, Billion Word Benchmark (lm1b) dataset and the option
-  to load sentences from a flat text file.
 
 To run:
   python -m lit_nlp.examples.blank_slate_demo --port=5432
diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py
index 1ab121f6..6ba20581 100644
--- a/lit_nlp/examples/prompt_debugging/transformers_lms.py
+++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py
@@ -1,6 +1,6 @@
 """Wrapper for HuggingFace models in LIT.
 
-Includes BERT masked LM, GPT-2, and T5.
+Supported models include Gemma, GPT-2, Llama, Mistral, etc.
 
 This wrapper loads a model into memory and implements the a number of helper
 functions to predict a batch of examples and extract information such as

From 3dad2b061b45cb44b1c3f9b9364660e907662069 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 18 Jun 2024 09:32:07 -0700
Subject: [PATCH 25/47] Remove LM prediction module.

PiperOrigin-RevId: 644414363
---
 lit_nlp/api/layout.py                         |   2 -
 .../client/modules/lm_prediction_module.css   |  84 ----
 .../client/modules/lm_prediction_module.ts    | 399 ------------------
 3 files changed, 485 deletions(-)
 delete mode 100644 lit_nlp/client/modules/lm_prediction_module.css
 delete mode 100644 lit_nlp/client/modules/lm_prediction_module.ts

diff --git a/lit_nlp/api/layout.py b/lit_nlp/api/layout.py
index be67215e..bb8d773e 100644
--- a/lit_nlp/api/layout.py
+++ b/lit_nlp/api/layout.py
@@ -45,7 +45,6 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   GeneratedImageModule = 'generated-image-module'
   GeneratedTextModule = 'generated-text-module'
   GeneratorModule = 'generator-module'
-  LanguageModelPredictionModule = 'lm-prediction-module'
   LegacySequenceSalienceModule = 'legacy-sequence-salience-module'
   MetricsModule = 'metrics-module'
   MultilabelModule = 'multilabel-module'
@@ -132,7 +131,6 @@ def to_json(self) -> dtypes.JsonDict:
     modules.ClassificationModule,
     modules.MultilabelModule,
     modules.RegressionModule,
-    modules.LanguageModelPredictionModule,
     modules.GeneratedTextModule,
     modules.AnnotatedTextGoldModule,
     modules.AnnotatedTextModule,
diff --git a/lit_nlp/client/modules/lm_prediction_module.css b/lit_nlp/client/modules/lm_prediction_module.css
deleted file mode 100644
index 59d5b0ab..00000000
--- a/lit_nlp/client/modules/lm_prediction_module.css
+++ /dev/null
@@ -1,84 +0,0 @@
-#main-area {
-  display: flex;
-  flex-direction: row;
-  justify-content: space-between;
-}
-
-.module-toolbar {
-  justify-content: space-between;
-  border-bottom: 1px solid rgb(218, 220, 224);
-}
-
-.mode-info {
-  color: gray;
-}
-
-.input-group {
-  padding: 4px;
-}
-
-.input-group-title {
-  margin-bottom: 3px;
-}
-
-.input-words {
-  display: flex;
-  flex-direction: row;
-  flex-wrap: wrap;
-  align-content: flex-start;
-}
-
-.token:hover {
-  cursor: pointer;
-}
-
-.token.masked {
-  text-decoration: line-through;
-}
-
-.sidebar {
-  min-width: 15%;
-  padding: 4px;
-  overflow-y: auto; /* Scroll this before the rest of the module */
-}
-
-.sidebar-shaded {
-  background: #f8f9fa;
-  border-left: 1px solid #bdc1c6;
-  border-radius: 2px;
-}
-
-#output-table {
-  border-spacing: 0px;
-}
-
-#output-words {
-  min-width: 190px;
-  width: 20%;
-}
-
-.output-row {
-  display: flex;
-  flex-direction: row;
-  justify-content: space-between;
-}
-
-.output-token {
-  padding: 1px;
-  border-width: 2px;
-  margin: 2px;
-  font-family: 'Roboto Mono', monospace;
-}
-
-.output-percent {
-  margin: 2px;
-}
-
-.flex-holder {
-  display: flex;
-}
-
-.add-icon-button {
-  margin-top: 6px;
-  margin-right: 4px;
-}
diff --git a/lit_nlp/client/modules/lm_prediction_module.ts b/lit_nlp/client/modules/lm_prediction_module.ts
deleted file mode 100644
index 90f7aeab..00000000
--- a/lit_nlp/client/modules/lm_prediction_module.ts
+++ /dev/null
@@ -1,399 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-import '../elements/checkbox';
-
-import {html} from 'lit';
-// tslint:disable:no-new-decorators
-import {customElement} from 'lit/decorators.js';
-import {classMap} from 'lit/directives/class-map.js';
-import {computed, observable} from 'mobx';
-
-import {LitModule} from '../core/lit_module';
-import {TextSegment, Tokens, TokenTopKPreds} from '../lib/lit_types';
-import {styles as sharedStyles} from '../lib/shared_styles.css';
-import {type IndexedInput, ModelInfoMap, Spec, TopKResult} from '../lib/types';
-import {findMatchingIndices, findSpecKeys, makeModifiedInput, replaceNth} from '../lib/utils';
-
-import {styles} from './lm_prediction_module.css';
-
-/**
- * A LIT module that renders masked predictions for a masked LM.
- */
-@customElement('lm-prediction-module')
-export class LanguageModelPredictionModule extends LitModule {
-  static override title = 'LM Predictions';
-  static override duplicateForExampleComparison = true;
-  static override duplicateAsRow = true;
-  static override numCols = 4;
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => html`
-  <lm-prediction-module model=${model} .shouldReact=${shouldReact}
-    selectionServiceIndex=${selectionServiceIndex}>
-  </lm-prediction-module>`;
-
-  static override get styles() {
-    return [sharedStyles, styles];
-  }
-
-  // Module options / configuration state
-  @observable private clickToMask: boolean = false;
-
-  // Fixed output state (based on unmodified input)
-  @observable private selectedInput: IndexedInput|null = null;
-  @observable private tokens: string[] = [];
-  @observable private maskedInput: IndexedInput|null = null;
-  @observable private originalResults: TopKResult[][] = [];
-  // Ephemeral output state (may depend on selectedTokenIndex)
-  @observable private selectedTokenIndex: number|null = null;
-  @observable private mlmResults: TopKResult[][] = [];
-
-  @computed
-  private get modelSpec() {
-    return this.appState.getModelSpec(this.model);
-  }
-
-  @computed
-  private get predKey(): string {
-    // This list is guaranteed to be non-empty due to checkModule()
-    return findSpecKeys(this.modelSpec.output, TokenTopKPreds)[0];
-  }
-
-  @computed
-  private get outputTokensKey(): string {
-    // This list is guaranteed to be non-empty due to checkModule()
-    return (this.modelSpec.output[this.predKey] as TokenTopKPreds).align as
-        string;
-  }
-
-  @computed
-  private get outputTokensPrefix(): string {
-    // This list is guaranteed to be non-empty due to checkModule()
-    return (this.modelSpec.output[this.outputTokensKey] as Tokens)
-               .token_prefix as string;
-  }
-
-  @computed
-  private get inputTokensKey(): string|null {
-    // Look for an input field matching the output tokens name.
-    if (this.modelSpec.input.hasOwnProperty(this.outputTokensKey) &&
-        this.modelSpec.input[this.outputTokensKey] instanceof Tokens) {
-      return this.outputTokensKey;
-    }
-    return null;
-  }
-
-  @computed
-  private get maskToken() {
-    // Look at metadata for /input/ field matching output tokens name.
-    return this.inputTokensKey ?
-        (this.modelSpec.input[this.inputTokensKey] as Tokens).mask_token :
-        undefined;
-  }
-
-  override firstUpdated() {
-    const getSelectedInputData = () =>
-        this.selectionService.primarySelectedInputData;
-    this.reactImmediately(getSelectedInputData, selectedInput => {
-      this.updateSelection(selectedInput);
-    });
-    this.react(() => this.selectedTokenIndex, tokenIndex => {
-      this.updateMLMResults();
-    });
-    this.react(() => this.clickToMask, clickToMask => {
-      this.updateMLMResults();
-    });
-    // Enable click-to-mask if the model supports it.
-    this.reactImmediately(() => this.model, model => {
-      if (this.maskToken != null) {
-        this.clickToMask = true;
-      }
-    });
-  }
-
-  private async updateSelection(input: IndexedInput|null) {
-    this.selectedInput = null;
-    this.tokens = [];
-    this.maskedInput =  null;
-    this.originalResults = [];
-    this.selectedTokenIndex = null;
-    this.mlmResults = [];
-
-    if (input == null) return;
-
-    const dataset = this.appState.currentDataset;
-    const promise = this.apiService.getPreds(
-        [input], this.model, dataset, [Tokens, TokenTopKPreds], [],
-        'Loading tokens');
-    const results = await this.loadLatest('modelPreds', promise);
-    if (results === null) return;
-
-    const predictions = results[0];
-    this.tokens = predictions[this.outputTokensKey];
-    this.originalResults = predictions[this.predKey];
-    this.mlmResults = this.originalResults;
-    this.selectedInput = input;
-
-    // If there's already a mask in the input, jump to that.
-    if (this.maskToken != null) {
-      const maskIndex = this.tokens.indexOf(this.maskToken);
-      if (maskIndex !== -1) {
-        // Show fills immediately for the first mask token, if there is one.
-        this.selectedTokenIndex = maskIndex;
-      }
-    }
-  }
-
-  private createChildDatapoint(orig: IndexedInput, tokens: string[]) {
-    return makeModifiedInput(orig, {[this.inputTokensKey!]: tokens}, 'masked');
-  }
-
-  private async updateMLMResults() {
-    if (this.selectedTokenIndex == null || !this.clickToMask) {
-      this.mlmResults = this.originalResults;  // reset
-      this.maskedInput =  null;
-      return;
-    }
-    if (this.selectedInput == null || this.maskToken == null) {
-      return;
-    }
-
-    const tokens = [...this.tokens];
-    // Short-circuit to avoid an extra inference call if this token
-    // is already masked in the original input.
-    if (tokens[this.selectedTokenIndex] === this.maskToken) {
-      this.mlmResults = this.originalResults;
-      return;
-    }
-
-    // Create masked datapoint.
-    tokens[this.selectedTokenIndex] = this.maskToken;
-    this.maskedInput = this.createChildDatapoint(this.selectedInput, tokens);
-    this.updateTextSegment(
-        this.maskedInput, this.maskToken, this.selectedTokenIndex);
-
-    // Reset current results.
-    this.mlmResults = [];
-
-    const dataset = this.appState.currentDataset;
-    const promise = this.apiService.getPreds(
-        [this.maskedInput], this.model, dataset, [TokenTopKPreds]);
-    const results = await this.loadLatest('mlmResults', promise);
-    if (results === null) return;
-
-    this.mlmResults = results[0][this.predKey];
-  }
-
-  /** Update the datapoint's TextSegment based on a new token value. */
-  private updateTextSegment(datapoint: IndexedInput, token: string,
-                            tokenIndex: number) {
-    // This logic ensure that if the token to replace occurs multiple times
-    // in the text segment, that the correct instance of the token is replaced.
-    const textField = findSpecKeys(this.modelSpec.input, TextSegment)[0];
-    let oldToken = this.tokens[tokenIndex];
-    const tokensIndicesMatchingToken = findMatchingIndices(
-        this.tokens, oldToken);
-    const replacementIndex = tokensIndicesMatchingToken.indexOf(tokenIndex);
-    if (this.outputTokensPrefix != null &&
-        oldToken.startsWith(this.outputTokensPrefix)) {
-      oldToken = oldToken.slice(this.outputTokensPrefix.length);
-    }
-    const newText = replaceNth(datapoint.data[textField], oldToken, token,
-                               replacementIndex + 1);
-    datapoint.data[textField] = newText;
-  }
-
-  override renderImpl() {
-    return html`
-      <div class='module-container'>
-        ${this.renderControls()}
-        <div id='main-area' class='module-results-area'>
-          ${this.renderInputWords()}
-          ${this.renderOutputWords()}
-        </div>
-      </div>
-    `;
-  }
-
-  /* Mode info if masking is available. */
-  renderModeInfo() {
-    if (this.selectedTokenIndex === null) {
-      return null;
-    }
-    // clang-format off
-    return html`
-      <span class='mode-info'>
-        ${this.clickToMask ? "Masked predictions" : "Unmasked predictions"}
-      </span>
-    `;
-    // clang-format on
-  }
-
-  renderAddInputButton() {
-    const onClickAdd = async () => {
-      const data: IndexedInput[] = await this.appState.annotateNewData(
-          [this.maskedInput!]);
-      this.appState.commitNewDatapoints(data);
-    };
-    return html`
-      <button class='hairline-button'
-        @click=${onClickAdd} ?disabled="${this.maskedInput == null}">
-        Add masked datapoint
-      </button>`;
-  }
-
-  renderControls() {
-    // clang-format off
-    return html`
-      <div class='module-toolbar'>
-        ${this.maskToken ? html`
-          <lit-checkbox label="Mask selected token"
-            ?checked=${this.clickToMask}
-            @change=${() => { this.clickToMask = !this.clickToMask; }}
-          ></lit-checkbox>` : null}
-        ${this.renderAddInputButton()}
-      </div>
-    `;
-    // clang-format on
-  }
-
-  renderInputWords() {
-    const renderToken = (token: string, i: number) => {
-      const handleClick = () => {
-        if (i === this.selectedTokenIndex) {
-          // Clear if the same position is clicked again.
-          this.selectedTokenIndex = null;
-        } else {
-          this.selectedTokenIndex = i;
-        }
-      };
-      const classes = classMap({
-        'token': true,
-        'token-chip-label': true,
-        'selected': i === this.selectedTokenIndex,
-        'masked': (i === this.selectedTokenIndex) && (this.maskToken != null) &&
-            this.clickToMask,
-      });
-      return html`<div class=${classes} @click=${handleClick}>${token}</div>`;
-    };
-
-    // clang-format off
-    return html`
-      <div class='input-group'>
-        <div class='field-title input-group-title' title='Field from model'>
-          ${this.tokens.length > 0 ? this.outputTokensKey : null}
-        </div>
-        <div class="input-words">${this.tokens.map(renderToken)}</div>
-      </div>
-    `;
-    // clang-format on
-  }
-
-  renderOutputWords() {
-
-    const renderPred = (pred: TopKResult) => {
-      const selectedWordType = this.tokens[this.selectedTokenIndex!];
-      const predWordType = pred[0];
-      // Convert probability into percent.
-      const predProb = (pred[1] * 100).toFixed(1);
-      const matchesInput = predWordType === selectedWordType;
-      const classes = classMap({
-        'output-token': true,
-        'token-chip-generated': true,
-        'selected': matchesInput,
-      });
-      const addPoint = async () => {
-        // Create new datapoint with selected replacement token and add to
-        // dataset.
-        const tokens = [...this.tokens];
-        const newInput = this.createChildDatapoint(this.selectedInput!, tokens);
-        newInput.data[this.inputTokensKey!][this.selectedTokenIndex!] =
-            predWordType;
-        this.updateTextSegment(
-            newInput, predWordType, this.selectedTokenIndex!);
-
-        const data: IndexedInput[] = await this.appState.annotateNewData(
-          [newInput]);
-        this.appState.commitNewDatapoints(data);
-      };
-      // clang-format off
-      return html`
-        <div class='output-row'>
-          <div class='flex-holder'>
-            <mwc-icon class="icon-button outlined add-icon-button"
-                      @click=${addPoint} ?disabled="${matchesInput}"
-                      title="Add to dataset">
-              add_box
-            </mwc-icon>
-            <div class=${classes}>${predWordType}</div>
-          </div>
-          <div class='token-chip-label output-percent'>${predProb}%</div>
-        </div>
-      `;
-      // clang-format on
-    };
-
-    const renderSidebarContents = () => {
-      if (this.selectedTokenIndex === null || this.mlmResults.length === 0) {
-        return html`
-            <span class='mode-info'>
-              Click a token to see predictions
-            </span>`;
-      } else {
-        // TODO(b/210998285): Add surrounding token context above predictions.
-        return html`
-            ${this.maskToken ? html`${this.renderModeInfo()}` : null}
-            ${this.mlmResults[this.selectedTokenIndex].map(renderPred)}`;
-      }
-    };
-
-    // clang-format off
-    return html`
-      <div id="output-words" class='sidebar sidebar-shaded'>
-        ${renderSidebarContents()}
-      </div>
-    `;
-    // clang-format on
-  }
-
-  /**
-   * Find available output fields.
-   */
-  static findTargetFields(outputSpec: Spec): string[] {
-    const candidates = findSpecKeys(outputSpec, TokenTopKPreds);
-    return candidates.filter(k => {
-      const align = (outputSpec[k] as TokenTopKPreds).align;
-      return align != null && (outputSpec[align] instanceof Tokens);
-    });
-  }
-
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
-    for (const modelInfo of Object.values(modelSpecs)) {
-      if (LanguageModelPredictionModule.findTargetFields(modelInfo.spec.output)
-              .length > 0) {
-        return true;
-      }
-    }
-    return false;
-  }
-}
-
-declare global {
-  interface HTMLElementTagNameMap {
-    'lm-prediction-module': LanguageModelPredictionModule;
-  }
-}

From 0656386188d6e4b6c83dab58fb4e6569ebea217e Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 18 Jun 2024 09:55:11 -0700
Subject: [PATCH 26/47] Remove span graph module from LIT.

PiperOrigin-RevId: 644421561
---
 lit_nlp/api/layout.py                         |   6 -
 lit_nlp/client/elements/span_graph_vis.css    |  74 ---
 lit_nlp/client/elements/span_graph_vis.ts     | 472 ------------------
 .../elements/span_graph_vis_vertical.css      | 103 ----
 .../elements/span_graph_vis_vertical.ts       | 278 -----------
 .../client/modules/annotated_text_module.ts   |  13 +-
 lit_nlp/client/modules/span_graph_module.ts   | 346 -------------
 7 files changed, 7 insertions(+), 1285 deletions(-)
 delete mode 100644 lit_nlp/client/elements/span_graph_vis.css
 delete mode 100644 lit_nlp/client/elements/span_graph_vis.ts
 delete mode 100644 lit_nlp/client/elements/span_graph_vis_vertical.css
 delete mode 100644 lit_nlp/client/elements/span_graph_vis_vertical.ts
 delete mode 100644 lit_nlp/client/modules/span_graph_module.ts

diff --git a/lit_nlp/api/layout.py b/lit_nlp/api/layout.py
index bb8d773e..188c8cd4 100644
--- a/lit_nlp/api/layout.py
+++ b/lit_nlp/api/layout.py
@@ -59,10 +59,6 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   SimpleDatapointEditorModule = 'simple-datapoint-editor-module'
   # Non-replicating version of Datapoint Editor
   SingleDatapointEditorModule = 'single-datapoint-editor-module'
-  SpanGraphGoldModule = 'span-graph-gold-module'
-  SpanGraphGoldModuleVertical = 'span-graph-gold-module-vertical'
-  SpanGraphModule = 'span-graph-module'
-  SpanGraphModuleVertical = 'span-graph-module-vertical'
   TCAVModule = 'tcav-module'
   ThresholderModule = 'thresholder-module'
   TrainingDataAttributionModule = 'tda-module'
@@ -126,8 +122,6 @@ def to_json(self) -> dtypes.JsonDict:
 modules = LitModuleName  # pylint: disable=invalid-name
 
 MODEL_PREDS_MODULES = (
-    modules.SpanGraphGoldModuleVertical,
-    modules.SpanGraphModuleVertical,
     modules.ClassificationModule,
     modules.MultilabelModule,
     modules.RegressionModule,
diff --git a/lit_nlp/client/elements/span_graph_vis.css b/lit_nlp/client/elements/span_graph_vis.css
deleted file mode 100644
index 310775f3..00000000
--- a/lit_nlp/client/elements/span_graph_vis.css
+++ /dev/null
@@ -1,74 +0,0 @@
-text.token-text {
-  alignment-baseline: middle;
-  dominant-baseline: central;
-}
-
-polyline.span-bracket {
-  fill: none;
-  stroke-width: 1.2px;
-  stroke: var(--group-color);
-}
-
-.selected polyline.span-bracket {
-  stroke-width: 1.8px;
-}
-
-path.arc-path {
-  stroke-width: 1.2px;
-  stroke: var(--group-color);
-  fill: none;
-}
-
-path.arc-path.arc-neg {
-  stroke-dasharray: 3,1;
-  stroke: gray;
-}
-
-.selected path.arc-path {
-  stroke-width: 1.8px;
-}
-
-path.arc-arrow {
-  stroke-width: 1.2px;
-  stroke: var(--group-color);
-  fill: var(--group-color);
-}
-
-path.arc-arrow.arc-neg {
-  stroke: gray;
-  fill: gray;
-}
-
-
-.layer-label text {
-  font-family: 'Share Tech Mono', monospace;
-  dominant-baseline: middle;
-  text-anchor: end;
-}
-
-foreignObject.span-label {
-  overflow: visible;
-}
-
-.span-label div {
-  background-color: white;
-  font-family: 'Share Tech Mono', monospace;
-  line-height: 1.0;
-  padding: 1px;
-  padding-right: 3px; /* for occluding labels on mouseover */
-  white-space: nowrap;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  color: var(--group-color);
-}
-
-g.selected .span-label div {
-  background-color: white;
-  overflow-x: visible;
-  width: fit-content;  /* needed to include background when expanding */
-}
-
-.mousebox {
-  fill: white;
-  fill-opacity: 0.0;
-}
diff --git a/lit_nlp/client/elements/span_graph_vis.ts b/lit_nlp/client/elements/span_graph_vis.ts
deleted file mode 100644
index 0e10cf3a..00000000
--- a/lit_nlp/client/elements/span_graph_vis.ts
+++ /dev/null
@@ -1,472 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * Visualization component for structured prediction over text.
- */
-
-import * as d3 from 'd3';
-import {html, LitElement, svg} from 'lit';
-import {customElement, property} from 'lit/decorators.js';
-import {classMap} from 'lit/directives/class-map.js';
-import {styleMap} from 'lit/directives/style-map.js';
-
-import {getVizColor} from '../lib/colors';
-import {EdgeLabel} from '../lib/dtypes';
-
-import {styles} from './span_graph_vis.css';
-
-
-/**
- * Represents a group of directed graphs anchored to token spans.
- * This is the general "edge probing" representation, which can be used for many
- * problems including sequence tagging, span labeling, and directed graphs like
- * semantic frames, coreference, and dependency parsing. See
- * https://arxiv.org/abs/1905.06316 for more on this formalism.
- */
-export interface SpanGraph {
-  'tokens': string[];
-  'layers': AnnotationLayer[];
-}
-
-/**
- * A single layer of annotations, like 'pos' (part-of-speech)
- * or 'ner' (named entities).
- */
-export interface AnnotationLayer {
-  'name': string;
-  'edges': EdgeLabel[];
-}
-
-/* Compute points for a polyline bracket. */
-function hBracketPoints(width: number, height: number, lean: number) {
-  // Points for a polyline bracket.
-  const start = [0, 0];
-  const ltop = [lean, height];
-  const rtop = [width - lean, height];
-  const end = [width, 0];
-  return [start, ltop, rtop, end];
-}
-
-/**
- * Compute path for a dependency arc.
- */
-function arcPath(
-    startY: number, x1: number, x2: number, height: number, aspect: number) {
-  const left = Math.min(x1, x2);
-  const right = Math.max(x1, x2);
-  let pathCommands = `M ${left} ${startY} `;
-  if ((right - left) > (2 * aspect * height)) {
-    // Long arcs: draw as 90* curve, flat, then 90* curve.
-    const majorAxis = aspect * height;
-    pathCommands += `A ${majorAxis} ${height} 0 0 1 ${left + majorAxis} ${
-        startY - height} `;
-    pathCommands += `L ${right - majorAxis} ${startY - height} `;
-    pathCommands += `A ${majorAxis} ${height} 0 0 1 ${right} ${startY} `;
-  } else {
-    // Short arcs: draw as single 180* curve.
-    height = (right - left) / (2 * aspect);
-    pathCommands +=
-        `A ${(right - left) / 2} ${height} 0 0 1 ${right} ${startY} `;
-  }
-  return pathCommands; /* assign as 'd' attribute to path */
-}
-
-/**
- * Compute path for the arrow at the end of an arc.
- */
-function arcArrow(startY: number, x: number, markSize: number) {
-  let pathCommands = `M ${x - markSize} ${startY - (1.5 * markSize)} `;
-  pathCommands += `L ${x + markSize} ${startY - (1.5 * markSize)} `;
-  pathCommands += `L ${x} ${startY} Z`;
-  return pathCommands; /* assign as 'd' attribute to path */
-}
-
-/* Set attributes to match target's size to the source element. */
-function matchBBox(source: SVGGElement, target: SVGRectElement) {
-  const bbox = source.getBBox();
-  target.setAttribute('x', `${bbox.x}`);
-  target.setAttribute('y', `${bbox.y}`);
-  target.setAttribute('width', `${bbox.width}`);
-  target.setAttribute('height', `${bbox.height}`);
-}
-
-/** Structured prediction (SpanGraph) visualization class. */
-@customElement('span-graph-vis')
-export class SpanGraphVis extends LitElement {
-  /* Data binding */
-  @property({type: Object}) data: SpanGraph = {tokens: [], layers: []};
-  @property({type: Boolean}) showLayerLabel: boolean = true;
-
-  /* Rendering parameters */
-  @property({type: Number}) lineHeight: number = 18;
-  @property({type: Number}) bracketHeight: number = 5.5;
-  @property({type: Number}) yPad: number = 5;
-  // For arcs between spans.
-  @property({type: Number}) arcBaseHeight: number = 20;
-  @property({type: Number}) arcMaxHeight: number = 40;
-  @property({type: Number}) arcAspect: number = 1.2;
-  @property({type: Number}) arcArrowSize: number = 4;
-  // Padding for SVG viewport, to avoid clipping some elements (like polyline).
-  @property({type: Number}) viewPad: number = 5;
-  // Multiplier from SVG units to screen pixels.
-  @property({type: Number}) svgScaling: number = 1.2;
-
-  /* Internal rendering state */
-  private tokenXBounds: Array<[number, number]> = [];
-
-  static override get styles() {
-    return styles;
-  }
-
-  renderTokens(tokens: string[]) {
-    return svg`
-      <g id='token-group'>
-        <text class='token-text' height=${this.lineHeight}>
-          ${tokens.map(t => svg`<tspan>${svg`${t + ' '}`}</tspan>`)}
-        </text>
-      </g>`;
-  }
-
-  private getTokenGroup() {
-    return this.shadowRoot!.querySelector('g#token-group') as SVGGElement;
-  }
-
-  renderEdge(edge: EdgeLabel, color: string) {
-    // Positioning relative to the group transform, which will be applied later.
-    const labelHeight = this.lineHeight;
-    const labelY = -(this.bracketHeight + this.lineHeight);
-
-    let labelText = edge.label;
-    let isNegativeEdge = false;
-    if (typeof edge.label === 'number') {
-      labelText = edge.label.toFixed(3);
-      isNegativeEdge = edge.label < 0.5;
-    }
-    const arcPathClass =
-        classMap({'arc-path': true, 'arc-neg': isNegativeEdge});
-    const arcArrowClass =
-        classMap({'arc-arrow': true, 'arc-neg': isNegativeEdge});
-    color = isNegativeEdge ? 'gray' : color;
-    // clang-format off
-    return svg`
-      <g class='edge-group' style=${styleMap({'--group-color': color})}
-        data-color=${color}>
-        ${edge.span2 ? svg`
-          <path class=${arcPathClass}></path>
-          <path class=${arcArrowClass}></path>
-          ` : ''}
-        <g class='at-span1'>
-          <polyline class='span-bracket'></polyline>
-          <foreignObject class='span-label' height=${labelHeight} y=${labelY}>
-            ${html`<div>${labelText}</div>`}
-          </foreignObject>
-          <rect class='mousebox'></rect>
-        </g>
-        ${edge.span2 ? svg`<g class='at-span2'>
-            <polyline class='span-bracket'></polyline>
-          </g>` : ''}
-      </g>
-    `;
-    // clang-format on
-  }
-
-  renderLayer(layer: AnnotationLayer, i: number) {
-    const rowColor = getVizColor('deep', i).color;
-    // Positioning relative to the group transform, which will be applied later.
-    const rowLabelX = -10;
-    const rowLabelY = -(this.bracketHeight + 0.5 * this.lineHeight);
-
-    const orderedEdges = this.sortEdges(layer.edges);
-    // clang-format off
-    return svg`
-      <g id='layer-group-${layer.name}' data-color=${rowColor}>
-        ${this.showLayerLabel ? svg`
-          <g class='layer-label'>
-            <text x=${rowLabelX} y=${rowLabelY} fill=${rowColor}>
-              ${svg`${layer.name}`}
-            </text>
-          </g>` : null}
-        ${orderedEdges.map(edge => this.renderEdge(edge, rowColor))}
-      </g>
-    `;
-    // clang-format on
-  }
-
-  private getLayerGroup(name: string) {
-    return this.shadowRoot!.querySelector(`g#layer-group-${name}`) as
-        SVGGElement;
-  }
-
-  override render() {
-    return svg`
-      <svg id='svg' xmlns='http://www.w3.org/2000/svg'><g id='all'>
-        ${this.data ? this.renderTokens(this.data.tokens) : ''}
-        ${this.data ? this.data.layers.map(this.renderLayer.bind(this)) : ''}
-      </g></svg>`;
-  }
-
-  private findTokenBounds() {
-    const tokenNodes = this.getTokenGroup().querySelectorAll('tspan');
-    const tokenXBounds: Array<[number, number]> = [];
-    tokenNodes.forEach(tspan => {
-      // Use getBBox() to avoid a crash when tspan.getNumberOfChars() === 0.
-      // TODO(lit-dev): figure out why this case happens - maybe
-      // the nodes are not yet attached to the DOM?
-      const bbox = tspan.getBBox();
-      tokenXBounds.push([bbox.x, bbox.x + bbox.width]);
-    });
-    return tokenXBounds;
-  }
-
-  /**
-   * Consistent sort order.
-   * Because span labels overflow to the right, we order these so the rightmost
-   * spans appear first in the DOM, and thus render under anything to the left
-   * that needs to overflow.
-   */
-  private sortEdges(edges: EdgeLabel[]) {
-    return edges.slice().sort((a, b) => d3.descending(a.span1[1], b.span1[1]));
-  }
-
-  /* Starting x position for a bracket, in SVG coordinates */
-  private getStartX(span: [number, number]) {
-    return this.tokenXBounds[span[0]][0];
-  }
-
-  /* Ending x position for a bracket, in SVG coordinates */
-  private getEndX(span: [number, number]) {
-    return this.tokenXBounds[span[1] - 1][1];
-  }
-
-  /* Find available width without clipping the next label */
-  private findAvailableWidths(layerGroup: Element, edges: EdgeLabel[]):
-      number[] {
-    const availableWidths: number[] = edges.map(() => 0);
-    // Find available space for each label, by checking where the next label
-    // starts. We iterate from right to left through the spans, starting with
-    // the second-rightmost (i=1).
-    for (let i = 1; i < edges.length; i++) {
-      const edge = edges[i];          // this span
-      const nextEdge = edges[i - 1];  // right neighboring span
-      availableWidths[i] =
-          this.getStartX(nextEdge.span1) - this.getStartX(edge.span1);
-    }
-    // We don't want the rightmost label (index 0) to be cut off by the edge
-    // of the SVG draw area, even if the label extends past the end of the
-    // text. So we need to:
-    // 1) Set this label to fit the content, so the bounding box contains all
-    // the label text.
-    // 2) Set the available width to this rendered width, so we don't clip it
-    // later.
-    const firstSpanDiv =
-        // tslint:disable-next-line:no-unnecessary-type-assertion
-        layerGroup.querySelector('g.edge-group foreignObject div') as
-            HTMLDivElement |
-        null;
-    if (firstSpanDiv !== null) {
-      firstSpanDiv.style.width = 'fit-content';
-      availableWidths[0] = firstSpanDiv.getBoundingClientRect().width;
-    }
-    return availableWidths;
-  }
-
-  /* Set mouseovers, using d3. */
-  private setMouseovers(group: SVGGElement, edges: EdgeLabel[]) {
-    const rowColor = group.dataset['color'] as string;
-    const grayColor = getVizColor('deep', 'other').color;
-
-    const spanGroups = d3.select(group).selectAll('g.edge-group').data(edges);
-    const tokenSpans = d3.select(this.getTokenGroup()).selectAll('tspan');
-
-    // On mouseover, highlight this span and the corresponding text.
-    spanGroups.each(function(d, i) {
-      const colorFn = (e: unknown, j: number) =>
-          (i === j) ? rowColor : grayColor;
-      const tokenColorFn = (t: unknown, j: number) => {
-        const inSpan1 = (d.span1[0] <= j && j < d.span1[1]);
-        const inSpan2 = d.span2 ? (d.span2[0] <= j && j < d.span2[1]) : false;
-        return (inSpan1 || inSpan2) ? rowColor : 'black';
-      };
-      const mouseBox = d3.select(this).select('rect.mousebox');
-      mouseBox.on('mouseover', () => {
-        spanGroups.style('--group-color', colorFn);
-        tokenSpans.attr('fill', tokenColorFn);
-        d3.select(this).classed('selected', true);
-        // Ideally we'd also move this element so that it renders above
-        // the other groups, but SVG2 z-index is not supported by most browsers
-        // and simply reordering child nodes does not play well with lit-html's
-        // rendering logic, which relies on pointers to specific positions in
-        // the DOM.
-        // d3.select(this).classed('selected', true).raise();
-        // TODO(iftenney): consider implementing a tooltip that clones this
-        // element but always renders above the other spans.
-      });
-      mouseBox.on('mouseout', () => {
-        // Reset to original color, stored on group element.
-        // TODO(lit-dev): do this with another CSS class instead?
-        spanGroups.style('--group-color', function(e) {
-          return (this as SVGElement).dataset['color'] as string;
-        });
-        tokenSpans.attr('fill', 'black');
-        d3.select(this).classed('selected', false);
-      });
-    });
-  }
-
-  /* Set y-position of rendered layers */
-  private positionLayers() {
-    let rowStartY = this.getTokenGroup().getBBox().y - this.yPad / 2;
-    for (let i = 0; i < this.data.layers.length; i++) {
-      const group: SVGGElement = this.getLayerGroup(this.data.layers[i].name);
-      group.setAttribute('transform', `translate(0, ${rowStartY})`);
-      rowStartY -= group.getBBox().height + this.yPad;
-    }
-  }
-
-  /* Set the SVG viewport to the bounding box of the main group. */
-  private setSVGViewport() {
-    const mainGroup = this.shadowRoot!.querySelector('g#all') as SVGGElement;
-    const bbox = mainGroup.getBBox();
-    const svg = this.shadowRoot!.getElementById('svg')!;
-    // Set bounding box to cover main group + viewPad on all sides.
-    const viewBox = [
-      bbox.x - this.viewPad, bbox.y - this.viewPad,
-      bbox.width + 2 * this.viewPad, bbox.height + 2 * this.viewPad
-    ];
-    svg.setAttribute('viewBox', `${viewBox}`);
-    // Set the height of the SVG as it will render on the page.
-    svg.setAttribute(
-        'height', `${this.svgScaling * (bbox.height + 2 * this.viewPad)}`);
-  }
-
-  /**
-   * Post-render callback. Performs imperative updates to layout and component
-   * sizes which need to depend on the positions of each token. Also sets up
-   * mouseover behavior.
-   */
-  override updated() {
-    if (this.data == null) {
-      this.tokenXBounds = [];
-      return;
-    }
-    this.tokenXBounds = this.findTokenBounds();
-
-    // For each layer, position the span groups
-    for (const layer of this.data.layers) {
-      const orderedEdges = this.sortEdges(layer.edges);
-
-      // Container group for this layer.
-      const layerGroup: SVGGElement = this.getLayerGroup(layer.name);
-
-      // Compute available widths, needed for clipping of labels.
-      const availableWidths =
-          this.findAvailableWidths(layerGroup, orderedEdges);
-
-      // Edge groups within this layer.
-      const edgeGroups = layerGroup.querySelectorAll('g.edge-group');
-      edgeGroups.forEach((g, i) => {
-        const edge = orderedEdges[i];
-
-        const g1 = g.querySelector('g.at-span1')!;
-        // Set position within this row.
-        g1.setAttribute(
-            'transform', `translate(${this.getStartX(edge.span1)}, 0)`);
-
-        // Compute span width in SVG units, based on rendered token width.
-        const span1Width =
-            this.getEndX(edge.span1) - this.getStartX(edge.span1);
-        // Set points for span1 bracket.
-        const points1 =
-            hBracketPoints(span1Width, -1 * (this.bracketHeight - 1), 1);
-        g1.querySelector('polyline')!.setAttribute('points', `${points1}`);
-
-        // Set the width for the label; this will show ellipsis for the label
-        // text if it is longer.
-        // Leave a few pixels spacing if we can afford it, but don't go
-        // shorter than the token width.
-        const displayWidth = Math.max(span1Width, availableWidths[i] - 5);
-        g.querySelector('foreignObject')!.setAttribute(
-            'width', `${displayWidth}`);
-
-        // If there's a second span, set up bracket
-        // and draw arc from span1 -> span2 with the arrow on span1.
-        if (edge.span2) {
-          const g2 = g.querySelector('g.at-span2')!;
-          // Set position within this row.
-          g2.setAttribute(
-              'transform', `translate(${this.getStartX(edge.span2)}, 0)`);
-          // Compute span width in SVG units, based on rendered token width.
-          const span2Width =
-              this.getEndX(edge.span2) - this.getStartX(edge.span2);
-          const points2 =
-              hBracketPoints(span2Width, -1 * (this.bracketHeight - 1), 1);
-          g2.querySelector('polyline')!.setAttribute('points', `${points2}`);
-
-          // Draw arc.
-          const startY =
-              -1 * (this.bracketHeight + this.lineHeight + 1 /* pad */);
-          const x1 =
-              (this.getEndX(edge.span1) + this.getStartX(edge.span1)) / 2;
-          let x2 = (this.getEndX(edge.span2) + this.getStartX(edge.span2)) / 2;
-          // Adjust arc end to avoid overlapping arrows.
-          // See //nlp/saft/rendering/sentence-html-renderer.js
-          if (x2 > x1) {
-            x2 -= (this.arcArrowSize + 2);
-          } else {
-            x2 += (this.arcArrowSize + 2);
-          }
-          // Adjust arc height based on edge length (# tokens between
-          // midpoints). See nlp_saft::SentenceRenderer::CalculateDimensions()
-          // from //nlp/saft/rendering/sentence-html-rendering.cc
-          const mid1 = (edge.span1[1] + edge.span1[0]) / 2;
-          const mid2 = (edge.span2[1] + edge.span2[0]) / 2;
-          const l = Math.min(30, Math.abs(mid2 - mid1));
-          const arcHeight = Math.min(
-              this.arcBaseHeight + Math.round((10 - (l / 6.0)) * l),
-              this.arcMaxHeight);
-          g.querySelector('path.arc-path')!.setAttribute(
-              'd', `${arcPath(startY, x1, x2, arcHeight, this.arcAspect)}`);
-          g.querySelector('path.arc-arrow')!.setAttribute(
-              'd', `${arcArrow(startY, x1, this.arcArrowSize)}`);
-        }
-      });
-
-      // Set mouseover behavior for this layer.
-      this.setMouseovers(layerGroup, orderedEdges);
-    }
-
-    // Set mouseover boxes to match the _visible_ size of the label container.
-    this.shadowRoot!.querySelectorAll('g.edge-group').forEach(g => {
-      matchBBox(
-          g.querySelector('foreignObject') as SVGGElement,
-          g.querySelector('rect.mousebox') as SVGRectElement);
-    });
-
-    // Stack layers vertically, using bounding boxes to avoid occlusion.
-    this.positionLayers();
-    // Finally, after everything is positioned, set the viewport for the whole
-    // SVG.
-    this.setSVGViewport();
-  }
-}
-
-declare global {
-  interface HTMLElementTagNameMap {
-    'span-graph-vis': SpanGraphVis;
-  }
-}
diff --git a/lit_nlp/client/elements/span_graph_vis_vertical.css b/lit_nlp/client/elements/span_graph_vis_vertical.css
deleted file mode 100644
index 2431fed4..00000000
--- a/lit_nlp/client/elements/span_graph_vis_vertical.css
+++ /dev/null
@@ -1,103 +0,0 @@
-.holder {
-  display: flex;
-  font-family: 'Share Tech Mono', monospace;
-  position: relative;
-  color: #555;
-}
-.layer {
-  cursor: pointer;
-  color: var(--group-color);
-}
-.layer-label-vert {
-  top: calc(0px - var(--line-height));
-  position: absolute;
-  padding-left: 7px;
-  display: flex;
-  transform: rotate(0deg);
-  transition: .25s transform;
-  transform-origin: 10px 10px;
-  color: var(--group-color);
-}
-.layer-label-vert.hidden {
-  transform: rotate(-90deg);
-}
-.column {
-  position: relative;
-  height: 100%;
-  transition: .25s width;
-}
-.column.hidden {
-  width: 13px !important;
-  opacity: 0;
-}
-.tokens {
-  z-index: 1;
-}
-.line {
-  height: var(--line-height);
-  box-sizing: border-box;
-  padding: 0 7px;
-  text-align: right;
-  white-space: nowrap;
-}
-.token.selected{
-  color: black;
-}
-.child {
-  font-weight: bold;
-  filter: hue-rotate(-40deg);
-  border-width: 3px;
-}
-.parent {
-  font-weight: bold;
-  filter: hue-rotate(40deg);
-  border-width: 3px;
-}
-.selected {
-  font-weight: bold;
-  border-width: 3px;
-}
-
-.gray {
-  border-color: #ddd !important;
-  color: #ddd
-}
-
-.edge {
-  position: absolute;
-}
-.edge-line {
-  border: 1px solid var(--group-color);
-  border-left: 0;
-  width: 3px;
-  left: -3px;
-}
-.arrow-head {
-  border: 5px solid transparent;
-  border-right: 5px solid var(--group-color);
-  width: 0;
-  height: 0;
-  top: -5px;
-  left: -5px;
-  position: absolute;
-}
-.gray .arrow-head{
-  border-right-color: #ddd;
-}
-.arrow-head.bottom{
-  bottom: -5px;
-  top: unset;
-}
-.background-lines{
-  position: absolute;
-  width: 100%;
-  top: -3px;
-}
-.background-line {
-  height: var(--line-height);
-  width: 100%;
-  padding: 0 7px;
-}
-.background-line:nth-child(odd){
-  background: #f5f5f5;
-}
diff --git a/lit_nlp/client/elements/span_graph_vis_vertical.ts b/lit_nlp/client/elements/span_graph_vis_vertical.ts
deleted file mode 100644
index 86915ef5..00000000
--- a/lit_nlp/client/elements/span_graph_vis_vertical.ts
+++ /dev/null
@@ -1,278 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * Visualization component for structured prediction over text.
- */
-
-// tslint:disable:no-new-decorators
-
-import {property} from 'lit/decorators.js';
-import {customElement} from 'lit/decorators.js';
-import { html} from 'lit';
-import {classMap} from 'lit/directives/class-map.js';
-import {styleMap} from 'lit/directives/style-map.js';
-import {observable} from 'mobx';
-
-import {getVizColor} from '../lib/colors';
-import {EdgeLabel} from '../lib/dtypes';
-import {ReactiveElement} from '../lib/elements';
-
-import {styles} from './span_graph_vis_vertical.css';
-
-
-/**
- * Represents a group of directed graphs anchored to token spans.
- * This is the general "edge probing" representation, which can be used for many
- * problems including sequence tagging, span labeling, and directed graphs like
- * semantic frames, coreference, and dependency parsing. See
- * https://arxiv.org/abs/1905.06316 for more on this formalism.
- */
-export interface SpanGraph {
-  tokens: string[];
-  layers: AnnotationLayer[];
-}
-
-/**
- * A single layer of annotations, like 'pos' (part-of-speech)
- * or 'ner' (named entities).
- */
-export interface AnnotationLayer {
-  name: string;
-  edges: EdgeLabel[];
-  hideBracket?: boolean;
-}
-
-function formatEdgeLabel(label: string|number): string {
-  if (typeof (label) === 'number') {
-    return Number.isInteger(label) ? label.toString() :
-                                     label.toFixed(3).toString();
-  }
-  return `${label}`;
-}
-
-/** Structured prediction (SpanGraph) visualization class. */
-@customElement('span-graph-vis-vertical')
-export class SpanGraphVis extends ReactiveElement {
-  /* Data binding */
-  @property({ type: Object }) data: SpanGraph = { tokens: [], layers: [] };
-  @property({ type: Boolean }) showLayerLabel: boolean = true;
-
-  @observable private selectedTokIdx?: number;
-  @observable private readonly columnVisibility: { [key: string]: boolean } = {};
-
-  /* Rendering parameters */
-  @property({ type: Number }) lineHeight: number = 18;
-  @property({ type: Number }) approxFontSize = this.lineHeight / 3;
-
-  // Padding for SVG viewport, to avoid clipping some elements (like polyline).
-  @property({ type: Number }) viewPad: number = 5;
-
-  static override get styles() {
-    return styles;
-  }
-
-  override render() {
-    if (!this.data) {
-      return ``;
-    }
-    const host = this.shadowRoot!.host as HTMLElement;
-    host.style.setProperty('--line-height', `${this.lineHeight}pt`);
-    const tokens = this.data.tokens;
-
-    const tokenClasses = (i: number) => classMap({
-      line: true,
-      token: true,
-      selected: i === this.selectedTokIdx
-    });
-
-    // clang-format off
-    return html`
-    <div class='holder' >
-      <div class='background-lines'>
-        ${tokens.map(t => html`<div class=background-line></div>`)}
-      </div>
-      <div class='tokens'>
-          ${tokens.map((t, i) => html`
-          <div class=${tokenClasses(i)}
-            @mouseenter=${() => this.selectedTokIdx = i}
-            @mouseleave=${() => this.selectedTokIdx = undefined}>
-            ${t}
-          </div>
-          `)}
-      </div>
-      ${this.data.layers.map((layer, i) => this.renderLayer(layer, i))}
-    </div>`;
-    // clang-format on
-  }
-
-  /**
-   * Render a given annotation layer.
-   */
-  renderLayer(layer: AnnotationLayer, i: number) {
-
-    if (!layer.edges.length) {
-      return html``;
-    }
-
-    const layerStyles = styleMap({
-      '--group-color': getVizColor('dark', i).color
-    });
-
-    // The column width is the width of the longest label, in pixels.
-    const colWidth =
-        Math.max(
-            layer.name.length,
-            ...layer.edges.map(e => formatEdgeLabel(e.label).length)) *
-            this.approxFontSize +
-        this.viewPad * 2;
-
-    const colStyles = styleMap({ width: `${colWidth}pt` });
-    const hidden = this.columnVisibility[layer.name];
-    const columnClasses = classMap({
-      'column': true,
-      'hidden': hidden
-    });
-
-    const headerClasses = classMap({ 'layer-label-vert': true, hidden });
-    const onClick = () =>
-      this.columnVisibility[layer.name] = !this.columnVisibility[layer.name];
-
-    // clang-format off
-    return html`
-    <div class=layer style=${layerStyles} @click=${onClick}>
-      ${this.showLayerLabel ? html`
-      <div class=${headerClasses}>
-        ${layer.name}
-      </div>` : null}
-      <div style=${colStyles} class=${columnClasses}>
-        ${layer.edges.map(edge => this.renderEdge(edge, layer, colWidth))}
-      </div>
-    </div>
-    `;
-    // clang-format on
-  }
-
-  /**
-   * Render an edge and its label. See the note on the SpanGraph interface
-   * above for more details.
-   */
-  private renderEdge(edge: EdgeLabel, layer: AnnotationLayer, colWidth: number) {
-    const isArc = 'span2' in edge;
-    const span0 = edge.span1[0];
-    const span1 = edge.span2 ? edge.span2[0] : edge.span1[1];
-    const topSpan = Math.min(span0, span1);
-    const botSpan = Math.max(span0, span1);
-
-
-    const isInSpan = (i: number, span:[number, number]) => i >= span[0] && i < span[1];
-
-    // Span classes (child, parent, etc, based on the currently selected token.)
-    const tokSelected = this.selectedTokIdx !== undefined;
-    const selected = isInSpan(this.selectedTokIdx!, edge.span1) || (isArc && isInSpan(this.selectedTokIdx!, edge.span2!));
-    const child = isArc && this.selectedTokIdx === span1;
-    const parent = isArc && this.isChildOfSelected(layer, span0);
-    const grayLine = tokSelected && !(selected || child);
-    const grayLabel = grayLine && !(parent);
-
-    // Edge labels can be either strings or numbers; format the latter nicely.
-    const formattedLabel = formatEdgeLabel(edge.label);
-
-    // Styling for the label text.
-    const labelWidthInPx = formattedLabel.length * this.approxFontSize;
-    const labelStyle = styleMap({
-      top: `${span0 * this.lineHeight}pt`,
-      left: isArc ? `${colWidth - labelWidthInPx - this.viewPad}pt` : '',
-    });
-    const labelClasses = classMap({
-      child, parent, selected,
-      gray: grayLabel,
-      line: true,
-      edge: true
-    });
-
-    // Styling for the arc (a line and sometimes an arrowhead)
-    const arcPad = .3;
-    const offset = this.lineHeight / 8;
-    const top = isArc ?
-      (topSpan + arcPad) * this.lineHeight + (topSpan === span0 ? 0 : this.viewPad) :
-      topSpan * (this.lineHeight) - offset;
-    const bottom = isArc ?
-      (botSpan + arcPad) * this.lineHeight + (botSpan === span0 ? 0 : -this.viewPad) :
-      botSpan * (this.lineHeight) - 2 * offset;
-
-    const arcHeight = bottom - top;
-    const width = isArc ? `${Math.max(arcHeight / 2, this.lineHeight / 2)}pt` : '';
-
-    const rad = isArc ? arcHeight / 2 : 3;
-    const lineStyle = styleMap({
-      top: `${top}pt`,
-      height: `${arcHeight}pt`,
-      width,
-      'border-radius': `0pt ${rad}pt ${rad}pt 0pt`,
-      left: isArc ? `${colWidth + 10}pt` : '',
-      visibility: layer.hideBracket ? 'hidden' : 'visble',
-    });
-
-    const arrowHeadClasses = classMap({
-      'arrow-head': true,
-      'bottom': topSpan === span1,
-    });
-
-    const arrowClasses = classMap({
-      child,
-      parent: selected,
-      gray: grayLine,
-      edge: true,
-      'edge-line': true
-    });
-
-    return html`
-    <div style=${lineStyle} class='${arrowClasses}'>
-      ${isArc ? html`<div class=${arrowHeadClasses}></div>` : ''}
-    </div>
-    <div class=${labelClasses}
-      style=${labelStyle}>
-      ${formattedLabel}
-    </div>
-    `;
-  }
-
-  /**
-   * Is this token (indicated by tokenIdx) a child of the selected token at
-   * the specified layer. This assumes that the edge goes from span1 to span2,
-   * as in a dependency parse tree.
-   */
-  isChildOfSelected(layer: AnnotationLayer, tokenIdx: number) {
-    for (let j = 0; j < layer.edges.length; j++) {
-      const edge = layer.edges[j];
-      if (edge.span2 &&
-        (this.selectedTokIdx === edge.span1[0]) &&
-        (tokenIdx === edge.span2[0])) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-}
-
-declare global {
-  interface HTMLElementTagNameMap {
-    'span-graph-vis-vertical': SpanGraphVis;
-  }
-}
diff --git a/lit_nlp/client/modules/annotated_text_module.ts b/lit_nlp/client/modules/annotated_text_module.ts
index 286d8c76..7165cb12 100644
--- a/lit_nlp/client/modules/annotated_text_module.ts
+++ b/lit_nlp/client/modules/annotated_text_module.ts
@@ -6,7 +6,7 @@
  * spans in running text, which is well-suited for tasks like QA or entity
  * recognition which have a small number of spans over a longer passage.
  *
- * Similar to span_graph_module, we provide two module classes:
+ * We provide two module classes:
  * - AnnotatedTextGoldModule for gold annotations (in the input data)
  * - AnnotatedTextModule for model predictions
  */
@@ -14,18 +14,17 @@
 // tslint:disable:no-new-decorators
 import '../elements/annotated_text_vis';
 
+import {html} from 'lit';
 import {customElement} from 'lit/decorators.js';
-import { html} from 'lit';
 import {observable} from 'mobx';
 
 import {LitModule} from '../core/lit_module';
 import {type AnnotationGroups, TextSegments} from '../elements/annotated_text_vis';
 import {MultiSegmentAnnotations, TextSegment} from '../lib/lit_types';
+import {styles as sharedStyles} from '../lib/shared_styles.css';
 import {type IndexedInput, ModelInfoMap, Spec} from '../lib/types';
 import {doesOutputSpecContain, filterToKeys, findSpecKeys} from '../lib/utils';
 
-import {styles as sharedStyles} from '../lib/shared_styles.css';
-
 /** LIT module for model output. */
 @customElement('annotated-text-gold-module')
 export class AnnotatedTextGoldModule extends LitModule {
@@ -80,7 +79,8 @@ export class AnnotatedTextGoldModule extends LitModule {
     // clang-format on
   }
 
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
+  static override shouldDisplayModule(
+      modelSpecs: ModelInfoMap, datasetSpec: Spec) {
     return findSpecKeys(datasetSpec, MultiSegmentAnnotations).length > 0;
   }
 }
@@ -159,7 +159,8 @@ export class AnnotatedTextModule extends LitModule {
     // clang-format on
   }
 
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
+  static override shouldDisplayModule(
+      modelSpecs: ModelInfoMap, datasetSpec: Spec) {
     return doesOutputSpecContain(modelSpecs, MultiSegmentAnnotations);
   }
 }
diff --git a/lit_nlp/client/modules/span_graph_module.ts b/lit_nlp/client/modules/span_graph_module.ts
deleted file mode 100644
index 52bc33a5..00000000
--- a/lit_nlp/client/modules/span_graph_module.ts
+++ /dev/null
@@ -1,346 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * Module within LIT for showing sequence and span tagging
- * results.
- */
-
-// tslint:disable:no-new-decorators
-import '../elements/span_graph_vis';
-import '../elements/span_graph_vis_vertical';
-
-import {customElement} from 'lit/decorators.js';
-import {css, html} from 'lit';
-import {computed, observable} from 'mobx';
-
-import {LitModule} from '../core/lit_module';
-import {AnnotationLayer, SpanGraph} from '../elements/span_graph_vis_vertical';
-import {EdgeLabel, SpanLabel} from '../lib/dtypes';
-import {EdgeLabels, SequenceTags, SpanLabels, LitTypeTypesList, LitTypeWithAlign, TextSegment, Tokens} from '../lib/lit_types';
-import {IndexedInput, Input, ModelInfoMap, Preds, Spec} from '../lib/types';
-import {findSpecKeys} from '../lib/utils';
-
-import {styles as sharedStyles} from '../lib/shared_styles.css';
-
-interface FieldNameMultimap {
-  [fieldName: string]: string[];
-}
-
-interface Annotations {
-  [tokenKey: string]: SpanGraph;
-}
-
-// Shared by gold and preds modules.
-const moduleStyles = css`
-  .outer-container {
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    position: relative;
-    overflow: hidden;
-  }
-
-  .token-group {
-    padding-top: 40px;
-  }
-
-  .field-title {
-    padding: 4px;
-  }
-`;
-
-const supportedPredTypes: LitTypeTypesList =
-    [SequenceTags, SpanLabels, EdgeLabels];
-
-/**
- * Convert sequence tags to a list of length-1 span labels.
- */
-function tagsToEdges(tags: string[]): EdgeLabel[] {
-  return tags.map((label: string, i: number) => {
-    return {span1: [i, i + 1], label} as EdgeLabel;
-  });
-}
-
-/**
- * Convert span labels to single-sided edge labels.
- */
-function spansToEdges(spans: SpanLabel[]): EdgeLabel[] {
-  return spans.map(
-      d => ({span1: [d.start, d.end], label: d.label as string} as EdgeLabel));
-}
-
-function mapTokenToTags(spec: Spec): FieldNameMultimap {
-  const tagKeys = findSpecKeys(spec, supportedPredTypes);
-  const tokenKeys = findSpecKeys(spec, Tokens);
-
-  // Make a mapping of token keys to one or more tag sets
-  const tokenToTags: FieldNameMultimap = {};
-  for (const tagKey of tagKeys) {
-    const {align: tokenKey} = spec[tagKey] as LitTypeWithAlign;
-    if (tokenKey == null || !tokenKeys.includes(tokenKey)) {
-      continue;
-    } else if (tokenToTags[tokenKey] == null) {
-      tokenToTags[tokenKey] = [];
-    }
-    tokenToTags[tokenKey].push(tagKey);
-  }
-  return tokenToTags;
-}
-
-function parseInput(data: Input|Preds, spec: Spec): Annotations {
-  const tokenToTags = mapTokenToTags(spec);
-
-  // Render a row for each set of tokens
-  const ret: Annotations = {};
-  for (const tokenKey of Object.keys(tokenToTags)) {
-    const annotationLayers: AnnotationLayer[] = [];
-    for (const tagKey of tokenToTags[tokenKey]) {
-      let edges = data[tagKey];
-      let hideBracket = false;
-      // Temporary workaround: if we manually create a new datapoint, the span
-      // or tag field may be "" rather than [].
-      // TODO(lit-team): remove this once the datapoint editor is type-safe
-      // for structured fields.
-      if (edges.length === 0) {
-        edges = [];
-      }
-      if (spec[tagKey] instanceof SequenceTags) {
-        edges = tagsToEdges(edges);
-        hideBracket = true;
-      } else if (spec[tagKey] instanceof SpanLabels) {
-        edges = spansToEdges(edges);
-      }
-      annotationLayers.push({name: tagKey, edges, hideBracket});
-    }
-    // Try to infer tokens from text, if that field is empty.
-    let tokens = data[tokenKey];
-    if (tokens.length === 0) {
-      const textKey = findSpecKeys(spec, TextSegment)[0];
-      tokens = data[textKey].split();
-    }
-    ret[tokenKey] = {tokens, layers: annotationLayers};
-  }
-  return ret;
-}
-
-function renderTokenGroups(
-    data: Annotations, spec: Spec, orientation: 'horizontal'|'vertical') {
-  const tokenToTags = mapTokenToTags(spec);
-  const visElement = (data: SpanGraph, showLayerLabel: boolean) => {
-    if (orientation === 'vertical') {
-      return html`<span-graph-vis-vertical .data=${data} .showLayerLabel=${
-          showLayerLabel}></span-graph-vis-vertical>`;
-    } else {
-      return html`<span-graph-vis .data=${data} .showLayerLabel=${
-          showLayerLabel}></span-graph-vis>`;
-    }
-  };
-  // clang-format off
-  return html`${Object.keys(tokenToTags).map(tokenKey => {
-    const labelHere = data[tokenKey]?.layers?.length === 1;
-    return html`
-      <div id=${tokenKey} class="token-group">
-        ${labelHere ?
-          html`<div class='field-title'>${data[tokenKey].layers[0].name}</div>`
-          : null}
-        ${visElement(data[tokenKey], !labelHere)}
-      </div>
-    `;
-  })}`;
-  // clang-format on
-}
-
-/** Gold predictions module class. */
-@customElement('span-graph-gold-module')
-export class SpanGraphGoldModule extends LitModule {
-  static override title = 'Structured Prediction (gold)';
-  static override duplicateForExampleComparison = true;
-  static override duplicateForModelComparison = false;
-  static override duplicateAsRow = false;
-  static override numCols = 4;
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => html`
-  <span-graph-gold-module model=${model} .shouldReact=${shouldReact}
-    selectionServiceIndex=${selectionServiceIndex}>
-  </span-graph-gold-module>`;
-  static orientation = 'horizontal';
-
-  @computed
-  get dataSpec() {
-    return this.appState.currentDatasetSpec;
-  }
-
-  @computed
-  get goldDisplayData(): Annotations {
-    const input = this.selectionService.primarySelectedInputData;
-    if (input === null) {
-      return {};
-    } else {
-      return parseInput(input.data, this.dataSpec);
-    }
-  }
-
-  static override get styles() {
-    return [sharedStyles, moduleStyles];
-  }
-
-  // tslint:disable:no-any
-  override renderImpl() {
-    // If more than one model is selected, SpanGraphModule will be offset
-    // vertically due to the model name header, while this one won't be.
-    // So, add an offset so that the content still aligns when there is a
-    // SpanGraphGoldModule and a SpanGraphModule side-by-side.
-    const offsetForHeader = !this.appState.compareExamplesEnabled &&
-        this.appState.currentModels.length > 1;
-    // clang-format off
-    return html`
-      ${offsetForHeader? html`<div class='offset-for-module-header'></div>` : null}
-      <div id="gold-group" class='outer-container'>
-        ${
-        renderTokenGroups(
-            this.goldDisplayData, this.dataSpec,
-            (this.constructor as any).orientation)}
-      </div>
-    `;
-    // clang-format on
-  }
-  // tslint:enable:no-any
-
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
-    const hasTokens = findSpecKeys(datasetSpec, Tokens).length > 0;
-    const hasSupportedPreds =
-        findSpecKeys(datasetSpec, supportedPredTypes).length > 0;
-    return (hasTokens && hasSupportedPreds);
-  }
-}
-
-/** Model output module class. */
-@customElement('span-graph-module')
-export class SpanGraphModule extends LitModule {
-  static override title = 'Structured Prediction (model preds)';
-  static override duplicateForExampleComparison = true;
-  static override duplicateAsRow = false;
-  static override numCols = 4;
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => html`
-  <span-graph-module model=${model} .shouldReact=${shouldReact}
-    selectionServiceIndex=${selectionServiceIndex}>
-  </span-graph-module>`;
-  static orientation = 'horizontal';
-
-  @computed
-  get predSpec() {
-    return this.appState.getModelSpec(this.model).output;
-  }
-
-  // This is updated with an API call, via a reaction.
-  @observable predDisplayData: Annotations = {};
-
-  private async updatePredDisplayData(input: IndexedInput|null) {
-    if (input === null) {
-      this.predDisplayData = {};
-    } else {
-      const promise = this.apiService.getPreds(
-          [input], this.model, this.appState.currentDataset,
-          [Tokens, ...supportedPredTypes]);
-
-      const results = await this.loadLatest('getPreds', promise);
-      if (!results) return;
-
-      this.predDisplayData = parseInput(results[0], this.predSpec);
-    }
-  }
-
-  static override get styles() {
-    return [sharedStyles, moduleStyles];
-  }
-
-  override firstUpdated() {
-    this.reactImmediately(
-        () => this.selectionService.primarySelectedInputData, input => {
-          this.updatePredDisplayData(input);
-        });
-  }
-
-  // tslint:disable:no-any
-  override renderImpl() {
-    return html`
-      <div id="pred-group" class='outer-container'>
-        ${
-        renderTokenGroups(
-            this.predDisplayData, this.predSpec,
-            (this.constructor as any).orientation)}
-      </div>
-    `;
-  }
-  // tslint:enable:no-any
-
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
-    const models = Object.keys(modelSpecs);
-    for (let modelNum = 0; modelNum < models.length; modelNum++) {
-      const spec = modelSpecs[models[modelNum]].spec;
-      const hasTokens = findSpecKeys(spec.output, Tokens).length > 0;
-      const hasSupportedPreds =
-          findSpecKeys(spec.output, supportedPredTypes).length > 0;
-      if (hasTokens && hasSupportedPreds) {
-        return true;
-      }
-    }
-    return false;
-  }
-}
-
-// tslint:disable:class-as-namespace
-
-/** Gold predictions module class. */
-@customElement('span-graph-gold-module-vertical')
-export class SpanGraphGoldModuleVertical extends SpanGraphGoldModule {
-  static override duplicateAsRow = true;
-  static override orientation = 'vertical';
-  static override numCols = 4;
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => html`
-  <span-graph-gold-module-vertical model=${model} .shouldReact=${shouldReact}
-    selectionServiceIndex=${selectionServiceIndex}>
-  </span-graph-gold-module-vertical>`;
-}
-
-/** Model output module class. */
-@customElement('span-graph-module-vertical')
-export class SpanGraphModuleVertical extends SpanGraphModule {
-  static override duplicateAsRow = true;
-  static override orientation = 'vertical';
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => html`
-  <span-graph-module-vertical model=${model} .shouldReact=${shouldReact}
-    selectionServiceIndex=${selectionServiceIndex}>
-  </span-graph-module-vertical>`;
-}
-
-// tslint:enable:class-as-namespace
-
-declare global {
-  interface HTMLElementTagNameMap {
-    'span-graph-gold-module': SpanGraphGoldModule;
-    'span-graph-module': SpanGraphModule;
-    // TODO(b/172979677): make these parameterized versions, rather than
-    // separate classes.
-    'span-graph-gold-module-vertical': SpanGraphGoldModuleVertical;
-    'span-graph-module-vertical': SpanGraphModuleVertical;
-  }
-}

From 27d7a841cf6d514e67ebfb2af9f603398499f6e3 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Thu, 20 Jun 2024 08:00:58 -0700
Subject: [PATCH 27/47] Remove attention module from LIT.

PiperOrigin-RevId: 645032202
---
 lit_nlp/api/layout.py                         |   3 -
 lit_nlp/client/modules/attention_module.ts    | 350 ------------------
 .../client/services/modules_service_test.ts   |  11 +-
 3 files changed, 6 insertions(+), 358 deletions(-)
 delete mode 100644 lit_nlp/client/modules/attention_module.ts

diff --git a/lit_nlp/api/layout.py b/lit_nlp/api/layout.py
index 188c8cd4..b39a2eb7 100644
--- a/lit_nlp/api/layout.py
+++ b/lit_nlp/api/layout.py
@@ -32,7 +32,6 @@ class LitModuleName(dtypes.EnumSerializableAsValues, enum.Enum):
   # keep-sorted start
   AnnotatedTextGoldModule = 'annotated-text-gold-module'
   AnnotatedTextModule = 'annotated-text-module'
-  AttentionModule = 'attention-module'
   ClassificationModule = 'classification-module'
   ConfusionMatrixModule = 'confusion-matrix-module'
   CurvesModule = 'curves-module'
@@ -198,7 +197,6 @@ def to_json(self) -> dtypes.JsonDict:
         'Explanations': [
             modules.SalienceMapModule,
             modules.LegacySequenceSalienceModule,
-            modules.AttentionModule,
             modules.FeatureAttributionModule,
         ],
         'Clustering': [modules.SalienceClusteringModule],
@@ -232,7 +230,6 @@ def to_json(self) -> dtypes.JsonDict:
             *MODEL_PREDS_MODULES,
             modules.SalienceMapModule,
             modules.LegacySequenceSalienceModule,
-            modules.AttentionModule,
             modules.FeatureAttributionModule,
         ],
         'Salience Clustering': [modules.SalienceClusteringModule],
diff --git a/lit_nlp/client/modules/attention_module.ts b/lit_nlp/client/modules/attention_module.ts
deleted file mode 100644
index ee9aa107..00000000
--- a/lit_nlp/client/modules/attention_module.ts
+++ /dev/null
@@ -1,350 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * Module within LIT the model's attention for a single input
- */
-
-// tslint:disable:no-new-decorators
-import {css, html, svg} from 'lit';
-import {customElement} from 'lit/decorators.js';
-import {classMap} from 'lit/directives/class-map.js';
-import {observable} from 'mobx';
-
-import {app} from '../core/app';
-import {LitModule} from '../core/lit_module';
-import {getBrandColor} from '../lib/colors';
-import {AttentionHeads as AttentionHeadsLitType, Tokens as TokensLitType} from '../lib/lit_types';
-import {styles as sharedStyles} from '../lib/shared_styles.css';
-import {IndexedInput, ModelInfoMap, SCROLL_SYNC_CSS_CLASS, Spec} from '../lib/types';
-import {doesOutputSpecContain, findSpecKeys, getTextWidth, getTokOffsets, sumArray} from '../lib/utils';
-import {FocusService} from '../services/services';
-
-type Tokens = string[];
-// <float>[num_heads, num_tokens, num_tokens]
-type AttentionHeads = number[][][];
-
-/**
- * A LIT module that renders the model's attention for a single input.
- */
-@customElement('attention-module')
-export class AttentionModule extends LitModule {
-  static override title = 'Attention';
-  static override infoMarkdown =
-      `Visualize \`AttentionHeads\`-type fields from your model.<br>
-      [Learn more.](https://pair-code.github.io/lit/documentation/components.md#attention)`;
-  static override numCols = 3;
-  static override collapseByDefault = true;
-  static override duplicateForExampleComparison = true;
-  static override template =
-      (model: string, selectionServiceIndex: number, shouldReact: number) => {
-        return html`
-      <attention-module model=${model} .shouldReact=${shouldReact}
-        selectionServiceIndex=${selectionServiceIndex}>
-      </attention-module>`;
-      };
-
-  static override get styles() {
-    const styles = css`
-        .head-selector-chip {
-          margin: 0px 1px;
-          width: 1rem;
-          text-align: center;
-        }
-
-        .head-selector-chip.selected {
-          color: var(--lit-cyea-600);
-          border-color: var(--lit-cyea-600);
-        }
-
-        .head-selector-chip:hover {
-          background: var(--lit-cyea-100);
-          cursor: pointer;
-        }
-
-        .padded-container {
-          padding: 4px 8px;
-        }
-    `;
-    return [sharedStyles, styles];
-  }
-
-  private readonly focusService = app.getService(FocusService);
-  private clearFocusTimer: number|undefined;
-
-  @observable private selectedLayer?: string;
-  @observable private selectedHeadIndex = 0;
-  @observable private preds?: {[key: string]: Tokens|AttentionHeads};
-
-  override firstUpdated() {
-    const getAttnInputs = () =>
-        [this.selectionService.primarySelectedInputData, this.selectedLayer];
-    this.reactImmediately(getAttnInputs, (selectedInput, selectedLayer) => {
-      this.updateSelection(
-          this.selectionService.primarySelectedInputData, this.selectedLayer!);
-    });
-  }
-
-  private async updateSelection(
-      selectedInput: IndexedInput|null, layer: string) {
-    this.preds = undefined;  // clear previous results
-
-    if (selectedInput === null) return;
-    const dataset = this.appState.currentDataset;
-    const promise = this.apiService.getPreds(
-        [selectedInput], this.model, dataset, [TokensLitType], [layer],
-        'Fetching attention');
-    const res = await this.loadLatest('attentionAndTokens', promise);
-    if (res === null) return;
-    this.preds = res[0];
-    // Make sure head selection is valid.
-    const numHeadsPerLayer =
-        this.preds[layer] != null ? this.preds[layer].length : 0;
-    if (this.selectedHeadIndex >= numHeadsPerLayer) {
-      this.selectedHeadIndex = 0;
-    }
-  }
-
-  override renderImpl() {
-    // Scrolling inside this module is done inside the module-results-area div.
-    // Giving this div the class defined by SCROLL_SYNC_CSS_CLASS allows
-    // scrolling to be sync'd instances of this module when doing comparisons
-    // between models and/or duplicated datapoints. See lit_module.ts for more
-    // details.
-    // clang-format off
-    return html`
-      <div class='module-container'>
-        <div class='module-toolbar'>
-          ${this.renderLayerSelector()}
-          ${this.renderHeadSelector()}
-        </div>
-        <div class='module-results-area padded-container ${SCROLL_SYNC_CSS_CLASS}'>
-          ${this.preds != null ? this.renderAttnHead(): null}
-        </div>
-      </div>
-    `;
-    // clang-format on
-  }
-
-  private renderAttnHead() {
-    const {output: outputSpec} = this.appState.getModelSpec(this.model);
-    const fieldSpec =
-        outputSpec[this.selectedLayer!] as AttentionHeadsLitType;
-
-    // Tokens involved in the attention.
-    const inToks = (this.preds!)[fieldSpec.align_in!] as Tokens;
-    const outToks = (this.preds!)[fieldSpec.align_out!] as Tokens;
-
-    const fontFamily = '\'Share Tech Mono\', monospace';
-    const fontSize = 12;
-    const defaultCharWidth = 6.5;
-    const font = `${fontSize}px ${fontFamily}`;
-    const inTokWidths = inToks.map(tok => getTextWidth(tok, font, defaultCharWidth));
-    const outTokWidths = outToks.map(tok => getTextWidth(tok, font, defaultCharWidth));
-    const spaceWidth = getTextWidth(' ', font, defaultCharWidth);
-
-    // Height of the attention visualization part.
-    const visHeight = 100;
-
-    // Vertical pad between attention vis and words.
-    const pad = 10;
-
-    // Calculate the full width and height.
-    const inTokWidth = sumArray(inTokWidths) + (inToks.length - 1) * spaceWidth;
-    const outTokWidth = sumArray(outTokWidths) + (outToks.length - 1) * spaceWidth;
-    const width = Math.max(inTokWidth, outTokWidth);
-    const height = visHeight + fontSize * 2 + pad * 4;
-    const inTokOffsets = getTokOffsets(inTokWidths, spaceWidth);
-    const outTokOffsets = getTokOffsets(outTokWidths, spaceWidth);
-
-    // If focus is one any of the tokens in the attention viz, then only show
-    // attention info for those tokens.
-    const focusData = this.focusService.focusData;
-    let inTokenIdxFocus = null;
-    let outTokenIdxFocus =  null;
-    const primaryDatapointFocused = focusData != null &&
-        focusData.datapointId ===
-          this.selectionService.primarySelectedInputData!.id;
-    if (primaryDatapointFocused &&
-        (focusData!.fieldName === fieldSpec.align_out ||
-         focusData!.fieldName === fieldSpec.align_in)) {
-      inTokenIdxFocus = focusData!.fieldName === fieldSpec.align_in
-          ? focusData!.subField: -1;
-      outTokenIdxFocus = focusData!.fieldName === fieldSpec.align_out
-          ? focusData!.subField: -1;
-    }
-    const clearFocus = () => {
-      if (this.clearFocusTimer != null) {
-        clearTimeout(this.clearFocusTimer);
-      }
-      this.clearFocusTimer = window.setTimeout(() => {
-        this.focusService.clearFocus();
-      }, 500) as unknown as number;
-    };
-
-    const toksRender = (tok: string, i: number, isInputToken: boolean) => {
-      const alignVal =
-          isInputToken ? fieldSpec.align_in! : fieldSpec.align_out!;
-      const mouseOver = () => {
-        clearTimeout(this.clearFocusTimer);
-        this.focusService.setFocusedField(
-            this.selectionService.primarySelectedInputData!.id,
-            'input',
-            alignVal,
-            i);
-      };
-      const mouseOut = () => {
-        clearFocus();
-      };
-      const x = isInputToken ? inTokOffsets[i] : outTokOffsets[i];
-      const text = svg`${tok}`;
-      let opacity = 1;
-      if (primaryDatapointFocused &&
-          focusData!.fieldName === alignVal &&
-          focusData!.subField !== i) {
-        opacity = 0.2;
-      }
-
-      const y = isInputToken ? visHeight + 4 * pad : pad * 2;
-      return svg`<text y=${y} x=${x} opacity=${opacity}
-          @mouseover=${mouseOver} @mouseout=${mouseOut}> ${text}</text>`;
-    };
-
-    // clang-format off
-    return svg`
-    <svg width=${width} height=${height}
-      font-family="${fontFamily}"
-      font-size="${fontSize}px">
-      ${outToks.map((tok, i) => toksRender(tok, i, false))}
-      ${this.renderAttnLines(visHeight, spaceWidth, 2.5 * pad, inTokWidths,
-                             outTokWidths, inTokenIdxFocus, outTokenIdxFocus)}
-      ${inToks.map((tok, i) => toksRender(tok, i, true))}
-    </svg>
-    `;
-    // clang-format on
-  }
-
-  /**
-   * Render the actual lines between tokens to show the attention values.
-   */
-  private renderAttnLines(
-      visHeight: number, spaceWidth: number, pad: number, inTokWidths: number[],
-      outTokWidths: number[], inTokenIdxFocus: number|null,
-      outTokenIdxFocus: number|null) {
-    const inTokOffsets = getTokOffsets(inTokWidths, spaceWidth);
-    const outTokOffsets = getTokOffsets(outTokWidths, spaceWidth);
-    const y1 = pad;
-    const y2 = pad + visHeight;
-
-    const xIn = (i: number) => inTokOffsets[i] + (inTokWidths[i] / 2);
-    const xOut = (i: number) => outTokOffsets[i] + (outTokWidths[i] / 2);
-
-    const heads = this.preds![this.selectedLayer!] as AttentionHeads;
-
-    // clang-format off
-    return heads[this.selectedHeadIndex].map(
-        (attnVals: number[], i: number) => {
-          return svg`
-            ${attnVals.map((attnVal: number, j: number) => {
-              // If token focus index is not null and not equal to an endpoint
-              // of an attention line, then do not render it.
-              if (inTokenIdxFocus != null && outTokenIdxFocus != null &&
-                  (i !== inTokenIdxFocus && j !== outTokenIdxFocus)) {
-                return null;
-              } else {
-                return svg`
-                  <line
-                    x1=${xOut(i)}
-                    y1=${y1}
-                    x2=${xIn(j)}
-                    y2=${y2}
-                    stroke="${getBrandColor('cyea', '600').color}"
-                    stroke-opacity="${attnVal}"
-                    stroke-width=2>
-                  </line>`;
-              }
-          })}`;
-        });
-    // clang-format on
-  }
-
-  /**
-   * Render the dropdown with the layer names.
-   */
-  private renderLayerSelector() {
-    const {output: outputSpec} = this.appState.getModelSpec(this.model);
-    const attnKeys = findSpecKeys(outputSpec, AttentionHeadsLitType);
-    if (this.selectedLayer === undefined) {
-      this.selectedLayer = attnKeys[0];
-    }
-    if (this.preds == null) {
-      return;
-    }
-    const onchange = (e: Event) => {
-      this.selectedLayer = (e.target as HTMLSelectElement).value;
-    };
-    // clang-format off
-    return html`
-      <select class="dropdown" @change=${onchange}>
-        ${attnKeys.map(key =>
-          html`<option value=${key} ?selected=${key === this.selectedLayer}>
-                 ${key}
-               </option>`)}
-      </select>
-    `;
-    // clang-format on
-  }
-
-  /**
-   * Render the dropdown for the attention head index.
-   */
-  private renderHeadSelector() {
-    const renderChip = (i: number) => {
-      const handleClick = () => {
-        this.selectedHeadIndex = i;
-      };
-      const classes = classMap({
-        'head-selector-chip': true,
-        'token-chip-function': true,
-        'selected': i === this.selectedHeadIndex,
-      });
-      return html`<div class=${classes} @click=${handleClick}>${i}</div>`;
-    };
-    if (this.preds == null || this.preds[this.selectedLayer!] == null) {
-      return;
-    }
-    const numHeadsPerLayer = this.preds![this.selectedLayer!].length;
-    const numHeadsPerLayerRange =
-        Array.from({length: numHeadsPerLayer}, (x: string, i: number) => i);
-    // clang-format off
-    return html`
-        <label class="head-selector-label">Head:</label>
-        ${numHeadsPerLayerRange.map(renderChip)}
-    `;
-    // clang-format on
-  }
-
-  static override shouldDisplayModule(modelSpecs: ModelInfoMap, datasetSpec: Spec) {
-    return doesOutputSpecContain(modelSpecs, AttentionHeadsLitType);
-  }
-}
-
-declare global {
-  interface HTMLElementTagNameMap {
-    'attention-module': AttentionModule;
-  }
-}
diff --git a/lit_nlp/client/services/modules_service_test.ts b/lit_nlp/client/services/modules_service_test.ts
index 6ea613be..b74c16a4 100644
--- a/lit_nlp/client/services/modules_service_test.ts
+++ b/lit_nlp/client/services/modules_service_test.ts
@@ -22,8 +22,8 @@ import {toJS} from 'mobx';
 import {LitApp} from '../core/app';
 import {mockMetadata} from '../lib/testing_utils';
 import {IndexedInput, LitCanonicalLayout} from '../lib/types';
-import {AttentionModule} from '../modules/attention_module';
 import {DatapointEditorModule} from '../modules/datapoint_editor_module';
+import {SequenceSalienceModule} from '../modules/sequence_salience_module';
 
 import {ApiService} from './api_service';
 import {ModulesService} from './modules_service';
@@ -38,7 +38,7 @@ const MOCK_LAYOUT: LitCanonicalLayout = {
   lower: {
     'internals': [
       // Duplicated per model and in compareDatapoints mode.
-      'attention-module',
+      'sequence-salience-module',
     ],
   },
   left: {},
@@ -94,9 +94,10 @@ describe('modules service test', () => {
 
     // Check that the two modules we added to the layout are reflected in
     // allModuleKeys.
-    const keys =
-        new Set([`Main_${DatapointEditorModule.title}`,
-                 `internals_${AttentionModule.title}`]);
+    const keys = new Set([
+      `Main_${DatapointEditorModule.title}`,
+      `internals_${SequenceSalienceModule.title}`
+    ]);
     expect(modulesService.allModuleKeys).toEqual(keys);
   });
 

From 886301972ec1e7ed274040b46ec0e0c3f34c8ace Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Fri, 21 Jun 2024 11:01:32 -0700
Subject: [PATCH 28/47] Fix import in TyDi model.

PiperOrigin-RevId: 645441653
---
 lit_nlp/examples/tydi/model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/lit_nlp/examples/tydi/model.py b/lit_nlp/examples/tydi/model.py
index a304dc5b..7f35f760 100644
--- a/lit_nlp/examples/tydi/model.py
+++ b/lit_nlp/examples/tydi/model.py
@@ -2,7 +2,7 @@
 from collections.abc import Iterable
 from lit_nlp.api import model as lit_model
 from lit_nlp.api import types as lit_types
-from lit_nlp.examples.datasets import question_answering
+from lit_nlp.examples.tydi import data as tydi_data
 import numpy as np
 import transformers
 
@@ -104,7 +104,7 @@ def input_spec(self):
         "question": lit_types.TextSegment(),
         "answers_text": lit_types.MultiSegmentAnnotations(),
         "language": lit_types.CategoryLabel(
-            required=False, vocab=question_answering.TYDI_LANG_VOCAB
+            required=False, vocab=tydi_data.TYDI_LANG_VOCAB
         ),
     }
 

From afd51fe299c0070a19946a789984957f14a9b5bb Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Fri, 21 Jun 2024 11:59:58 -0700
Subject: [PATCH 29/47] Update code links in LIT documentation to point to new
 demo locations.

PiperOrigin-RevId: 645459602
---
 website/sphinx_src/demos.md | 4 ++--
 website/sphinx_src/faq.md   | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 89812af9..31c057da 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/.
 ### Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \
-**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 *   Multi-task demo:
     *   Sentiment analysis as a binary classification task
@@ -50,7 +50,7 @@ https://pair-code.github.io/lit/tutorials/sentiment
 ### Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \
-**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 *   STS-B textual similarity task, predicting scores on a range from 0
     (unrelated) to 5 (very similar).
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index 7ebc1ae1..2d4a3242 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -19,7 +19,7 @@ images and tabular data. For examples, see:
 
 *   [DALL·E demo](google3/third_party/py/lit_nlp/examples/dalle/) - image
     generation, using DALL·E Mini model.
-*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) -
+*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the
     [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins)

From e0e35c3ffcfd9ad5331d4154e7d33d0b1d0daf89 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Fri, 21 Jun 2024 12:22:21 -0700
Subject: [PATCH 30/47] Fix a bug in the prompt debugging demo.

PiperOrigin-RevId: 645466395
---
 lit_nlp/examples/prompt_debugging/models.py | 2 +-
 lit_nlp/examples/prompt_debugging/server.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py
index 2c72c9b6..9518cf70 100644
--- a/lit_nlp/examples/prompt_debugging/models.py
+++ b/lit_nlp/examples/prompt_debugging/models.py
@@ -12,7 +12,7 @@
 DEFAULT_BATCH_SIZE = 1
 DEFAULT_DL_FRAMEWORK = "kerasnlp"
 DEFAULT_DL_RUNTIME = "tensorflow"
-DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"],
+DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"]
 DEFAULT_PRECISION = "bfloat16"
 
 
diff --git a/lit_nlp/examples/prompt_debugging/server.py b/lit_nlp/examples/prompt_debugging/server.py
index 55ef7420..f360a0b4 100644
--- a/lit_nlp/examples/prompt_debugging/server.py
+++ b/lit_nlp/examples/prompt_debugging/server.py
@@ -8,7 +8,7 @@
 To run with the default configuration (Gemma on TensorFlow via Keras):
 
   python3 -m lit_nlp.examples.prompt_debugging.server -- \
-    --models=gemma:gemma_1.1_instruct_2b_en \
+    --models=gemma:gemma_1.1_instruct_2b_en, \
     --alsologtostderr
 
 MODELS:

From 71cbdbaee0fee8e96f52cd4df7a269a0873b9259 Mon Sep 17 00:00:00 2001
From: Googler <no-reply@google.com>
Date: Mon, 24 Jun 2024 07:28:43 -0700
Subject: [PATCH 31/47] Fix for Remove attention module from LIT. Removed
 AttentionModule from default LIT notebook layouts.

PiperOrigin-RevId: 646087107
---
 lit_nlp/notebook.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/lit_nlp/notebook.py b/lit_nlp/notebook.py
index aae2eb9a..02ad0e4e 100644
--- a/lit_nlp/notebook.py
+++ b/lit_nlp/notebook.py
@@ -51,7 +51,6 @@
             *layout.MODEL_PREDS_MODULES,
             modules.SalienceMapModule,
             modules.LegacySequenceSalienceModule,
-            modules.AttentionModule,
         ],
         'Analysis': [
             modules.MetricsModule,

From 416d573d79f84b9a6964d36e498b850a249ef452 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Mon, 24 Jun 2024 10:37:00 -0700
Subject: [PATCH 32/47] Remove the DALL-E demo from LIT.  The package
 dependency could not be resolved.

(The newest version of dalle-mini is 0.1.5, which depends on jax==0.3.25 strictly. For jaxlib, the oldest available version is 0.4.6. However, jax (0.3.25) is not compatible with any jaxlib verion >= 0.4.6.)

PiperOrigin-RevId: 646145542
---
 .gitignore                              |   1 -
 lit_nlp/examples/dalle/README.md        |  35 ----
 lit_nlp/examples/dalle/dataset.py       |  23 ---
 lit_nlp/examples/dalle/demo.py          | 102 ----------
 lit_nlp/examples/dalle/model.py         | 257 ------------------------
 lit_nlp/examples/dalle/requirements.txt |  24 ---
 website/sphinx_src/components.md        |   3 -
 website/sphinx_src/faq.md               |   2 -
 8 files changed, 447 deletions(-)
 delete mode 100644 lit_nlp/examples/dalle/README.md
 delete mode 100644 lit_nlp/examples/dalle/dataset.py
 delete mode 100644 lit_nlp/examples/dalle/demo.py
 delete mode 100644 lit_nlp/examples/dalle/model.py
 delete mode 100644 lit_nlp/examples/dalle/requirements.txt

diff --git a/.gitignore b/.gitignore
index 1388772e..e52eb35b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,7 +14,6 @@ docs/documentation/.buildinfo
 docs/documentation/.doctrees/**
 
 **/.DS_Store
-.dalle-venv/
 .tydi-venv/
 .venv/
 .vscode/
diff --git a/lit_nlp/examples/dalle/README.md b/lit_nlp/examples/dalle/README.md
deleted file mode 100644
index 52a53c54..00000000
--- a/lit_nlp/examples/dalle/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
-DALL·E Mini Demo for the Learning Interpretability Tool
-=======================================================
-
-This demo showcases how LIT can be used to visualize generative image models,
-using [DALL·E Mini](https://github.com/borisdayma/dalle-mini) as a case study.
-
-Due to the specific requirements of the `dalle-mini` library, this demo
-necessitates some specific setup.
-
-First, you will need to [create an account](https://wandb.ai/login) on Weights &
-Biases in order to generate an API key to use some of the models included in
-this demo.
-
-Second, you will need a stand-alone virtual environment for the Python
-libraries, which you can setup using the following commands from the root of the
-LIT repo.
-
-```sh
-# Create the virtual environment. You may want to use python3 or python3.10
-# depends on how many Python versions you have installed and their aliases.
-python -m venv .dalle-venv
-source .dalle-venv/bin/activate
-# This requirements.txt file will also install the core LIT library deps.
-pip install -r ./lit_nlp/examples/dalle/requirements.txt
-# The LIT web app still needs to be built in the usual way.
-(cd ./lit_nlp && yarn && yarn build)
-```
-
-Once your virtual environment is setup, you can launch the demo with the
-following command. Note that you will be prompted to log into you Weights &
-Biases account and provide an API key as the demo starts up.
-
-```sh
-python -m lit_nlp.examples.dalle.demo
-```
diff --git a/lit_nlp/examples/dalle/dataset.py b/lit_nlp/examples/dalle/dataset.py
deleted file mode 100644
index 75a0b97e..00000000
--- a/lit_nlp/examples/dalle/dataset.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Example prompts to use with Dall-E and other text-to-image GenAI models."""
-
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types as lit_types
-
-_CANNED_PROMPTS = (
-    "A pikachu that looks lika a pug",
-    "Trail cam footage of gollum eating watermelon",
-    "An astronaut riding a horse in a photorealistic style",
-    "Pixar coronavirus movie",
-    "Darth Vader in a Soviet space propaganda poster",
-)
-
-
-class DallePrompts(lit_dataset.Dataset):
-  """Example prompts to use with Dall-E and other text-to-image GenAI models."""
-
-  def __init__(self):
-    for phrase in _CANNED_PROMPTS:
-      self._examples.append({"prompt": phrase})
-
-  def spec(self) -> lit_types.Spec:
-    return {"prompt": lit_types.TextSegment()}
diff --git a/lit_nlp/examples/dalle/demo.py b/lit_nlp/examples/dalle/demo.py
deleted file mode 100644
index a96f4abd..00000000
--- a/lit_nlp/examples/dalle/demo.py
+++ /dev/null
@@ -1,102 +0,0 @@
-r"""Example for dalle demo model.
-
-To run locally with a small number of examples:
-  python -m lit_nlp.examples.dalle.demo
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-from collections.abc import Sequence
-import os
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.examples.dalle import dataset
-from lit_nlp.examples.dalle import model
-
-# NOTE: additional flags defined in server_flags.py
-_FLAGS = flags.FLAGS
-_FLAGS.set_default("development_demo", True)
-_FLAGS.set_default("default_layout", "DALLE_LAYOUT")
-
-_MODELS = flags.DEFINE_list(
-    "models",
-    [
-        "dalle-mini/dalle-mini/mega-1-fp16:latest",
-        "dalle-mini/dalle-mini/mini-1:v0",
-    ],
-    "Models to load",
-)
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    "max_examples",
-    5,
-    "Maximum number of examples to load from each evaluation set. Set to None "
-    "to load the full set.",
-)
-
-# Custom frontend layout; see api/layout.py
-_modules = layout.LitModuleName
-_DALLE_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        "Main": [
-            _modules.DataTableModule,
-            _modules.DatapointEditorModule,
-        ]
-    },
-    lower={
-        "Predictions": [
-            _modules.GeneratedImageModule,
-            _modules.GeneratedTextModule,
-        ],
-    },
-    description="Custom layout for Text to Image models.",
-)
-
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {"DALLE_LAYOUT": _DALLE_LAYOUT}
-
-
-def get_wsgi_app() -> Optional[dev_server.LitServerType]:
-  _FLAGS.set_default("server_type", "external")
-  _FLAGS.set_default("demo_mode", True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = _FLAGS(sys.argv, known_only=True)
-  return main(unused)
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  # Load models, according to the --models flag.
-  models = {}
-  for model_name_or_path in _MODELS.value:
-    model_name = os.path.basename(model_name_or_path)
-    # set number of images to generate default is 6
-    models[model_name] = model.DalleModel(
-        model_name=model_name_or_path, predictions=6
-    )
-
-  datasets = {"Dalle_prompt": dataset.DallePrompts()}
-
-  for name in datasets:
-    datasets[name] = datasets[name].slice[:_MAX_EXAMPLES.value]
-    logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      layouts=CUSTOM_LAYOUTS,
-      **server_flags.get_flags(),
-  )
-  return lit_demo.serve()
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/lit_nlp/examples/dalle/model.py b/lit_nlp/examples/dalle/model.py
deleted file mode 100644
index 30f49cb1..00000000
--- a/lit_nlp/examples/dalle/model.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""Dalle model based on https://github.com/borisdayma/dalle-mini."""
-
-from collections.abc import Iterable
-import functools
-import random
-from typing import Optional
-
-import dalle_mini
-import flax
-from flax.training import common_utils as flax_common_utils
-import jax
-from jax import numpy as jnp
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import image_utils
-import numpy as np
-from PIL import Image
-import tqdm.notebook
-import transformers
-from vqgan_jax import modeling_flax_vqgan as vqgan_flax
-
-# DalleBart, VQModel & CLIP to generate Score
-_JsonDict = lit_types.JsonDict
-_CLIPProcessor = transformers.CLIPProcessor
-_DalleBart = dalle_mini.DalleBart
-_DalleBartProcessor = dalle_mini.DalleBartProcessor
-_FlaxCLIPModel = transformers.FlaxCLIPModel
-_VQModel = vqgan_flax.VQModel
-
-# Some other functions
-_flax_replicate = flax.jax_utils.replicate
-_flax_shard = flax_common_utils.shard
-_flax_shard_prng_key = flax_common_utils.shard_prng_key
-_trange = tqdm.notebook.trange
-
-
-class DalleModel(lit_model.Model):
-  """LIT model wrapper for the Dalle-Mini Text-to-Image model.
-
-  The model wrapper consists of a few already known works connected in an
-  interesting way to generate images from the text:
-
-  * VQGAN: The generative image model.
-  * BART: A sequence-to-sequence autoencoder used to reconstruct input text.
-  * CLIP: A scoring model that assesses the alingment of an image and a prompt.
-
-  The basic flow within this model wrapper's predict() function is:
-
-  1.  The BART encoder is fed the prompt text.
-  2.  The BART decoder is sampled multiple times to generate candidates.
-  3.  Each candidate is passed to VQGAN, which generates images.
-  4.  CLIP then scores each generated image against the prompt.
-  """
-
-  def __init__(self,
-               model_name: str,
-               predictions: int = 1,
-               mode_revision: Optional[str] = None,
-               vqgan_repo: str = "dalle-mini/vqgan_imagenet_f16_16384",
-               vqgan_revision: str = "e93a26e7707683d349bf5d5c41c5b0ef69b677a9",
-               clip_repo: str = "openai/clip-vit-base-patch32",
-               clip_revision: Optional[str] = None):
-    super().__init__()
-
-    self.model = model_name
-    self.n_predictions = predictions
-
-    # Load Dalle model
-    self.dalle_bart_model, params = _DalleBart.from_pretrained(
-        self.model, revision=mode_revision, dtype=jnp.float16, _do_init=False
-    )
-    self.processor = _DalleBartProcessor.from_pretrained(
-        self.model, revision=mode_revision
-    )
-
-    # Load the VQGan model
-    self.vqgan, vqgan_params = _VQModel.from_pretrained(
-        vqgan_repo, revision=vqgan_revision, _do_init=False
-    )
-    self.params = _flax_replicate(params)
-    self.vqgan_params = _flax_replicate(vqgan_params)
-
-    # Load CLIP model to generate CLIP score
-    # Scores how accurate generated image is
-    self.clip, clip_params = _FlaxCLIPModel.from_pretrained(
-        clip_repo, revision=clip_revision, dtype=jnp.float16, _do_init=False
-    )
-    self.clip_processor = _CLIPProcessor.from_pretrained(
-        clip_repo, revision=clip_revision
-    )
-    self.clip_params = _flax_replicate(clip_params)
-
-  # LIT API implementation
-  def max_minibatch_size(self) -> int:
-    return 8
-
-  def predict(
-      self, inputs: Iterable[_JsonDict], **unused_kw
-  ) -> Iterable[_JsonDict]:
-    # Model prediction based on code pipeline in doc
-    # https://github.com/borisdayma/dalle-mini
-    #
-    # Three models are required for prediction and it's in the following stages:
-    #
-    # 1.  First the prompt(text from which image is generated) is fed into
-    #     BART encoder model (dalle_bart_model) then the BART decoder is sampled
-    #     multiple times to generate candidates.
-    #
-    # 2.  Each candidate is passed to VQGAN which generates images. The
-    #     generated images are stored in two arrays. One images[], which
-    #     contains images in ImageBytes format second pil_images[] which
-    #     contains the same images in PIL format which is used to generate CLIP
-    #     score using the CLIP model.
-    #
-    # 3.  CLIP is another model by openai, which takes in an image and a prompt
-    #     and tells how well they match by generating a score. At the last, the
-    #     final output is formated such that it includes the CLIP score along
-    #     with the generated images.
-
-    # BART model inference function
-    @functools.partial(jax.pmap, axis_name="batch")
-    def p_generate(
-        tokenized_prompt,
-        key,
-        params,
-        top_k: Optional[int] = None,
-        top_p: Optional[float] = None,
-        temperature: Optional[float] = None,
-        condition_scale: float = 10.0
-    ):
-      return self.dalle_bart_model.generate(
-          **tokenized_prompt,
-          prng_key=key,
-          params=params,
-          top_k=top_k,
-          top_p=top_p,
-          temperature=temperature,
-          condition_scale=condition_scale,
-      )
-
-    # VQGAN image decoder function
-    @functools.partial(jax.pmap, axis_name="batch")
-    def p_decode(indices, params):
-      return self.vqgan.decode_code(indices, params=params)
-
-    # CLIP <image, prompt> scoring function
-    @functools.partial(jax.pmap, axis_name="batch")
-    def p_clip(inputs, params):
-      logits = self.clip(params=params, **inputs).logits_per_image
-      return logits
-
-    # create a random key
-    seed = random.randint(0, 2**32 - 1)
-    key = jax.random.PRNGKey(seed)
-    prompts = [ex["prompt"] for ex in inputs]
-    tokenized_prompts = self.processor(prompts)
-    tokenized_prompt = _flax_replicate(tokenized_prompts)
-
-    # images has all generated ImageByste
-    # pil_images has images in pil format for clip score
-    images = []
-    pil_images = []
-    for _ in _trange(max(self.n_predictions // jax.device_count(), 1)):
-      # Get a new key; passed to the model on each device to generate unique
-      # inference.
-      key, subkey = jax.random.split(key)
-      # generate images
-      encoded_images = p_generate(
-          tokenized_prompt, _flax_shard_prng_key(subkey), self.params
-      )
-
-      # remove BOS
-      encoded_images = encoded_images.sequences[..., 1:]
-      # decode images
-      decoded_images = p_decode(encoded_images, self.vqgan_params)
-      decoded_images = decoded_images.clip(0.0, 1.0).reshape((-1, 256, 256, 3))
-      for decoded_img in decoded_images:
-        img = Image.fromarray(np.asarray(decoded_img * 255, dtype=np.uint8))
-        # need pil format images too to generate CLIP score
-        pil_images.append(img)
-        # convert to ImageBytes
-        image_str = image_utils.convert_pil_to_image_str(img)
-        images.append(image_str)
-
-    # get CLIP scores
-    clip_inputs = self.clip_processor(
-        text=prompts * jax.device_count(),
-        images=pil_images,
-        return_tensors="np",
-        padding="max_length",
-        max_length=77,
-        truncation=True,
-    ).data
-
-    # array containing clip score for all images
-    p = len(prompts)
-    logits = p_clip(_flax_shard(clip_inputs), self.clip_params)
-    logits = np.asarray([logits[:, i::p, i] for i in range(p)]).squeeze()
-
-    # Organize VQGAN images and CLIP scores per prompt.
-    #
-    # Until now, images contains data structured as:
-    # [
-    #     prompt1_prediction_1,
-    #     prompt2_prediction_1,
-    #     prompt1_prediction_2,
-    #     prompt2_prediction_2,
-    # ]
-    #
-    # Below, the structure changes to...
-    # [
-    #     {'image': [prompt1_prediction_1, prompt1_prediction_2]},
-    #     {'image': [prompt2_prediction_1, prompt2_prediction_2]},
-    # ]
-    #
-    # ...and also adds clip score to check image accuracy
-    final_images = []
-    clip_score = []
-    images_per_prompt = []
-
-    # Add images as per prompt [prompt1_prediction_1,prompt1_prediction_2]
-    # and also add clip_score
-    for i in range(p):
-      # logits shape is different if n_predictions is only 1, vs more than one
-      clip_prompts = logits if logits.ndim == 0 else logits[i].argsort()[::-1]
-      if logits.ndim == 0 or len(clip_prompts) == 1:
-        my_loop = [0]*self.n_predictions
-      else:
-        my_loop = clip_prompts
-      for idx in range(len(my_loop)):
-        images_per_prompt.append(images[idx * p + i])
-        # Append CLIP score depending on size hence the condition
-        if logits.ndim == 0:
-          clip_score.append((str(logits), None))
-        elif len(np.shape(logits)) == 1:
-          clip_score.append((str(logits[idx]), None))
-        else:
-          clip_score.append((str(logits[i][idx]), None))
-      # Append to final list[JsonDict]
-      final_images.append({
-          "image": images_per_prompt,
-          "clip_score": clip_score,
-      })
-      # Reset images & clip score for new prompt
-      images_per_prompt = []
-      clip_score = []
-
-    return final_images
-
-  def input_spec(self):
-    return {"prompt": lit_types.TextSegment()}
-
-  def output_spec(self):
-    return {
-        "image": lit_types.ImageBytesList(),
-        "clip_score": lit_types.GeneratedTextCandidates(parent="prompt"),
-    }
diff --git a/lit_nlp/examples/dalle/requirements.txt b/lit_nlp/examples/dalle/requirements.txt
deleted file mode 100644
index d431c2dd..00000000
--- a/lit_nlp/examples/dalle/requirements.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
--r ../../../requirements_core.txt
-
-jax==0.4.13
-jaxlib==0.4.13
-flax==0.6.11
-dalle-mini==0.1.5
-ipywidgets==7.5.0
-orbax==0.0.23
-vqgan-jax @ git+https://github.com/patil-suraj/vqgan-jax@10ef240
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index e4ef2044..d8e88c4d 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -239,9 +239,6 @@ NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don't break existing checked-in code with
 such a change.
 
-*   See [examples/dalle/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/dalle/demo.py) for a demo on
-    image generation and how LIT handles image bytes.
-
 ## Token-based Salience
 
 LIT supports several methods for token-based input salience, including
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index 2d4a3242..aa8bd03d 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -17,8 +17,6 @@ works with any modern ML framework. For more information, see
 In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
 
-*   [DALL·E demo](google3/third_party/py/lit_nlp/examples/dalle/) - image
-    generation, using DALL·E Mini model.
 *   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the

From bcc481e44185d04268f5f8bb4ba762ec2cd35907 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Mon, 24 Jun 2024 10:40:00 -0700
Subject: [PATCH 33/47] Update requirements.txt files for penguin demo, glue
 demo, and tydi demo.

PiperOrigin-RevId: 646146700
---
 lit_nlp/examples/glue/requirements.txt        | 20 +++++++++++++++++
 lit_nlp/examples/penguin/requirements.txt     | 19 ++++++++++++++++
 .../prompt_debugging/requirements.txt         | 22 +++++++++++++++++++
 lit_nlp/examples/tydi/requirements.txt        |  6 ++---
 4 files changed, 64 insertions(+), 3 deletions(-)
 create mode 100644 lit_nlp/examples/glue/requirements.txt
 create mode 100644 lit_nlp/examples/penguin/requirements.txt
 create mode 100644 lit_nlp/examples/prompt_debugging/requirements.txt

diff --git a/lit_nlp/examples/glue/requirements.txt b/lit_nlp/examples/glue/requirements.txt
new file mode 100644
index 00000000..51f146be
--- /dev/null
+++ b/lit_nlp/examples/glue/requirements.txt
@@ -0,0 +1,20 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+-r ../../../requirements_core.txt
+
+tensorflow>=2.10.0,<2.16.0
+tensorflow-datasets>=4.9.0
+transformers>=4.27.1
\ No newline at end of file
diff --git a/lit_nlp/examples/penguin/requirements.txt b/lit_nlp/examples/penguin/requirements.txt
new file mode 100644
index 00000000..6f141cfd
--- /dev/null
+++ b/lit_nlp/examples/penguin/requirements.txt
@@ -0,0 +1,19 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+-r ../../../requirements_core.txt
+
+tensorflow>=2.10.0,<2.16.0
+tensorflow-datasets>=4.9.0
\ No newline at end of file
diff --git a/lit_nlp/examples/prompt_debugging/requirements.txt b/lit_nlp/examples/prompt_debugging/requirements.txt
new file mode 100644
index 00000000..5f76376e
--- /dev/null
+++ b/lit_nlp/examples/prompt_debugging/requirements.txt
@@ -0,0 +1,22 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+-r ../../../requirements_core.txt
+
+keras>=3.1.0
+keras-nlp>=0.9.0
+tensorflow-text>=2.10.0
+torch>=2.0.0
+transformers>=4.27.1
\ No newline at end of file
diff --git a/lit_nlp/examples/tydi/requirements.txt b/lit_nlp/examples/tydi/requirements.txt
index 184b9971..84f94ecb 100644
--- a/lit_nlp/examples/tydi/requirements.txt
+++ b/lit_nlp/examples/tydi/requirements.txt
@@ -15,6 +15,6 @@
 
 -r ../../../requirements_core.txt
 
-jax==0.3.16
-jaxlib==0.3.15
-flax==0.5.3
+jax==0.4.6
+jaxlib==0.4.6
+flax>=0.5.3

From bb29f430ff7be55d74a82aec5dee1e54fa27bed0 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Mon, 24 Jun 2024 17:52:14 -0700
Subject: [PATCH 34/47] Add Jinja2 to penguin requirements.txt.

PiperOrigin-RevId: 646279470
---
 lit_nlp/examples/penguin/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/lit_nlp/examples/penguin/requirements.txt b/lit_nlp/examples/penguin/requirements.txt
index 6f141cfd..7bef3d18 100644
--- a/lit_nlp/examples/penguin/requirements.txt
+++ b/lit_nlp/examples/penguin/requirements.txt
@@ -15,5 +15,6 @@
 
 -r ../../../requirements_core.txt
 
+Jinja2>=3.1.4
 tensorflow>=2.10.0,<2.16.0
 tensorflow-datasets>=4.9.0
\ No newline at end of file

From c7970fb8c51d2a8bd3647cc7eedd15cca285ac08 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 25 Jun 2024 08:47:44 -0700
Subject: [PATCH 35/47] Update prompt_examples.jsonl path in MANIFEST.in.

PiperOrigin-RevId: 646493892
---
 MANIFEST.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 107506d4..ca18dea6 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,2 +1,2 @@
 recursive-include lit_nlp/client/build/default *.js* *.gif *.html *.png *.svg
-include lit_nlp/examples/datasets/prompt_examples.jsonl
+include lit_nlp/examples/prompt_debugging/prompt_examples.jsonl

From 7dda659bec4e933d187b0d7afc04d954ae262cc2 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Tue, 25 Jun 2024 09:18:00 -0700
Subject: [PATCH 36/47] Internal change

PiperOrigin-RevId: 646502922
---
 docs/documentation/_sources/getting_started.md.txt | 2 +-
 lit_nlp/examples/glue/demo.py                      | 4 ++--
 lit_nlp/examples/penguin/demo.py                   | 2 +-
 website/sphinx_src/getting_started.md              | 5 ++---
 4 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt
index 569bb521..80e4eae9 100644
--- a/docs/documentation/_sources/getting_started.md.txt
+++ b/docs/documentation/_sources/getting_started.md.txt
@@ -76,7 +76,7 @@ Most demos can be run with a single blaze command. To run the default one, you
 can do:
 
 ```sh
-python -m lit_nlp.examples.glue_demo \
+python -m lit_nlp.examples.glue.demo \
   --quickstart --port=4321 --alsologtostderr
 ```
 
diff --git a/lit_nlp/examples/glue/demo.py b/lit_nlp/examples/glue/demo.py
index 8fe9d703..0d577032 100644
--- a/lit_nlp/examples/glue/demo.py
+++ b/lit_nlp/examples/glue/demo.py
@@ -1,11 +1,11 @@
 r"""Example demo loading a handful of GLUE models.
 
 For a quick-start set of models, run:
-  blaze run -c opt --config=cuda examples/glue:demo -- \
+  python -m lit_nlp.examples.glue.demo \
     --quickstart --port=5432
 
 To run with the 'normal' defaults, including full-size BERT models:
-  blaze run -c opt --config=cuda examples/glue:demo -- --port=5432
+  python -m lit_nlp.examples.glue.demo --port=5432
 
 Then navigate to localhost:5432 to access the demo UI.
 """
diff --git a/lit_nlp/examples/penguin/demo.py b/lit_nlp/examples/penguin/demo.py
index c48fd538..b6d1e855 100644
--- a/lit_nlp/examples/penguin/demo.py
+++ b/lit_nlp/examples/penguin/demo.py
@@ -1,7 +1,7 @@
 """🐧 LIT demo for tabular data using penguin classification.
 
 To run:
-  blaze run -c opt --config=cuda examples/penguin:demo -- --port=5432
+  python -m lit_nlp.examples.penguin.demo --port=5432
 
 Then navigate to localhost:5432 to access the demo UI.
 """
diff --git a/website/sphinx_src/getting_started.md b/website/sphinx_src/getting_started.md
index 569bb521..fed48807 100644
--- a/website/sphinx_src/getting_started.md
+++ b/website/sphinx_src/getting_started.md
@@ -72,11 +72,10 @@ variety of model types. The code for these is under [examples](https://github.co
 ;
 each is a small script that loads one or more models and starts a LIT server.
 
-Most demos can be run with a single blaze command. To run the default one, you
-can do:
+Most demos can be run with a single command. To run the default one, you can do:
 
 ```sh
-python -m lit_nlp.examples.glue_demo \
+python -m lit_nlp.examples.glue.demo \
   --quickstart --port=4321 --alsologtostderr
 ```
 

From 79ada6edf8b2e485ec6a6425d4c60720b4dab8d1 Mon Sep 17 00:00:00 2001
From: Bin Du <dubin@google.com>
Date: Tue, 25 Jun 2024 11:10:45 -0700
Subject: [PATCH 37/47] LIT documentation update post demo cleanup.

PiperOrigin-RevId: 646541561
---
 website/sphinx_src/api.md                     |  33 ++++---
 website/sphinx_src/components.md              |  64 ++++----------
 website/sphinx_src/demos.md                   |  13 +--
 website/sphinx_src/frontend_development.md    |  12 +--
 website/sphinx_src/getting_started.md         |   2 +-
 .../images/components/attention.png           | Bin 188417 -> 0 bytes
 .../images/components/pair-selection.png      | Bin 34353 -> 159071 bytes
 website/sphinx_src/images/lit-attention.png   | Bin 91620 -> 0 bytes
 .../images/lit-datapoint-compare.png          | Bin 459728 -> 368329 bytes
 .../images/lit-winogender-metrics.png         | Bin 160995 -> 0 bytes
 website/sphinx_src/images/lit-winogender.png  | Bin 254066 -> 0 bytes
 website/sphinx_src/ui_guide.md                |  82 ++++++------------
 12 files changed, 74 insertions(+), 132 deletions(-)
 delete mode 100644 website/sphinx_src/images/components/attention.png
 delete mode 100644 website/sphinx_src/images/lit-attention.png
 delete mode 100644 website/sphinx_src/images/lit-winogender-metrics.png
 delete mode 100644 website/sphinx_src/images/lit-winogender.png

diff --git a/website/sphinx_src/api.md b/website/sphinx_src/api.md
index 59c3be20..f89fed37 100644
--- a/website/sphinx_src/api.md
+++ b/website/sphinx_src/api.md
@@ -1,6 +1,6 @@
 # LIT Python API
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -349,7 +349,7 @@ list of scores for each token. The Integrated Gradients saliency method
 additionally requires a `TokenEmbeddings` input and corresponding output, as
 well as a label field `Target` to pin the gradient target to the same class as
 an input and corresponding output. See the
-[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
+[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
 for an example of these spec requirements.
 
 The core API involves implementing the `run()` method:
@@ -712,11 +712,9 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing,
 etc.) when switching between models or datasets with heterogeneous `Spec`s.
 
 When implementing your own LIT components and modules, you can use
-[`utils.find_spec_keys()`][utils-lib]
-(Python) and
-[`findSpecKeys()`][utils-lib]
-(TypeScript) to identify fields of interest in a `Spec`. These methods recognize
-and respect subclasses. For example,
+[`utils.find_spec_keys()`][utils-lib-py] (Python) and
+[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a
+`Spec`. These methods recognize and respect subclasses. For example,
 `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore`
 fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all
 `Scalar` fields in the `Spec`.
@@ -807,8 +805,12 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples)
 
 ### Available types
 
-The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized
-in the table below.
+The full set of `LitType`s is defined in
+[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types
+such as `Integer` and `Scalar` have predefined ranges that can be overridden
+using corresponding `min_val` and `max_val` attributes as seen
+[here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py;l=19-22;rcl=639554825).
+The different types available in LIT are summarized in the table below.
 
 Note: Bracket syntax, such as `<float>[num_tokens]`, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
@@ -981,15 +983,15 @@ needing to reload the server or click the UI.
 For example, to view examples in a dataset:
 
 ```python
-from lit_nlp.examples.datasets import glue
-dataset = glue.SST2Data('validation')
+from lit_nlp.examples.glue import data as glue_data
+dataset = glue_data.SST2Data('validation')
 print(dataset.examples)  # list of records {"sentence": ..., "label": ...}
 ```
 
 And to run inference on a few of them:
 
 ```python
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 
 model = glue_models.SST2Model("/path/to/model/files")
 preds = list(model.predict(dataset.examples[:5]))
@@ -1013,16 +1015,19 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https://
 <!-- Links -->
 
 [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py
-[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
+[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py
 [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py
 [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
 [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py
 [json]: https://www.json.org
 [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/
+
 [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html
-[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
+
+[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py
 [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts
 [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py
 [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts
 [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts
+[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py
 [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index d8e88c4d..9b9189d1 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -1,8 +1,6 @@
 # Components and Features
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
-
-TODO(b/343678324): Remove the files after cl/629748253 is submitted.
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -258,7 +256,7 @@ regression (`RegressionScore`) and generation (`GeneratedText` or
 ### Gradient Norm
 
 This is a simple method, in which salience scores are proportional to the L2
-norm of the gradient, i.e. the score for token $i$ is:
+norm of the gradient, i.e. the score for token $$i$$ is:
 
 $$S(i) \propto ||\nabla_{x_i} \hat{y}||_2$$
 
@@ -270,26 +268,26 @@ To enable this method, your model should, as part of the
 *   Return a `TokenGradients` field with the `align` attribute pointing to the
     name of the `Tokens` field (i.e. `align="tokens"`). Values should be arrays
     of shape `<float>[num_tokens, emb_dim]` representing the gradient
-    $\nabla_{x} \hat{y}$ of the embeddings with respect to the prediction
-    $\hat{y}$.
+    $$\nabla_{x} \hat{y}$$ of the embeddings with respect to the prediction
+    $$\hat{y}$$.
 
 Because LIT is framework-agnostic, the model code is responsible for performing
 the gradient computation and returning the result as a NumPy array. The choice
-of $\hat{y}$ is up to the developer; typically for regression/scoring this is
+of $$\hat{y}$$ is up to the developer; typically for regression/scoring this is
 the raw score and for classification this is the score of the predicted (argmax)
 class.
 
 ### Gradient-dot-Input
 
 In this method, salience scores are proportional to the dot product of the input
-embeddings and their gradients, i.e. for token $i$ we compute:
+embeddings and their gradients, i.e. for token $$i$$ we compute:
 
 $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$
 
 Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-$\hat{y}$, while a negative score suggests that the prediction would be stronger
-if that token was removed.
+$$\hat{y}$$, while a negative score suggests that the prediction would be
+stronger if that token was removed.
 
 To enable this method, your model should, as part of the
 [output spec and `predict()` implementation](./api.md#models):
@@ -297,13 +295,13 @@ To enable this method, your model should, as part of the
 *   Return a `Tokens` field with values (as `list[str]`) containing the
     tokenized input.
 *   Return a `TokenEmbeddings` field with values as arrays of shape
-    `<float>[num_tokens, emb_dim]` containing the input embeddings $x$.
+    `<float>[num_tokens, emb_dim]` containing the input embeddings $$x$$.
 *   Return a `TokenGradients` field with the `align` attribute pointing to the
     name of the `Tokens` field (i.e. `align="tokens"`), and the `grad_for`
     attribute pointing to the name of the `TokenEmbeddings` field. Values should
     be arrays of shape `<float>[num_tokens, emb_dim]` representing the gradient
-    $\nabla_{x} \hat{y}$ of the embeddings with respect to the prediction
-    $\hat{y}$.
+    $$\nabla_{x} \hat{y}$$ of the embeddings with respect to the prediction
+    $$\hat{y}$$.
 
 As with grad-norm, the model should return embeddings and gradients as NumPy
 arrays. The LIT `GradientDotInput` component will compute the dot products and
@@ -525,9 +523,7 @@ your model should, as part of the
     input image.
 
     The model should be able to accept input images as numpy arrays in addition
-    to accepting base64 URL encoded format. See
-    [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py)
-    for an example.
+    to accepting base64 URL encoded format.
 
 A variety of image saliency techniques are implemented for models that return
 image gradients, through use of the
@@ -538,36 +534,6 @@ and XRAI.
 Each of these techniques returns a saliency map image as a base64-encoded string
 through the `ImageSalience` type.
 
-## Attention
-
-LIT can display a visualization of attention heads from transformers and other
-models:
-
-![Attention Visualization](./images/components/attention.png){w=400px align=center}
-
-To enable this, your model should return one or more fields of the type
-`AttentionHeads`, with values as arrays of shape `<float>[num_heads, num_tokens,
-num_tokens]`. Each field represents a set of heads at a single layer of the
-model, so models will often have more than one:
-
-```python
-   def output_spec(self) -> types.Spec:
-     return {
-         # ...
-         "tokens": lit_types.Tokens(parent="input_text"),
-         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         # ...
-     }
-```
-
-The `align_in` and `align_out` attributes are the names of `Tokens` fields for
-the source and target tokens for that layer. For self-attention (such as in
-BERT) these would name the same field, but for encoder-decoder attention they
-could reference different token sets (such as "input_tokens" and
-"target_tokens").
-
 ## Embedding Projector
 
 LIT includes a version of the
@@ -599,7 +565,7 @@ example, we could facet by class label:
 
 ![Metrics Table](./images/components/metrics-table.png)
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
 
 To enable metrics, your model should set the `parent` attribute on one or more
 output fields, pointing to the name of the input field that it should be
@@ -638,7 +604,7 @@ analysis.
 
 ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center}
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
 
 The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (`CategoryLabel`).
@@ -724,6 +690,8 @@ datapoints, giving a global view of feature effects.
 
 ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center}
 
+To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab.
+
 ### Dive
 
 Dive is a visualization module, inspired by our prior work on
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index 31c057da..448c0d23 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -1,6 +1,6 @@
 # Demos
 
-<!-- freshness: { owner: 'lit-dev' reviewed: '2023-08-29' } -->
+<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -36,7 +36,6 @@ https://pair-code.github.io/lit/demos/.
         the margin.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
-    *   Attention visualization.
     *   Counterfactual generators, including HotFlip for targeted adversarial
         perturbations.
 
@@ -61,7 +60,6 @@ https://pair-code.github.io/lit/tutorials/sentiment
         quick filtering.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
-    *   Attention visualization.
 
 --------------------------------------------------------------------------------
 
@@ -72,11 +70,12 @@ https://pair-code.github.io/lit/tutorials/sentiment
 **Code:**
 [examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)
 
-*   Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
+*   Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
+    and Transformers (with PyTorch).
 *   Interactively debug LLM prompts using
     [sequence salience](./components.md#sequence-salience).
 *   Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-    granularities: token-, word-, sentence-, and paragraph-level.
+    granularities: token-, word-, line-, sentence-, and paragraph-level.
 
 Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
@@ -84,10 +83,6 @@ Generative AI Toolkit.
 
 --------------------------------------------------------------------------------
 
-## Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} -->
-
---------------------------------------------------------------------------------
-
 ## Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} -->
 
 ### Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} -->
diff --git a/website/sphinx_src/frontend_development.md b/website/sphinx_src/frontend_development.md
index 6f399a43..fe8a82bb 100644
--- a/website/sphinx_src/frontend_development.md
+++ b/website/sphinx_src/frontend_development.md
@@ -1,6 +1,6 @@
 # Frontend Developer Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go
 into greater detail later.
 
 The JS bundle entry point is
-[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which
-first imports the loaded, the `<lit-app>` web component is declared, and
-attaches itself to the DOM, waiting for the app to be initialized.
+[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first
+imports the loaded, the `<lit-app>` web component is declared, and attaches
+itself to the DOM, waiting for the app to be initialized.
 
 The second step is kicking off app initialization. The
 [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton
@@ -67,7 +67,7 @@ pre-configured layouts in
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `experimental`: A three-panel layout that puts exploratory data
+*   `three_panel`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -84,7 +84,7 @@ Note: The pre-configured layouts are added to every `LitApp` instance using
 the Mapping passed to the `LitApp` constructor overrides the pre-configured
 layouts `Mapping`. Thus, you can remove or change these pre-configured layouts
 as you like by passing a `Mapping` where the values of `simple`, `default`,
-and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance
+and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance
 (to override) as you desire.
 
 The actual layout of components in the LIT UI, see
diff --git a/website/sphinx_src/getting_started.md b/website/sphinx_src/getting_started.md
index fed48807..0ca605bf 100644
--- a/website/sphinx_src/getting_started.md
+++ b/website/sphinx_src/getting_started.md
@@ -1,6 +1,6 @@
 # Getting Started with LIT
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2022-08-17' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
diff --git a/website/sphinx_src/images/components/attention.png b/website/sphinx_src/images/components/attention.png
deleted file mode 100644
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diff --git a/website/sphinx_src/ui_guide.md b/website/sphinx_src/ui_guide.md
index 4a38a88c..9014ce02 100644
--- a/website/sphinx_src/ui_guide.md
+++ b/website/sphinx_src/ui_guide.md
@@ -1,6 +1,6 @@
 # UI Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 This is a user guide for the Learning Interpretability Tool (LIT) UI.
 
@@ -42,7 +42,7 @@ LIT provides three pre-configured layouts:
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `experimental`: A three-panel layout that puts exploratory data
+*   `three_panel`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -130,19 +130,26 @@ controls and information. The left side of the toolbar contains a set of menus
 for quickly controlling datapoint selection and coloring. This includes the
 following controls:
 
-*   The **"Select related"** option adds any datapoints "related" to the current
-    selection. In LIT, "related" is defined as datapoints created from some
-    source datapoint (through manual editing or a datapoint generator), or a
-    source datapoint that a selected datapoint was created from.
-*   The **"Clear selection"** button deselects all selected datapoints. The
-    dropdown also contains a color legend for the current color setting.
-*   The **Slices** option allows quick selection of an already-created slice of
-    datapoints.
-*   The **"Datapoint color"** menu enables setting of the color of each
-    datapoint in the modules that visualize all datapoints (such as the
-    *Embeddings* and *Scalars* modules) by any number of datapoint features or
-    model outputs on those datapoints (such as coloring by some categorical
-    input feature, or by prediction error for a regression task).
+*   The **"Select datapoint"** menu provides a drop-down of several options:
+    *   the **"Random"** option selects a random datapoint,
+    *   the **"All related"** option adds any datapoints "related" to the
+        current selection. In LIT, "related" is defined as datapoints created
+        from some source datapoint (through manual editing or a datapoint
+        generator), or a source datapoint that a selected datapoint was created
+        from,
+    *   the **"Parents"** option adds the source datapoints that the selected
+        datapoints were created from,
+    *   the **"Children"** option adds the datapoints created from the selected
+        datapoints (through manual editing or a datapoint generator),
+    *   the **Slices** option allows quick selection of an already-created slice
+        of datapoints,
+    *   the **"Clear selection"** button deselects all selected datapoints.
+*   The **"Color by"** menu enables setting of the color of each datapoint in
+    the modules that visualize all datapoints (such as the *Embeddings* and
+    *Scalars* modules) by any number of datapoint features or model outputs on
+    those datapoints (such as coloring by some categorical input feature, or by
+    prediction error for a regression task).
+*   The **Slices** menu allows adding/selecting/removing slices of datapoints.
 
 Next to the menus is a button for pinning/unpinning a datapoint. Pinning a
 datapoint puts LIT into datapoint comparison mode, where two datapoints can be
@@ -322,9 +329,9 @@ such as model predictions, enable them from the "Columns" dropdown.
 
 The datapoint editor shows the details of the primary selected datapoint, if one
 is selected. Any field can be edited, and a new datapoint created with those
-edits through the **"Make new datapoint"** button. Any edit to an existing
-datapoint must be saved as a new datapoint to be explored, to keep datapoints
-immutable for simplicity of use.
+edits through the **"Add"** button. Any edit to an existing datapoint must be
+saved as a new datapoint to be explored, to keep datapoints immutable for
+simplicity of use.
 
 When no datapoint is selected, the editor shows a blank datapoint that can be
 filled out by hand to create a completely new datapoint.
@@ -473,24 +480,15 @@ background of each text piece is colored by the salience of that piece on the
 prediction, and hovering on any piece will display the exact value calculated
 for that piece.
 
-There is an **"autorun"** button by each methodology. If it is checked, then
-that calculation is made when a new primary datapoint is selected. If it is
+There is an **"autorun"** button by each methodology on the right side of the
+bar (the methodoloy name is on the left side). If it is checked, then that
+calculation is made when a new primary datapoint is selected. If it is
 unchecked, the calculation isn't made until it is checked. This can be valuable
 so that expensive, long-running saliency calculations (such as LIME) aren't
 performed on every datapoint selection, but only when explicitly asked for.
 
 ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps")
 
-### Attention
-
-For models that return attention head information along with predictions, the
-attention module shows which tokens are attended to between layers of a model.
-Dropdowns allow you to select which layer and attention head is being
-visualized. Line opacity is controlled by the magnitude of the attention between
-those tokens.
-
-![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center}
-
 ## User Journeys
 
 In this section, we explore some example user journeys and how LIT enables them.
@@ -515,30 +513,6 @@ model.
 
 ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis")
 
-### Gender Bias in Coreference
-
-Does a system encode gendered associations, which might lead to incorrect
-predictions? We load a coreference model trained on OntoNotes, and load the
-Winogender dataset into LIT for evaluation. Each Winogender example has a
-pronoun and two candidate referents, one a occupation term like (“technician”)
-and one is an “other participant” (like “customer”). Our model predicts
-coreference probabilities for each candidate. We can explore the model’s
-sensitivity to pronouns by comparing two examples side-by-side (see structured
-predictions in screenshot). We can see how commonly the model makes similar
-errors by paging through the dataset, or by selecting specific slices of
-interest.
-
-![Winogender analysis](./images/lit-winogender.png "Winogender analysis")
-
-In the *Metrics* table, we can slice this selection by pronoun type and by the
-true referent. On the set of male-dominated occupations (< 25% female by BLS),
-we see the model performs well when the ground-truth agrees with the
-stereotype - e.g. when the answer is the occupation term, male pronouns are
-correctly resolved 83% of the time, compared to female pronouns only 37.5% of
-the time (screenshot section (c)).
-
-![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis")
-
 ### Debugging Text Generation
 
 <!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a

From b14e3b1a81d7b6305063f778f46666a4d1326045 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Tue, 25 Jun 2024 11:13:48 -0700
Subject: [PATCH 38/47] Updated gunicorn config for demos running in Docker.

PiperOrigin-RevId: 646542614
---
 README.md                           | 35 +++++++----------------------
 lit_nlp/examples/gunicorn_config.py |  6 ++---
 lit_nlp/examples/tydi/demo.py       |  5 ++++-
 website/sphinx_src/docker.md        | 12 +++++-----
 4 files changed, 20 insertions(+), 38 deletions(-)

diff --git a/README.md b/README.md
index 44b71343..a28e0aa4 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # 🔥 Learning Interpretability Tool (LIT)
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-22' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
 
 The Learning Interpretability Tool (🔥LIT, formerly known as the Language
 Interpretability Tool) is a visual, interactive ML model-understanding tool that
@@ -21,8 +21,8 @@ LIT is built to answer questions such as:
 LIT supports a variety of debugging workflows through a browser-based UI.
 Features include:
 
-*   **Local explanations** via salience maps, attention, and rich visualization
-    of model predictions.
+*   **Local explanations** via salience maps and rich visualization of model
+    predictions.
 *   **Aggregate analysis** including custom metrics, slicing and binning, and
     visualization of embedding spaces.
 *   **Counterfactual generation** via manual edits or generator plug-ins to
@@ -90,8 +90,8 @@ git clone https://github.com/PAIR-code/lit.git && cd lit
 ```
 
 
-Note: be sure you are running Python 3.10. If you have a different version on  
-your system, use the `conda` instructions below to set up a Python 3.10 
+Note: be sure you are running Python 3.9+. If you have a different version on
+your system, use the `conda` instructions below to set up a Python 3.9
 environment.
 
 Set up a Python environment with `venv`:
@@ -106,7 +106,7 @@ Or set up a Python environment using `conda`:
 ```sh
 conda create --name lit-nlp
 conda activate lit-nlp
-conda install python=3.10
+conda install python=3.9
 conda install pip
 ```
 
@@ -142,13 +142,7 @@ To explore classification and regression models tasks from the popular
 [GLUE benchmark](https://gluebenchmark.com/):
 
 ```sh
-python -m lit_nlp.examples.glue_demo --port=5432 --quickstart
-```
-
-Or, using `docker`:
-
-```sh
-docker run --rm -e DEMO_NAME=glue_demo -p 5432:5432 -t lit-nlp --quickstart
+python -m lit_nlp.examples.glue.demo --port=5432 --quickstart
 ```
 
 Navigate to http://localhost:5432 to access the LIT UI.
@@ -160,19 +154,6 @@ but you can switch to
 [STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or
 [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the
 gear icon in the upper right.
-
-### Quick-start: language modeling
-
-To explore predictions from a pre-trained language model (BERT or GPT-2), run:
-
-```sh
-python -m lit_nlp.examples.lm_demo --models=bert-base-uncased --port=5432
-```
-
-Or, using `docker`:
-
-```sh
-docker run --rm -e DEMO_NAME=lm_demo -p 5432:5432 -t lit-nlp --models=bert-base-uncased
 ```
 
 And navigate to http://localhost:5432 for the UI.
@@ -192,7 +173,7 @@ See [lit_nlp/examples](./lit_nlp/examples). Most are run similarly to the
 quickstart example above:
 
 ```sh
-python -m lit_nlp.examples.<example_name> --port=5432 [optional --args]
+python -m lit_nlp.examples.<example_name>.demo --port=5432 [optional --args]
 ```
 
 ## User Guide
diff --git a/lit_nlp/examples/gunicorn_config.py b/lit_nlp/examples/gunicorn_config.py
index 7817674a..e4e635ea 100644
--- a/lit_nlp/examples/gunicorn_config.py
+++ b/lit_nlp/examples/gunicorn_config.py
@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Config for gunicorn for cloud-hosted demos."""
+"""gunicorn configuration for cloud-hosted demos."""
 
 import os
 
-_DEMO_NAME = os.getenv('DEMO_NAME', 'glue_demo')
+_DEMO_NAME = os.getenv('DEMO_NAME', 'glue')
 _DEMO_PORT = os.getenv('DEMO_PORT', '5432')
 
 bind = f'0.0.0.0:{_DEMO_PORT}'
 timeout = 3600
 threads = 8
 worker_class = 'gthread'
-wsgi_app = f'lit_nlp.examples.{_DEMO_NAME}:get_wsgi_app()'
+wsgi_app = f'lit_nlp.examples.{_DEMO_NAME}.demo:get_wsgi_app()'
diff --git a/lit_nlp/examples/tydi/demo.py b/lit_nlp/examples/tydi/demo.py
index 307ad8a3..5619e3d0 100644
--- a/lit_nlp/examples/tydi/demo.py
+++ b/lit_nlp/examples/tydi/demo.py
@@ -12,6 +12,7 @@
 
 from absl import app
 from absl import flags
+from absl import logging
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.components import word_replacer
@@ -40,7 +41,9 @@ def get_wsgi_app() -> Optional[dev_server.LitServerType]:
   # Parse flags without calling app.run(main), to avoid conflict with
   # gunicorn command line flags.
   unused = flags.FLAGS(sys.argv, known_only=True)
-  return main(unused)
+  if unused:
+    logging.info("tydi_demo:get_wsgi_app() called with unused args: %s", unused)
+  return main([])
 
 
 def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
diff --git a/website/sphinx_src/docker.md b/website/sphinx_src/docker.md
index c6b501d4..b58279c6 100644
--- a/website/sphinx_src/docker.md
+++ b/website/sphinx_src/docker.md
@@ -49,13 +49,11 @@ below.
 ```shell
 # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME"
+#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as
-#
-#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
+# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -66,8 +64,8 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp
 # containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
 ```
 
 ## Integrating Custom LIT Instances with the Default Docker Image

From 7ff377f92820748476e796994fd207e1b5dba1d9 Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Tue, 25 Jun 2024 11:19:56 -0700
Subject: [PATCH 39/47] LIT: Disable embeddings for TyDi.

PiperOrigin-RevId: 646544804
---
 lit_nlp/examples/tydi/model.py | 56 +++++++++++++++++++---------------
 1 file changed, 31 insertions(+), 25 deletions(-)

diff --git a/lit_nlp/examples/tydi/model.py b/lit_nlp/examples/tydi/model.py
index 7f35f760..55001377 100644
--- a/lit_nlp/examples/tydi/model.py
+++ b/lit_nlp/examples/tydi/model.py
@@ -76,23 +76,26 @@ def predict(self, inputs: Iterable[_JsonDict], **kw) -> Iterable[_JsonDict]:
       total_tokens = self.tokenizer.convert_ids_to_tokens(tokens[0])
       # split by question & context
       slicer_question, slicer_context = self._segment_slicers(total_tokens)
-      # get embeddings
-      embeddings = results.hidden_states[0][0]
-      # gradient
-      gradient = results.hidden_states[-1][0]
+
+      # TODO(b/349177755): Gradients and embeddings are not implemented
+      # correctly. Use lit_nlp/examples/prompt_debugging/transformers_lms.py
+      # code as a reference for how to implement these correctly.
+      # embeddings = results.hidden_states[0][0]
+      # gradient = results.hidden_states[-1][0]
 
       prediction_output.append({
           "generated_text": self.tokenizer.decode(predict_answer_tokens),
           "answers_text": inp["answers_text"],
-          # Embeddings come from the first token of the last layer.
-          "cls_emb": results.hidden_states[-1][:, 0][0],
           "tokens_question": total_tokens[slicer_question],
           "tokens_context": total_tokens[slicer_context],
-          "grad_class": None,
-          "tokens_embs_question": np.asarray(embeddings[slicer_question]),
-          "token_grad_context": np.asarray(embeddings[slicer_context]),
-          "tokens_grad_question": np.asarray(gradient[slicer_question]),
-          "tokens_embs_context": np.asarray(gradient[slicer_context])
+          # TODO(b/349177755): Re-enable these once the embeddings and gradients
+          # are implemented correctly.
+          # Embeddings come from the first token of the last layer.
+          # "cls_emb": results.hidden_states[-1][:, 0][0],
+          # "tokens_embs_question": np.asarray(embeddings[slicer_question]),
+          # "token_grad_context": np.asarray(embeddings[slicer_context]),
+          # "tokens_grad_question": np.asarray(gradient[slicer_question]),
+          # "tokens_embs_context": np.asarray(gradient[slicer_context]),
       })
 
     return prediction_output
@@ -110,20 +113,23 @@ def input_spec(self):
 
   def output_spec(self):
     return {
+        "answers_text": lit_types.MultiSegmentAnnotations(),
         "generated_text": lit_types.GeneratedText(parent="answers_text"),
-        "cls_emb": lit_types.Embeddings(),
-        "tokens_question": lit_types.Tokens(parent="question"),
-        "tokens_embs_question": lit_types.TokenEmbeddings(
-            align="tokens_question"
-        ),
-        "tokens_grad_question": lit_types.TokenGradients(
-            align="tokens_question", grad_for="tokens_embs_question"
-        ),
         "tokens_context": lit_types.Tokens(parent="question"),
-        "tokens_embs_context": lit_types.TokenEmbeddings(
-            align="tokens_context"
-        ),
-        "token_grad_context": lit_types.TokenGradients(
-            align="tokens_context", grad_for="tokens_embs_context"
-        ),
+        "tokens_question": lit_types.Tokens(parent="question"),
+        # TODO(b/349177755): Re-enable these once the embeddings and gradients
+        # are implemented correctly.
+        # "cls_emb": lit_types.Embeddings(),
+        # "tokens_embs_question": lit_types.TokenEmbeddings(
+        #     align="tokens_question"
+        # ),
+        # "tokens_grad_question": lit_types.TokenGradients(
+        #     align="tokens_question", grad_for="tokens_embs_question"
+        # ),
+        # "tokens_embs_context": lit_types.TokenEmbeddings(
+        #     align="tokens_context"
+        # ),
+        # "token_grad_context": lit_types.TokenGradients(
+        #     align="tokens_context", grad_for="tokens_embs_context"
+        # ),
     }

From 5639e3b1b71b1c0ddf4a3c9e1bd25517fba18375 Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 25 Jun 2024 11:27:04 -0700
Subject: [PATCH 40/47] Update requirements.txt for TyDi example.

PiperOrigin-RevId: 646547287
---
 lit_nlp/examples/tydi/requirements.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/lit_nlp/examples/tydi/requirements.txt b/lit_nlp/examples/tydi/requirements.txt
index 84f94ecb..f04e568d 100644
--- a/lit_nlp/examples/tydi/requirements.txt
+++ b/lit_nlp/examples/tydi/requirements.txt
@@ -15,6 +15,9 @@
 
 -r ../../../requirements_core.txt
 
+flax>=0.5.3
 jax==0.4.6
 jaxlib==0.4.6
-flax>=0.5.3
+tensorflow>=2.10.0,<2.16.0
+tensorflow-datasets>=4.9.0
+transformers>=4.27.1

From 1c8d6a0269ce5637e05e79ae435f770e2a0da147 Mon Sep 17 00:00:00 2001
From: Cibi Arjun <arjuncibi@google.com>
Date: Tue, 25 Jun 2024 14:44:11 -0700
Subject: [PATCH 41/47] Update LIT documentation to reflect path changes in the
 codebase and remove dead links.

PiperOrigin-RevId: 646613181
---
 lit_nlp/examples/custom_module/potato_demo.py | 2 +-
 website/src/tutorials.md                      | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/lit_nlp/examples/custom_module/potato_demo.py b/lit_nlp/examples/custom_module/potato_demo.py
index b9a50594..6a3416ba 100644
--- a/lit_nlp/examples/custom_module/potato_demo.py
+++ b/lit_nlp/examples/custom_module/potato_demo.py
@@ -36,7 +36,7 @@
 _MODEL = flags.DEFINE_string(
     "model",
     "https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz",
-    "Path to model, as in glue_demo.py")
+    "Path to model, as in examples/glue/demo.py")
 
 # Use our custom frontend build from this directory.
 FLAGS.set_default(
diff --git a/website/src/tutorials.md b/website/src/tutorials.md
index a2e95743..6e3a4c0e 100644
--- a/website/src/tutorials.md
+++ b/website/src/tutorials.md
@@ -40,9 +40,6 @@ c-copy: "Learn about examining model behavior through user-curated concepts." %}
 {% include partials/tutorial-link-element c-title: "Exploring a Sentiment Classifier", link: "/tutorials/sentiment",
 c-copy: "Learn about how we used LIT to analyze a sentiment classifier." %}
 
-{% include partials/tutorial-link-element c-title: "Debugging a Text Generator", link: "/tutorials/generation",
-c-copy: "Learn about how we used LIT to debug summarization by a text generation model." %}
-
 {% include partials/spacer height:50 %}
 
 </div>

From cee3b58baea2de27633109e6dd5b3e4211fa46ea Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 25 Jun 2024 15:16:58 -0700
Subject: [PATCH 42/47] Fix typo in example command.

PiperOrigin-RevId: 646624034
---
 lit_nlp/examples/prompt_debugging/server.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lit_nlp/examples/prompt_debugging/server.py b/lit_nlp/examples/prompt_debugging/server.py
index f360a0b4..55ef7420 100644
--- a/lit_nlp/examples/prompt_debugging/server.py
+++ b/lit_nlp/examples/prompt_debugging/server.py
@@ -8,7 +8,7 @@
 To run with the default configuration (Gemma on TensorFlow via Keras):
 
   python3 -m lit_nlp.examples.prompt_debugging.server -- \
-    --models=gemma:gemma_1.1_instruct_2b_en, \
+    --models=gemma:gemma_1.1_instruct_2b_en \
     --alsologtostderr
 
 MODELS:

From 48b029c3a1a3f25d4d2611a9b0e94355d41078ef Mon Sep 17 00:00:00 2001
From: Cibi Arjun <arjuncibi@google.com>
Date: Tue, 25 Jun 2024 16:35:04 -0700
Subject: [PATCH 43/47] Update colab examples to include installation of the
 lit-nlp package.

PiperOrigin-RevId: 646648117
---
 .../notebooks/LIT_components_example.ipynb    | 988 +++++++++---------
 .../notebooks/LIT_sentiment_classifier.ipynb  | 362 ++++---
 2 files changed, 705 insertions(+), 645 deletions(-)

diff --git a/lit_nlp/examples/notebooks/LIT_components_example.ipynb b/lit_nlp/examples/notebooks/LIT_components_example.ipynb
index 3a73924f..6817ac54 100644
--- a/lit_nlp/examples/notebooks/LIT_components_example.ipynb
+++ b/lit_nlp/examples/notebooks/LIT_components_example.ipynb
@@ -1,482 +1,512 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "h2c6PyqQaNiA"
-   },
-   "source": [
-    "# LIT Standalone Components\n",
-    "\n",
-    "This notebook shows use of the [Learning Interpretability Tool](https://pair-code.github.io/lit) components on a binary classifier for labelling statement sentiment (0 for negative, 1 for positive).\n",
-    "\n",
-    "All LIT backend components (models, datasets, metrics, generators, etc.) are standalone Python classes, and can easily be used from Colab or another Python context without starting a server. This can be handy for development, of if you want to re-use components in an offline workflow.\n",
-    "\n",
-    "Copyright 2021 Google LLC.\n",
-    "SPDX-License-Identifier: Apache-2.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import attr\n",
-    "import pandas as pd\n",
-    "\n",
-    "from lit_nlp import notebook\n",
-    "from lit_nlp.examples.glue import data\n",
-    "from lit_nlp.examples.glue import models\n",
-    "\n",
-    "# Hide INFO and lower logs. Comment this out for debugging.\n",
-    "from absl import logging\n",
-    "logging.set_verbosity(logging.WARNING)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "x38BqRdJFlyW"
-   },
-   "source": [
-    "## Load data\n",
-    "\n",
-    "LIT's `Dataset` classes are just lists of records, plus spec information to describe each field."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "AWhbAZg57RpB",
-    "outputId": "f34dc6b4-9fe4-4d00-f987-a7660b96cd15"
-   },
-   "outputs": [],
-   "source": [
-    "sst_data = data.SST2Data('validation')\n",
-    "sst_data.spec()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "9GSfs1waBdLd",
-    "outputId": "fcbb376d-dab5-4bae-b4f0-191643683c4a",
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "sst_data.examples[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "7_hlS2eAFtxu"
-   },
-   "source": [
-    "You can easily convert this to tabular form, too:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 414
-    },
-    "id": "fW6GyeJ8FrkB",
-    "outputId": "2f397e2b-9eb6-417d-9c90-738c3bfb7c05"
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(sst_data.examples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YZeDs3bOFygS"
-   },
-   "source": [
-    "## Load a model and run inference\n",
-    "\n",
-    "LIT's `Model` class defines a `predict()` function to perform inference. The `input_spec()` describes the expected inputs (it should be a subset of the dataset fields), and `output_spec()` describes the output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "30l9ZyTjxJjf",
-    "outputId": "cda76ba1-a403-4f5a-e753-18dddef50107"
-   },
-   "outputs": [],
-   "source": [
-    "# Fetch the trained model weights and load the model to analyze\n",
-    "!wget https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz\n",
-    "!mkdir sst2_tiny\n",
-    "!tar -xvf sst2_tiny.tar.gz -C sst2_tiny\n",
-    "\n",
-    "sentiment_model = models.SST2Model('./sst2_tiny')\n",
-    "sentiment_model.input_spec(), sentiment_model.output_spec()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KKcqVijmGPLH"
-   },
-   "source": [
-    "There's a lot of fields in the output spec, since this model returns embeddings, gradients, attention, and more. We can view it using Pandas to avoid too much clutter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 872
-    },
-    "id": "-KdWHCUHGP4o",
-    "outputId": "2a16ae42-51a1-4285-ddae-21bb2b704fa9"
-   },
-   "outputs": [],
-   "source": [
-    "preds = list(sentiment_model.predict(sst_data.examples[:10]))\n",
-    "pd.DataFrame(preds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kf_cufk9GXRp"
-   },
-   "source": [
-    "If we just want the predicted probabilites for each class, we can look at the `probas` field:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 355
-    },
-    "id": "ZppG2HSrGXyc",
-    "outputId": "560714fb-f43c-434e-b226-792e9d04b2de"
-   },
-   "outputs": [],
-   "source": [
-    "labels = sentiment_model.output_spec()['probas'].vocab\n",
-    "pd.DataFrame([p['probas'] for p in preds], columns=pd.Index(labels, name='label'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "555KTfI8GcHN"
-   },
-   "source": [
-    "## Salience methods\n",
-    "\n",
-    "We can use different interpretability components as well. Here's an example running LIME to get a salience map. The output has entries for each input field, though here that's just one field named \"sentence\":"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "3iW42OVpGcme",
-    "outputId": "4dd41374-18d0-4f0e-c813-d91a1667aad2"
-   },
-   "outputs": [],
-   "source": [
-    "from lit_nlp.components import lime_explainer\n",
-    "lime = lime_explainer.LIME()\n",
-    "\n",
-    "lime_results = lime.run(sst_data.examples[:1], sentiment_model, sst_data)[0]\n",
-    "lime_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 324
-    },
-    "id": "ludO8l76Geyv",
-    "outputId": "900408e3-c6c9-4f3b-d49a-4107b38ea827"
-   },
-   "outputs": [],
-   "source": [
-    "# Again, pretty-print output with Pandas. The SalienceMap object is just a dataclass defined using attr.s.\n",
-    "pd.DataFrame(attr.asdict(lime_results['sentence']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "RosugrhIGifH",
-    "outputId": "562df56b-5b07-407e-a314-5e0b23c1e006"
-   },
-   "outputs": [],
-   "source": [
-    "from lit_nlp.components import gradient_maps\n",
-    "ig = gradient_maps.IntegratedGradients()\n",
-    "\n",
-    "ig_results = ig.run(sst_data.examples[:1], sentiment_model, sst_data)[0]\n",
-    "ig_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 355
-    },
-    "id": "v1ekBujJGk_I",
-    "outputId": "20fb48a1-8251-4086-922a-c9a4a587515f"
-   },
-   "outputs": [],
-   "source": [
-    "# Again, pretty-print output with Pandas. The SalienceMap object is just a dataclass defined using attr.s.\n",
-    "pd.DataFrame(attr.asdict(ig_results['token_grad_sentence']))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kRe6I5y1GlQv"
-   },
-   "source": [
-    "## Metrics\n",
-    "\n",
-    "We can also compute metrics. The metrics components (via the `SimpleMetrics` API) will automatically detect compatible fields marked by the `parent` attribute - in this case, our model's `probas` field that should be scored against `label` in the input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Av16xG_8Gn4y",
-    "outputId": "37fa6860-e8a9-424b-dfb1-6920dee92de5",
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "from lit_nlp.components import metrics\n",
-    "classification_metrics = metrics.MulticlassMetrics()\n",
-    "classification_metrics.run(sst_data.examples[:100], sentiment_model, sst_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8dKD5QKRGuOG"
-   },
-   "source": [
-    "## Generators\n",
-    "\n",
-    "We can use counterfactual generators as well. Here's an example with a generator that simply scrambles words in a text segment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dHAv4C03GuvU",
-    "outputId": "58f05e89-9c1b-4982-8535-989eae92e58e"
-   },
-   "outputs": [],
-   "source": [
-    "from lit_nlp.components import scrambler\n",
-    "sc = scrambler.Scrambler()\n",
-    "\n",
-    "sc_in = sst_data.examples[:5]\n",
-    "sc_out = sc.generate_all(sc_in, model=None, dataset=sst_data,\n",
-    "                         config={'Fields to scramble': ['sentence']})\n",
-    "# The output is a list-of-lists, generated from each original example.\n",
-    "sc_out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h2c6PyqQaNiA"
+      },
+      "source": [
+        "# LIT Standalone Components\n",
+        "\n",
+        "This notebook shows use of the [Learning Interpretability Tool](https://pair-code.github.io/lit) components on a binary classifier for labelling statement sentiment (0 for negative, 1 for positive).\n",
+        "\n",
+        "All LIT backend components (models, datasets, metrics, generators, etc.) are standalone Python classes, and can easily be used from Colab or another Python context without starting a server. This can be handy for development, of if you want to re-use components in an offline workflow.\n",
+        "\n",
+        "Copyright 2021 Google LLC.\n",
+        "SPDX-License-Identifier: Apache-2.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mxeB9Hmtq8QP"
+      },
+      "cell_type": "code",
+      "source": [
+        "# The pip installation will install all necessary prerequisite packages for use of the core LIT package.\n",
+        "!pip install lit-nlp"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wADoVZ1Nq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "import attr\n",
+        "import pandas as pd\n",
+        "\n",
+        "from lit_nlp import notebook\n",
+        "from lit_nlp.examples.glue import data\n",
+        "from lit_nlp.examples.glue import models\n",
+        "\n",
+        "# Hide INFO and lower logs. Comment this out for debugging.\n",
+        "from absl import logging\n",
+        "logging.set_verbosity(logging.WARNING)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x38BqRdJFlyW"
+      },
+      "source": [
+        "## Load data\n",
+        "\n",
+        "LIT's `Dataset` classes are just lists of records, plus spec information to describe each field."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AWhbAZg57RpB",
+        "outputId": "f34dc6b4-9fe4-4d00-f987-a7660b96cd15"
+      },
+      "outputs": [],
+      "source": [
+        "sst_data = data.SST2Data('validation')\n",
+        "sst_data.spec()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9GSfs1waBdLd",
+        "outputId": "fcbb376d-dab5-4bae-b4f0-191643683c4a",
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "sst_data.examples[:10]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7_hlS2eAFtxu"
+      },
+      "source": [
+        "You can easily convert this to tabular form, too:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 414
+        },
+        "id": "fW6GyeJ8FrkB",
+        "outputId": "2f397e2b-9eb6-417d-9c90-738c3bfb7c05"
+      },
+      "outputs": [],
+      "source": [
+        "pd.DataFrame(sst_data.examples)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YZeDs3bOFygS"
+      },
+      "source": [
+        "## Load a model and run inference\n",
+        "\n",
+        "LIT's `Model` class defines a `predict()` function to perform inference. The `input_spec()` describes the expected inputs (it should be a subset of the dataset fields), and `output_spec()` describes the output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "30l9ZyTjxJjf",
+        "outputId": "cda76ba1-a403-4f5a-e753-18dddef50107"
+      },
+      "outputs": [],
+      "source": [
+        "# Fetch the trained model weights and load the model to analyze\n",
+        "!wget https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz\n",
+        "!mkdir sst2_tiny\n",
+        "!tar -xvf sst2_tiny.tar.gz -C sst2_tiny\n",
+        "\n",
+        "sentiment_model = models.SST2Model('./sst2_tiny')\n",
+        "sentiment_model.input_spec(), sentiment_model.output_spec()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KKcqVijmGPLH"
+      },
+      "source": [
+        "There's a lot of fields in the output spec, since this model returns embeddings, gradients, attention, and more. We can view it using Pandas to avoid too much clutter:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "-KdWHCUHGP4o",
+        "outputId": "2a16ae42-51a1-4285-ddae-21bb2b704fa9"
+      },
+      "outputs": [],
+      "source": [
+        "preds = list(sentiment_model.predict(sst_data.examples[:10]))\n",
+        "pd.DataFrame(preds)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kf_cufk9GXRp"
+      },
+      "source": [
+        "If we just want the predicted probabilites for each class, we can look at the `probas` field:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "id": "ZppG2HSrGXyc",
+        "outputId": "560714fb-f43c-434e-b226-792e9d04b2de"
+      },
+      "outputs": [],
+      "source": [
+        "labels = sentiment_model.output_spec()['probas'].vocab\n",
+        "pd.DataFrame([p['probas'] for p in preds], columns=pd.Index(labels, name='label'))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "555KTfI8GcHN"
+      },
+      "source": [
+        "## Salience methods\n",
+        "\n",
+        "We can use different interpretability components as well. Here's an example running LIME to get a salience map. The output has entries for each input field, though here that's just one field named \"sentence\":"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3iW42OVpGcme",
+        "outputId": "4dd41374-18d0-4f0e-c813-d91a1667aad2"
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp.components import lime_explainer\n",
+        "lime = lime_explainer.LIME()\n",
+        "\n",
+        "lime_results = lime.run(sst_data.examples[:1], sentiment_model, sst_data)[0]\n",
+        "lime_results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 324
+        },
+        "id": "ludO8l76Geyv",
+        "outputId": "900408e3-c6c9-4f3b-d49a-4107b38ea827"
+      },
+      "outputs": [],
+      "source": [
+        "# Again, pretty-print output with Pandas. The SalienceMap object is just a dataclass defined using attr.s.\n",
+        "pd.DataFrame(attr.asdict(lime_results['sentence']))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RosugrhIGifH",
+        "outputId": "562df56b-5b07-407e-a314-5e0b23c1e006"
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp.components import gradient_maps\n",
+        "ig = gradient_maps.IntegratedGradients()\n",
+        "\n",
+        "ig_results = ig.run(sst_data.examples[:1], sentiment_model, sst_data)[0]\n",
+        "ig_results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "id": "v1ekBujJGk_I",
+        "outputId": "20fb48a1-8251-4086-922a-c9a4a587515f"
+      },
+      "outputs": [],
+      "source": [
+        "# Again, pretty-print output with Pandas. The SalienceMap object is just a dataclass defined using attr.s.\n",
+        "pd.DataFrame(attr.asdict(ig_results['token_grad_sentence']))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kRe6I5y1GlQv"
+      },
+      "source": [
+        "## Metrics\n",
+        "\n",
+        "We can also compute metrics. The metrics components (via the `SimpleMetrics` API) will automatically detect compatible fields marked by the `parent` attribute - in this case, our model's `probas` field that should be scored against `label` in the input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Av16xG_8Gn4y",
+        "outputId": "37fa6860-e8a9-424b-dfb1-6920dee92de5",
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp.components import metrics\n",
+        "classification_metrics = metrics.MulticlassMetrics()\n",
+        "classification_metrics.run(sst_data.examples[:100], sentiment_model, sst_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8dKD5QKRGuOG"
+      },
+      "source": [
+        "## Generators\n",
+        "\n",
+        "We can use counterfactual generators as well. Here's an example with a generator that simply scrambles words in a text segment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dHAv4C03GuvU",
+        "outputId": "58f05e89-9c1b-4982-8535-989eae92e58e"
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp.components import scrambler\n",
+        "sc = scrambler.Scrambler()\n",
+        "\n",
+        "sc_in = sst_data.examples[:5]\n",
+        "sc_out = sc.generate_all(sc_in, model=None, dataset=sst_data,\n",
+        "                         config={'Fields to scramble': ['sentence']})\n",
+        "# The output is a list-of-lists, generated from each original example.\n",
+        "sc_out"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "id": "5jCaQkpTGuzd",
+        "outputId": "b89c7d7e-2379-443c-c36c-f910444aa1fc"
+      },
+      "outputs": [],
+      "source": [
+        "# Format as a flat table for display, including original sentences\n",
+        "import itertools\n",
+        "for ex_in, exs_out in zip(sc_in, sc_out):\n",
+        "  for ex_out in exs_out:\n",
+        "    ex_out['original_sentence'] = ex_in['sentence']\n",
+        "pd.DataFrame(itertools.chain.from_iterable(sc_out), columns=['original_sentence', 'sentence', 'label'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XGZIbOy9IuBT"
+      },
+      "source": [
+        "# Running the LIT UI\n",
+        "\n",
+        "Of course, you can always still use these components in the LIT UI, without leaving Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hLCOC1hmGu4x"
+      },
+      "outputs": [],
+      "source": [
+        "widget = notebook.LitWidget(models={'sentiment': sentiment_model},\n",
+        "                            datasets={'sst2': sst_data}, port=8890)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "z0DKzSjtIzWd"
+      },
+      "outputs": [],
+      "source": [
+        "widget.render(height=600)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wNxmBPr2q8QP"
+      },
+      "source": [
+        "If you've found interesting examples using the LIT UI, you can access these in Python using `widget.ui_state`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kSGtVXRtq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.primary  # the main selected datapoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FmyzL26Vq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.selection  # the full selected set, if you have multiple points selected"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ktP0CWHgq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.pinned  # the pinned datapoint, if you use the 📌 icon or comparison mode"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ntxyncu5q8QP"
+      },
+      "source": [
+        "Note that these include some metadata; the bare example is in the `['data']` field for each record:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AwGRITwGq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.primary['data']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5eUVaK2Eq8QP"
+      },
+      "outputs": [],
+      "source": [
+        "[ex['data'] for ex in widget.ui_state.selection]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7bF6lmLfq8QP"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 202
-    },
-    "id": "5jCaQkpTGuzd",
-    "outputId": "b89c7d7e-2379-443c-c36c-f910444aa1fc"
-   },
-   "outputs": [],
-   "source": [
-    "# Format as a flat table for display, including original sentences\n",
-    "import itertools\n",
-    "for ex_in, exs_out in zip(sc_in, sc_out):\n",
-    "  for ex_out in exs_out:\n",
-    "    ex_out['original_sentence'] = ex_in['sentence']\n",
-    "pd.DataFrame(itertools.chain.from_iterable(sc_out), columns=['original_sentence', 'sentence', 'label'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XGZIbOy9IuBT"
-   },
-   "source": [
-    "# Running the LIT UI\n",
-    "\n",
-    "Of course, you can always still use these components in the LIT UI, without leaving Colab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "hLCOC1hmGu4x"
-   },
-   "outputs": [],
-   "source": [
-    "widget = notebook.LitWidget(models={'sentiment': sentiment_model}, \n",
-    "                            datasets={'sst2': sst_data}, port=8890)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "z0DKzSjtIzWd"
-   },
-   "outputs": [],
-   "source": [
-    "widget.render(height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you've found interesting examples using the LIT UI, you can access these in Python using `widget.ui_state`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.primary  # the main selected datapoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.selection  # the full selected set, if you have multiple points selected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.pinned  # the pinned datapoint, if you use the 📌 icon or comparison mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that these include some metadata; the bare example is in the `['data']` field for each record:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.primary['data']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[ex['data'] for ex in widget.ui_state.selection]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "LIT Components Example",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+      "collapsed_sections": [],
+      "name": "LIT Components Example",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
 }
diff --git a/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb b/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
index cab395fe..a3d9b811 100644
--- a/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
+++ b/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb
@@ -1,169 +1,199 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "h2c6PyqQaNiA"
-   },
-   "source": [
-    "# Using the Learning Interpretability Tool in Notebooks\n",
-    "\n",
-    "This notebook shows use of the [Learning Interpretability Tool](https://pair-code.github.io/lit) on a binary classifier for labelling statement sentiment (0 for negative, 1 for positive).\n",
-    "\n",
-    "The LitWidget object constructor takes a dict mapping model names to model objects, and a dict mapping dataset names to dataset objects. Those will be the datasets and models displayed in LIT. Running the constructor will cause the LIT server to be started in the background, loading the models and datasets and enabling the UI to be served.\n",
-    "\n",
-    "Render the LIT UI in an output cell by calling the `render` method on the LitWidget object. The LIT UI can be rendered multiple times in separate cells if desired. The widget also contains a `stop` method to shut down the LIT server.\n",
-    "\n",
-    "Copyright 2020 Google LLC.\n",
-    "SPDX-License-Identifier: Apache-2.0"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h2c6PyqQaNiA"
+      },
+      "source": [
+        "# Using the Learning Interpretability Tool in Notebooks\n",
+        "\n",
+        "This notebook shows use of the [Learning Interpretability Tool](https://pair-code.github.io/lit) on a binary classifier for labelling statement sentiment (0 for negative, 1 for positive).\n",
+        "\n",
+        "The LitWidget object constructor takes a dict mapping model names to model objects, and a dict mapping dataset names to dataset objects. Those will be the datasets and models displayed in LIT. Running the constructor will cause the LIT server to be started in the background, loading the models and datasets and enabling the UI to be served.\n",
+        "\n",
+        "Render the LIT UI in an output cell by calling the `render` method on the LitWidget object. The LIT UI can be rendered multiple times in separate cells if desired. The widget also contains a `stop` method to shut down the LIT server.\n",
+        "\n",
+        "Copyright 2020 Google LLC.\n",
+        "SPDX-License-Identifier: Apache-2.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "k5QzhJlsq9_U"
+      },
+      "cell_type": "code",
+      "source": [
+        "# The pip installation will install all necessary prerequisite packages for use of the core LIT package.\n",
+        "!pip install lit-nlp"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tthVhLoIq9_U"
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp import notebook\n",
+        "from lit_nlp.examples.glue import data\n",
+        "from lit_nlp.examples.glue import models\n",
+        "\n",
+        "# Hide INFO and lower logs. Comment this out for debugging.\n",
+        "from absl import logging\n",
+        "logging.set_verbosity(logging.WARNING)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "30l9ZyTjxJjf"
+      },
+      "outputs": [],
+      "source": [
+        "# Fetch the trained model weights\n",
+        "!wget https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz\n",
+        "!tar -xvf sst2_tiny.tar.gz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AWhbAZg57RpB"
+      },
+      "outputs": [],
+      "source": [
+        "# Create the LIT widget with the model and dataset to analyze.\n",
+        "datasets = {'sst_dev': data.SST2Data('validation')}\n",
+        "models = {'sst_tiny': models.SST2Model('./')}\n",
+        "\n",
+        "widget = notebook.LitWidget(models, datasets, port=8890)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9GSfs1waBdLd"
+      },
+      "outputs": [],
+      "source": [
+        "# Render the widget\n",
+        "widget.render(height=600)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dwYz8o1dq9_U"
+      },
+      "source": [
+        "If you've found interesting examples using the LIT UI, you can access these in Python using `widget.ui_state`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HRtO27puq9_U"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.primary  # the main selected datapoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J-XT048tq9_U"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.selection  # the full selected set, if you have multiple points selected"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6gxrwk-Bq9_U"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.pinned  # the pinned datapoint, if you use the 📌 icon or comparison mode"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RndbhKC9q9_U"
+      },
+      "source": [
+        "Note that these include some metadata; the bare example is in the `['data']` field for each record:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jbEVvUK6q9_U"
+      },
+      "outputs": [],
+      "source": [
+        "widget.ui_state.primary['data']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Pt3djyXPq9_U"
+      },
+      "outputs": [],
+      "source": [
+        "[ex['data'] for ex in widget.ui_state.selection]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "u03ZgGlAq9_U"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "LIT in Notebooks",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.15"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from lit_nlp import notebook\n",
-    "from lit_nlp.examples.glue import data\n",
-    "from lit_nlp.examples.glue import models\n",
-    "\n",
-    "# Hide INFO and lower logs. Comment this out for debugging.\n",
-    "from absl import logging\n",
-    "logging.set_verbosity(logging.WARNING)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "30l9ZyTjxJjf"
-   },
-   "outputs": [],
-   "source": [
-    "# Fetch the trained model weights\n",
-    "!wget https://storage.googleapis.com/what-if-tool-resources/lit-models/sst2_tiny.tar.gz\n",
-    "!tar -xvf sst2_tiny.tar.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "AWhbAZg57RpB"
-   },
-   "outputs": [],
-   "source": [
-    "# Create the LIT widget with the model and dataset to analyze.\n",
-    "datasets = {'sst_dev': data.SST2Data('validation')}\n",
-    "models = {'sst_tiny': models.SST2Model('./')}\n",
-    "\n",
-    "widget = notebook.LitWidget(models, datasets, port=8890)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "9GSfs1waBdLd"
-   },
-   "outputs": [],
-   "source": [
-    "# Render the widget\n",
-    "widget.render(height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you've found interesting examples using the LIT UI, you can access these in Python using `widget.ui_state`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.primary  # the main selected datapoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.selection  # the full selected set, if you have multiple points selected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.pinned  # the pinned datapoint, if you use the 📌 icon or comparison mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that these include some metadata; the bare example is in the `['data']` field for each record:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widget.ui_state.primary['data']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[ex['data'] for ex in widget.ui_state.selection]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "LIT in Notebooks",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat": 4,
+  "nbformat_minor": 1
 }

From 2e9d26738d9344cde0eebd66d49dfc14cd800e74 Mon Sep 17 00:00:00 2001
From: Bin Du <dubin@google.com>
Date: Tue, 25 Jun 2024 16:41:03 -0700
Subject: [PATCH 44/47] More LIT documentation updates.

PiperOrigin-RevId: 646649859
---
 website/sphinx_src/api.md                     |   7 +--
 website/sphinx_src/components.md              |  18 ++++----
 website/sphinx_src/docker.md                  |  10 ++---
 website/sphinx_src/images/lit-s2s-journey.png | Bin 147695 -> 0 bytes
 website/sphinx_src/ui_guide.md                |  32 ++++----------
 website/src/demos.md                          |  40 ------------------
 website/src/index.md                          |   4 +-
 website/src/tutorials/sentiment.md            |   2 +-
 website/src/tutorials/sequence-salience.md    |   6 +--
 website/src/tutorials/tab-feat-attr.md        |   2 +-
 website/src/tutorials/text-salience.md        |   2 +-
 11 files changed, 32 insertions(+), 91 deletions(-)
 delete mode 100644 website/sphinx_src/images/lit-s2s-journey.png

diff --git a/website/sphinx_src/api.md b/website/sphinx_src/api.md
index f89fed37..97ea5b64 100644
--- a/website/sphinx_src/api.md
+++ b/website/sphinx_src/api.md
@@ -808,9 +808,10 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples)
 The full set of `LitType`s is defined in
 [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types
 such as `Integer` and `Scalar` have predefined ranges that can be overridden
-using corresponding `min_val` and `max_val` attributes as seen
-[here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py;l=19-22;rcl=639554825).
-The different types available in LIT are summarized in the table below.
+using corresponding `min_val` and `max_val` attributes as seen in
+[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py)
+`INPUT_SPEC`. The different types available in LIT are summarized in the table
+below.
 
 Note: Bracket syntax, such as `<float>[num_tokens]`, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 9b9189d1..90b99dbd 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -256,7 +256,7 @@ regression (`RegressionScore`) and generation (`GeneratedText` or
 ### Gradient Norm
 
 This is a simple method, in which salience scores are proportional to the L2
-norm of the gradient, i.e. the score for token $$i$$ is:
+norm of the gradient, i.e. the score for token $i$ is:
 
 $$S(i) \propto ||\nabla_{x_i} \hat{y}||_2$$
 
@@ -268,25 +268,25 @@ To enable this method, your model should, as part of the
 *   Return a `TokenGradients` field with the `align` attribute pointing to the
     name of the `Tokens` field (i.e. `align="tokens"`). Values should be arrays
     of shape `<float>[num_tokens, emb_dim]` representing the gradient
-    $$\nabla_{x} \hat{y}$$ of the embeddings with respect to the prediction
-    $$\hat{y}$$.
+    $\nabla_{x} \hat{y}$ of the embeddings with respect to the prediction
+    $\hat{y}$.
 
 Because LIT is framework-agnostic, the model code is responsible for performing
 the gradient computation and returning the result as a NumPy array. The choice
-of $$\hat{y}$$ is up to the developer; typically for regression/scoring this is
+of $\hat{y}$ is up to the developer; typically for regression/scoring this is
 the raw score and for classification this is the score of the predicted (argmax)
 class.
 
 ### Gradient-dot-Input
 
 In this method, salience scores are proportional to the dot product of the input
-embeddings and their gradients, i.e. for token $$i$$ we compute:
+embeddings and their gradients, i.e. for token $i$ we compute:
 
 $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$
 
 Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-$$\hat{y}$$, while a negative score suggests that the prediction would be
+$\hat{y}$, while a negative score suggests that the prediction would be
 stronger if that token was removed.
 
 To enable this method, your model should, as part of the
@@ -295,13 +295,13 @@ To enable this method, your model should, as part of the
 *   Return a `Tokens` field with values (as `list[str]`) containing the
     tokenized input.
 *   Return a `TokenEmbeddings` field with values as arrays of shape
-    `<float>[num_tokens, emb_dim]` containing the input embeddings $$x$$.
+    `<float>[num_tokens, emb_dim]` containing the input embeddings $x$.
 *   Return a `TokenGradients` field with the `align` attribute pointing to the
     name of the `Tokens` field (i.e. `align="tokens"`), and the `grad_for`
     attribute pointing to the name of the `TokenEmbeddings` field. Values should
     be arrays of shape `<float>[num_tokens, emb_dim]` representing the gradient
-    $$\nabla_{x} \hat{y}$$ of the embeddings with respect to the prediction
-    $$\hat{y}$$.
+    $\nabla_{x} \hat{y}$ of the embeddings with respect to the prediction
+    $\hat{y}$.
 
 As with grad-norm, the model should return embeddings and gradients as NumPy
 arrays. The LIT `GradientDotInput` component will compute the dot products and
diff --git a/website/sphinx_src/docker.md b/website/sphinx_src/docker.md
index b58279c6..ebf7eb7c 100644
--- a/website/sphinx_src/docker.md
+++ b/website/sphinx_src/docker.md
@@ -13,10 +13,8 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or
 your preferred engine. This is how we run our
 [hosted demos](https://pair-code.github.io/lit/demos/).
 
-We provide a basic
-[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can
-use to build and run any of the demos in the `lit_nlp/examples` directory. The
-`Dockerfile` installs all necessary dependencies for LIT and builds the
+We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory.
+The `Dockerfile` installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as
 the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
@@ -26,7 +24,7 @@ You can find a reference implementation in
 [`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 Use the following shell
-[.github/workflows/ci.ymlcommands](https://github.com/PAIR-code/lit/blob/main/lit_nlp/.github/workflows/ci.ymlcommands) to build the
+https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the
 default Docker image for LIT from the provided `Dockerfile`, and then run a
 container from that image. Comments are provided in-line to help explain what
 each step does.
@@ -72,7 +70,7 @@ docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
 
 Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a `main()` method, as
-described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data).
+described in the [Python API docs](api.md#adding-models-and-data).
 
 These custom server scripts can be easily integrated with LIT's default image as
 long as your server meets two requirements:
diff --git a/website/sphinx_src/images/lit-s2s-journey.png b/website/sphinx_src/images/lit-s2s-journey.png
deleted file mode 100644
index 7e0728c6f61ba1f22cb426494b56d41a7d03bb22..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/website/sphinx_src/ui_guide.md b/website/sphinx_src/ui_guide.md
index 9014ce02..bb5028ae 100644
--- a/website/sphinx_src/ui_guide.md
+++ b/website/sphinx_src/ui_guide.md
@@ -513,27 +513,11 @@ model.
 
 ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis")
 
-### Debugging Text Generation
-
-<!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a
-     example that fits better with the text generation debugging story -->
-
-Does the training data explain a particular error in text generation? We analyze
-an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module
-allows us to look at per-example ROUGE scores, and quickly select an example
-with middling performance (screenshot section (a)). We find the generated text
-(screenshot section (b)) contains an erroneous constituent: “alastair cook was
-replaced as captain by former captain ...”. We can dig deeper, using LIT’s
-language modeling module (screenshot section (c)) to see that the token “by” is
-predicted with high probability (28.7%).
-
-To find out how T5 arrived at this prediction, we utilize the “similarity
-searcher” component through the datapoint generator (screenshot section (d)).
-This performs a fast approximate nearest-neighbor lookup from a pre-built index
-over the training corpus, using embeddings from the T5 decoder. With one click,
-we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection.
-We see that the words “captain” and “former” appear 34 and 16 times in these
-examples–along with 3 occurrences of “replaced by” (screenshot section (e)),
-suggesting a strong prior toward our erroneous phrase.
-
-![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center}
+### Sequence salience
+
+Sequence salience generalizes token-based salience to text-to-text models,
+allowing you to explain the impact of the prompt tokens on parts of the model
+output.
+
+Check out [here](components.md#sequence-salience) for more details on how to
+navigate the Sequence Salience UI module.
diff --git a/website/src/demos.md b/website/src/demos.md
index e26af521..42568f92 100644
--- a/website/src/demos.md
+++ b/website/src/demos.md
@@ -19,14 +19,6 @@ color: "#49596c"
       tags: "tabular, binary classification",
       external:"true" %}
 
-  {%  include partials/demo-card,
-      c-title: "Image classification",
-      link: "/demos/images.html",
-      c-data-source: "Imagenette",
-      c-copy: "Analyze an image classification model with LIT, including multiple image salience techniques.",
-      tags: "images, multiclass classification",
-      external:"true" %}
-
   {%  include partials/demo-card,
       c-title: "Classification and regression models",
       link: "/demos/glue.html",
@@ -42,37 +34,5 @@ color: "#49596c"
       c-copy: "Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.",
       tags: "BERT, binary classification, notebooks",
       external:"true" %}
-
-  {%  include partials/demo-card,
-      c-title: "Gender bias in coreference systems",
-      link: "/demos/coref.html",
-      c-data-source: "Winogender schemas",
-      c-copy: "Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.",
-      tags: "BERT, coreference, fairness, Winogender",
-      external:"true" %}
-
-  {%  include partials/demo-card,
-      c-title: "Fill in the blanks",
-      link: "/demos/lm.html",
-      c-data-source: "Stanford Sentiment Treebank, Movie Reviews",
-      c-copy: "Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.",
-      tags: "BERT, masked language model",
-      external:"true" %}
-
-  {%  include partials/demo-card,
-      c-title: "Text generation",
-      link: "/demos/t5.html",
-      c-data-source: "CNN / Daily Mail",
-      c-copy: "Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.",
-      tags: "T5, generation",
-      external:"true" %}
-
-  {%  include partials/demo-card,
-      c-title: "Evaluating input salience methods",
-      link: "/demos/is_eval.html",
-      c-data-source: "Stanford Sentiment Treebank, Toxicity",
-      c-copy: "Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.",
-      tags: "BERT, salience, evaluation",
-      external:"true" %}
   </div>
 </div>
diff --git a/website/src/index.md b/website/src/index.md
index bea47e24..690c463a 100644
--- a/website/src/index.md
+++ b/website/src/index.md
@@ -44,8 +44,6 @@ LIT can be run as a standalone server, or inside of python notebook environments
 
 Salience maps
 
-Attention visualization
-
 Metrics calculations
 
 Counterfactual generation
@@ -105,7 +103,7 @@ And more...
 <div class="mdl-grid no-padding">
   {% include partials/home-card image: '/assets/images/LIT_Updates.png',
       action: 'UPDATES',
-      title: 'Version 1.1',
+      title: 'Version 1.2',
       desc: 'Input salience for text-to-text LLMs, with wrappers for HuggingFace Transformers and KerasNLP models.',
       cta-text:"See release notes",
       link: 'https://github.com/PAIR-code/lit/blob/main/RELEASE.md'
diff --git a/website/src/tutorials/sentiment.md b/website/src/tutorials/sentiment.md
index 16dd5dfc..d706e557 100644
--- a/website/src/tutorials/sentiment.md
+++ b/website/src/tutorials/sentiment.md
@@ -18,7 +18,7 @@ takeaways: "Learn about how the metrics table and saliency maps assisted an anal
 
 {% include partials/link-out link: "../../demos/glue.html", text: "Explore this demo yourself." %}
 
-Or, run your own with [`examples/glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+Or, run your own with [`examples/glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.
 
diff --git a/website/src/tutorials/sequence-salience.md b/website/src/tutorials/sequence-salience.md
index 29a99d34..3f5ad91b 100644
--- a/website/src/tutorials/sequence-salience.md
+++ b/website/src/tutorials/sequence-salience.md
@@ -20,7 +20,7 @@ takeaways: "Learn to use LIT's Sequence Salience module for prompt debugging."
     link: "https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb",
     text: "Follow along in Google Colab." %}
 
-Or, run this locally with [`examples/lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+Or, run this locally with [`examples/prompt_debugging/server.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)
 
 Large language models (LLMs), such as [Gemini][gemini] and [GPT-4][gpt4], have
 become ubiquitous. Recent releases of "open weights" models, including
@@ -470,9 +470,9 @@ helpful guides that can help you develop better prompts, including:
 [howitworks_icl]: https://par.nsf.gov/servlets/purl/10462310
 [lit_1_1_release_notes]:https://github.com/PAIR-code/lit/blob/main/RELEASE.md#release-11
 [lit_colab]: https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb
-[lit_hf]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py
+[lit_hf]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py
 [lit_issues]: https://github.com/PAIR-code/lit/issues
-[lit_keras]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py
+[lit_keras]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py
 [lit_sxs]: ../../documentation/ui_guide.html#comparing-datapoints
 [llama]: https://llama.meta.com/
 [main_toolbar]: ../../documentation/ui_guide.html#main-toolbar
diff --git a/website/src/tutorials/tab-feat-attr.md b/website/src/tutorials/tab-feat-attr.md
index 8a36c5ab..709b72d4 100644
--- a/website/src/tutorials/tab-feat-attr.md
+++ b/website/src/tutorials/tab-feat-attr.md
@@ -21,7 +21,7 @@ takeaways: "Learn how to use the Kernel SHAP based Tabular Feature Attribution m
     text: "Explore this demo yourself." %}
 
 Or, run your own with
-[`examples/penguin_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py)
+[`examples/penguin/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py)
 
 LIT supports many techniques like salience maps and counterfactual generators
 for text data. But what if you have a tabular dataset? You might want to find
diff --git a/website/src/tutorials/text-salience.md b/website/src/tutorials/text-salience.md
index 598a1a8b..ecc6c10e 100644
--- a/website/src/tutorials/text-salience.md
+++ b/website/src/tutorials/text-salience.md
@@ -20,7 +20,7 @@ takeaways: "Learn how to use salience maps for text data in LIT."
     link: "../../demos/glue.html",
     text: "Explore this demo yourself." %}
 
-Or, run your own with [`examples/glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+Or, run your own with [`examples/glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 LIT enables users to analyze individual predictions for text input using
 salience maps, for which gradient-based and/or blackbox methods are available.

From 5456011db8ead5d53db6f39bcdca3fc388802fbe Mon Sep 17 00:00:00 2001
From: Fan Ye <fanyecourage@google.com>
Date: Tue, 25 Jun 2024 16:46:32 -0700
Subject: [PATCH 45/47] Cast embeddings to float32 before computing distances.

This fixes a bug where bfloat16 is not supported in nonGPU or nonTPU machine.

PiperOrigin-RevId: 646651355
---
 lit_nlp/components/nearest_neighbors.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/lit_nlp/components/nearest_neighbors.py b/lit_nlp/components/nearest_neighbors.py
index ecc6862b..0b9a5eb3 100644
--- a/lit_nlp/components/nearest_neighbors.py
+++ b/lit_nlp/components/nearest_neighbors.py
@@ -124,7 +124,11 @@ def run(
 
     # <float32>[emb_size]
     dataset_embs = [output[nnconf.embedding_name] for output in dataset_outputs]
+    dataset_embs = [emb.astype(np.float32) for emb in dataset_embs]
+
     example_embs = [example_output[nnconf.embedding_name]]
+    example_embs = [emb.astype(np.float32) for emb in example_embs]
+
     distances = distance.cdist(example_embs, dataset_embs)[0]
     sorted_indices = np.argsort(distances)
     k = nnconf.num_neighbors

From 998ed15613c83b91357bf1e4d95c5656a9f83eed Mon Sep 17 00:00:00 2001
From: Ryan Mullins <ryanmullins@google.com>
Date: Wed, 26 Jun 2024 00:33:20 +0000
Subject: [PATCH 46/47] Website updates for v1.2 release

---
 docs/demos/index.html                         |  55 ----
 docs/documentation/_images/attention.png      | Bin 188417 -> 0 bytes
 docs/documentation/_images/lit-attention.png  | Bin 91620 -> 0 bytes
 .../_images/lit-datapoint-compare.png         | Bin 459728 -> 368329 bytes
 .../documentation/_images/lit-s2s-journey.png | Bin 147695 -> 0 bytes
 .../_images/lit-winogender-metrics.png        | Bin 160995 -> 0 bytes
 docs/documentation/_images/lit-winogender.png | Bin 254066 -> 0 bytes
 docs/documentation/_images/pair-selection.png | Bin 34353 -> 159071 bytes
 docs/documentation/_sources/api.md.txt        |  60 ++---
 docs/documentation/_sources/components.md.txt |  89 ++-----
 docs/documentation/_sources/demos.md.txt      |  99 +------
 docs/documentation/_sources/docker.md.txt     |  34 ++-
 docs/documentation/_sources/faq.md.txt        |  22 +-
 .../_sources/frontend_development.md.txt      |  16 +-
 .../_sources/getting_started.md.txt           |   5 +-
 docs/documentation/_sources/ui_guide.md.txt   | 114 +++-----
 docs/documentation/_static/basic.css          |   2 +-
 docs/documentation/_static/doctools.js        |   2 +-
 docs/documentation/_static/language_data.js   |   4 +-
 docs/documentation/_static/pygments.css       |  12 +-
 docs/documentation/_static/scripts/furo.js    |   2 +-
 .../documentation/_static/scripts/furo.js.map |   2 +-
 docs/documentation/_static/searchtools.js     | 165 +++++++-----
 docs/documentation/_static/styles/furo.css    |   2 +-
 .../documentation/_static/styles/furo.css.map |   2 +-
 docs/documentation/api.html                   | 193 +++++++++-----
 docs/documentation/components.html            | 250 ++++++++++--------
 docs/documentation/demos.html                 | 219 +++++++--------
 docs/documentation/docker.html                | 127 ++++++---
 docs/documentation/faq.html                   | 136 +++++++---
 docs/documentation/frontend_development.html  | 129 ++++++---
 docs/documentation/genindex.html              |  78 +++++-
 docs/documentation/getting_started.html       | 106 ++++++--
 docs/documentation/glossary.html              |  87 +++++-
 .../includes/highlight_demos.html             |  85 +++++-
 docs/documentation/index.html                 |  94 +++++--
 docs/documentation/search.html                |  83 +++++-
 docs/documentation/searchindex.js             |   2 +-
 docs/documentation/ui_guide.html              | 248 +++++++++--------
 docs/index.html                               |   3 +-
 docs/tutorials/coref/index.html               | 187 -------------
 docs/tutorials/generation/index.html          | 149 -----------
 docs/tutorials/index.html                     |   8 -
 docs/tutorials/sentiment/index.html           |   2 +-
 docs/tutorials/sequence-salience/index.html   |  17 +-
 docs/tutorials/tab-feat-attr/index.html       |   2 +-
 docs/tutorials/text-salience/index.html       |   2 +-
 47 files changed, 1483 insertions(+), 1411 deletions(-)
 delete mode 100644 docs/documentation/_images/attention.png
 delete mode 100644 docs/documentation/_images/lit-attention.png
 delete mode 100644 docs/documentation/_images/lit-s2s-journey.png
 delete mode 100644 docs/documentation/_images/lit-winogender-metrics.png
 delete mode 100644 docs/documentation/_images/lit-winogender.png
 delete mode 100644 docs/tutorials/coref/index.html
 delete mode 100644 docs/tutorials/generation/index.html

diff --git a/docs/demos/index.html b/docs/demos/index.html
index 546221f4..3a33dc86 100644
--- a/docs/demos/index.html
+++ b/docs/demos/index.html
@@ -98,17 +98,6 @@
   <div class="demo-card-copy">Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/penguins.html"></a></div>
 </div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lit/demos/images.html" target="_blank">Image classification</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> images </span>  <span class="demo-tag"> multiclass classification </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Imagenette
-  </div>
-  <div class="demo-card-copy">Analyze an image classification model with LIT, including multiple image salience techniques.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/images.html"></a></div>
-</div>
 <div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
   <div class="demo-card-title"><a href="/lit/demos/glue.html" target="_blank">Classification and regression models</a></div>
   <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> binary classification </span>  <span class="demo-tag"> multi-class classification </span>  <span class="demo-tag"> regression </span> 
@@ -130,50 +119,6 @@
   </div>
   <div class="demo-card-copy">Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.</div>
   <div class="demo-card-cta-button"><a href="https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb"></a></div>
-</div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lit/demos/coref.html" target="_blank">Gender bias in coreference systems</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> coreference </span>  <span class="demo-tag"> fairness </span>  <span class="demo-tag"> Winogender </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Winogender schemas
-  </div>
-  <div class="demo-card-copy">Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/coref.html"></a></div>
-</div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lit/demos/lm.html" target="_blank">Fill in the blanks</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> masked language model </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Stanford Sentiment Treebank, Movie Reviews
-  </div>
-  <div class="demo-card-copy">Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/lm.html"></a></div>
-</div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lit/demos/t5.html" target="_blank">Text generation</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> T5 </span>  <span class="demo-tag"> generation </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    CNN / Daily Mail
-  </div>
-  <div class="demo-card-copy">Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/t5.html"></a></div>
-</div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lit/demos/is_eval.html" target="_blank">Evaluating input salience methods</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> salience </span>  <span class="demo-tag"> evaluation </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Stanford Sentiment Treebank, Toxicity
-  </div>
-  <div class="demo-card-copy">Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/is_eval.html"></a></div>
 </div>
   </div>
 </div>
diff --git a/docs/documentation/_images/attention.png b/docs/documentation/_images/attention.png
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diff --git a/docs/documentation/_sources/api.md.txt b/docs/documentation/_sources/api.md.txt
index a174bf39..97ea5b64 100644
--- a/docs/documentation/_sources/api.md.txt
+++ b/docs/documentation/_sources/api.md.txt
@@ -1,6 +1,6 @@
 # LIT Python API
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -349,7 +349,7 @@ list of scores for each token. The Integrated Gradients saliency method
 additionally requires a `TokenEmbeddings` input and corresponding output, as
 well as a label field `Target` to pin the gradient target to the same class as
 an input and corresponding output. See the
-[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
+[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
 for an example of these spec requirements.
 
 The core API involves implementing the `run()` method:
@@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see
 *   A field that appears in _both_ the model's input and output specs is assumed
     to represent the same value. This pattern is used for model-based input
     manipulation. For example, a
-    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
+    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
     might output `'tokens': lit_types.Tokens(...)`, and accept as (optional)
     input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability
     component could take output from the former, swap one or more tokens (e.g.
@@ -712,11 +712,9 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing,
 etc.) when switching between models or datasets with heterogeneous `Spec`s.
 
 When implementing your own LIT components and modules, you can use
-[`utils.find_spec_keys()`][utils-lib]
-(Python) and
-[`findSpecKeys()`][utils-lib]
-(TypeScript) to identify fields of interest in a `Spec`. These methods recognize
-and respect subclasses. For example,
+[`utils.find_spec_keys()`][utils-lib-py] (Python) and
+[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a
+`Spec`. These methods recognize and respect subclasses. For example,
 `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore`
 fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all
 `Scalar` fields in the `Spec`.
@@ -807,8 +805,13 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples)
 
 ### Available types
 
-The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized
-in the table below.
+The full set of `LitType`s is defined in
+[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types
+such as `Integer` and `Scalar` have predefined ranges that can be overridden
+using corresponding `min_val` and `max_val` attributes as seen in
+[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py)
+`INPUT_SPEC`. The different types available in LIT are summarized in the table
+below.
 
 Note: Bracket syntax, such as `<float>[num_tokens]`, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
@@ -859,7 +862,7 @@ naming collisions with protected TypeScript keywords.*
 Some properties of the LIT frontend can be configured from Python as
 **arguments to `dev_server.Server()`**. These include:
 
-*   `page_title`: set a custom page title, such as "Coreference Demo".
+*   `page_title`: set a custom page title.
 *   `canonical_url`: set a "canonical" URL (such as a shortlink) that will be
     used as the base when copying links from the LIT UI.
 *   `default_layout`: set the default UI layout, by name. See `layout.ts` and
@@ -886,22 +889,16 @@ You can specify custom web app layouts from Python via the `layouts=` attribute.
 The value should be a `Mapping[str, LitCanonicalLayout]`, such as:
 
 ```python
-LM_LAYOUT = layout.LitCanonicalLayout(
+PENGUIN_LAYOUT = layout.LitCanonicalLayout(
     upper={
-        "Main": [
-            modules.EmbeddingsModule,
+        'Main': [
+            modules.DiveModule,
             modules.DataTableModule,
             modules.DatapointEditorModule,
         ]
     },
-    lower={
-        "Predictions": [
-            modules.LanguageModelPredictionModule,
-            modules.ConfusionMatrixModule,
-        ],
-        "Counterfactuals": [modules.GeneratorModule],
-    },
-    description="Custom layout for language models.",
+    lower=layout.STANDARD_LAYOUT.lower,
+    description='Custom layout for the Palmer Penguins demo.',
 )
 ```
 
@@ -912,14 +909,12 @@ lit_demo = dev_server.Server(
     models,
     datasets,
     # other args...
-    layouts={"lm": LM_LAYOUT},
+    layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT},
+    default_layout='penguins',
     **server_flags.get_flags())
 return lit_demo.serve()
 ```
 
-For a full example, see
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
-
 You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A
@@ -989,15 +984,15 @@ needing to reload the server or click the UI.
 For example, to view examples in a dataset:
 
 ```python
-from lit_nlp.examples.datasets import glue
-dataset = glue.SST2Data('validation')
+from lit_nlp.examples.glue import data as glue_data
+dataset = glue_data.SST2Data('validation')
 print(dataset.examples)  # list of records {"sentence": ..., "label": ...}
 ```
 
 And to run inference on a few of them:
 
 ```python
-from lit_nlp.examples.models import glue_models
+from lit_nlp.examples.glue import models as glue_models
 
 model = glue_models.SST2Model("/path/to/model/files")
 preds = list(model.predict(dataset.examples[:5]))
@@ -1021,16 +1016,19 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https://
 <!-- Links -->
 
 [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py
-[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
+[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py
 [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py
 [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
 [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py
 [json]: https://www.json.org
 [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/
+
 [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html
-[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
+
+[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py
 [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts
 [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py
 [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts
 [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts
+[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py
 [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py
diff --git a/docs/documentation/_sources/components.md.txt b/docs/documentation/_sources/components.md.txt
index f2366d7c..90b99dbd 100644
--- a/docs/documentation/_sources/components.md.txt
+++ b/docs/documentation/_sources/components.md.txt
@@ -1,6 +1,6 @@
 # Components and Features
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -44,10 +44,9 @@ every invocation.) Generally, you'll need to:
     directly.
 
 Alternatively, you can export to a `SavedModel` and load this in an eager mode
-runtime. This leads to much simpler code (see
-[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)),
-but may require changes to your `SavedModel` exporter in order to access model
-internals like embeddings, gradients, or attention.
+runtime. This leads to much simpler code, but may require changes to your
+`SavedModel` exporter in order to access model internals like embeddings,
+gradients, or attention.
 
 ### Remote or hosted models
 
@@ -105,8 +104,6 @@ LIT supports many features for classification tasks, including common metrics,
 confusion matrices, and custom thresholding via the UI. Classification is
 implemented with the `MulticlassPreds` and `CategoryLabel` types.
 
-*   For a basic example on a binary sentiment task, see
-    [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py).
 *   Models should define a `MulticlassPreds` field in their output spec with the
     `vocab=` attribute as the set of class labels, and for each example should
     return a vector of probabilities for each class.
@@ -116,9 +113,7 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types.
 *   A negative class can be designated using the `null_idx` attribute of
     `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as
     precision, recall, F1 will be computed for the remaining classes. AUC and
-    AUCPR will be computed for binary classification tasks. For an example, see
-    the
-    [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180).
+    AUCPR will be computed for binary classification tasks.
 *   If `null_idx` is set and there is only one other class, the other class
     (often, class `1`) is treated as a positive class, and the LIT UI can be
     used to change the classification threshold. If `null_idx` is set and there
@@ -139,7 +134,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the
     which contains numerical targets, and the model's `RegressionScore` field
     should set `parent=` to the name of this field.
 *   For an example, see the STS-B textual similarity task in
-    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
+    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 ### Multi-label classification
 
@@ -147,8 +142,6 @@ LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 `SparseMultilabelPreds` and `SparseMultilabel` types.
 
-*   For a basic example on an image labeling task, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
 *   Models should define a `SparseMultilabelPreds` field in their output spec
     with the`vocab=` attribute as the set of class labels, and for each example
     should return a list of class score tuples. Each tuple contains two
@@ -179,8 +172,6 @@ or decoder.
     the model can also output a `ReferenceScores` field (with values as
     `list[float]`) with `parent=` set to reference a `TextSegment` or
     `ReferenceTexts` field from the input.
-*   For modeling examples, see
-    [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py)
 
 ![Generated Text Module](images/components/generation-results.png){w=600px align=center}
 
@@ -215,7 +206,7 @@ and otherwise to different parts of the input.
     [Dataset class documentation](./api.md#datasets) and the corresponding
     [Model](./api.md#models).
 *   For a more involved code example including per-token gradients, see
-    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
+    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
 ### Tabular data
 
@@ -233,7 +224,7 @@ these cases, LIT validates model compatibility by checking that:
     (`SparseMultilabel`) field.
 
 For a demo using a penguin stats dataset/binary classification task, see
-[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py).
+[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py).
 
 ### Images
 
@@ -246,12 +237,6 @@ NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don't break existing checked-in code with
 such a change.
 
-*   See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a
-    dataset containing images, including converting images to base64 encoded
-    strings.
-*   For a demo of an image classifier, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
-
 ## Token-based Salience
 
 LIT supports several methods for token-based input salience, including
@@ -301,8 +286,8 @@ $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$
 
 Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-$\hat{y}$, while a negative score suggests that the prediction would be stronger
-if that token was removed.
+$\hat{y}$, while a negative score suggests that the prediction would be
+stronger if that token was removed.
 
 To enable this method, your model should, as part of the
 [output spec and `predict()` implementation](./api.md#models):
@@ -366,7 +351,7 @@ An example spec would look like:
 
 For a more concrete example that also supports multiple segments with separate
 gradients, see our
-[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py),
+[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py),
 or contact the LIT team for assistance.
 
 ### LIME
@@ -448,7 +433,7 @@ left-to-right ("causal") language models:
 
 The UI supports multiple options for analysis, including:
 
-*   Select from pre-defined target sequences, or explain generations from the
+*   Select from predefined target sequences, or explain generations from the
     model.
 *   Different salience methods, including [Gradient Norm](#gradient-norm) and
     [Gradient-dot-Input](#gradient-dot-input).
@@ -456,7 +441,7 @@ The UI supports multiple options for analysis, including:
     to words, sentences, lines, or paragraphs. Quickly switch between different
     views to refine your analysis to different parts of a prompt.
 *   Display density options to enable working with longer sequences, such as
-    document text, few-shot eaxmples, or chain-of-thought prompts.
+    document text, few-shot examples, or chain-of-thought prompts.
 
 For a walkthrough of how to use sequence salience to debug LLMs, check out the
 Responsible Generative AI Toolkit at
@@ -482,13 +467,11 @@ using either KerasNLP or Transformers.
 *   LIT-for-Gemma Colab:
     [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb)
 *   Demo binary:
-    [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py
 *   KerasNLP model wrappers:
-    [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py)
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py
 *   Transformers model wrappers:
-    [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
-
-
+    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py
 
 ## Salience Clustering
 
@@ -540,9 +523,7 @@ your model should, as part of the
     input image.
 
     The model should be able to accept input images as numpy arrays in addition
-    to accepting base64 URL encoded format. See
-    [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py)
-    for an example.
+    to accepting base64 URL encoded format.
 
 A variety of image saliency techniques are implemented for models that return
 image gradients, through use of the
@@ -553,36 +534,6 @@ and XRAI.
 Each of these techniques returns a saliency map image as a base64-encoded string
 through the `ImageSalience` type.
 
-## Attention
-
-LIT can display a visualization of attention heads from transformers and other
-models:
-
-![Attention Visualization](./images/components/attention.png){w=400px align=center}
-
-To enable this, your model should return one or more fields of the type
-`AttentionHeads`, with values as arrays of shape `<float>[num_heads, num_tokens,
-num_tokens]`. Each field represents a set of heads at a single layer of the
-model, so models will often have more than one:
-
-```python
-   def output_spec(self) -> types.Spec:
-     return {
-         # ...
-         "tokens": lit_types.Tokens(parent="input_text"),
-         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         # ...
-     }
-```
-
-The `align_in` and `align_out` attributes are the names of `Tokens` fields for
-the source and target tokens for that layer. For self-attention (such as in
-BERT) these would name the same field, but for encoder-decoder attention they
-could reference different token sets (such as "input_tokens" and
-"target_tokens").
-
 ## Embedding Projector
 
 LIT includes a version of the
@@ -614,7 +565,7 @@ example, we could facet by class label:
 
 ![Metrics Table](./images/components/metrics-table.png)
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
 
 To enable metrics, your model should set the `parent` attribute on one or more
 output fields, pointing to the name of the input field that it should be
@@ -653,7 +604,7 @@ analysis.
 
 ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center}
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
 
 The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (`CategoryLabel`).
@@ -739,6 +690,8 @@ datapoints, giving a global view of feature effects.
 
 ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center}
 
+To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab.
+
 ### Dive
 
 Dive is a visualization module, inspired by our prior work on
diff --git a/docs/documentation/_sources/demos.md.txt b/docs/documentation/_sources/demos.md.txt
index 306d3139..448c0d23 100644
--- a/docs/documentation/_sources/demos.md.txt
+++ b/docs/documentation/_sources/demos.md.txt
@@ -1,6 +1,6 @@
 # Demos
 
-<!-- freshness: { owner: 'lit-dev' reviewed: '2023-08-29' } -->
+<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/.
 ### Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \
-**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 *   Multi-task demo:
     *   Sentiment analysis as a binary classification task
@@ -36,26 +36,12 @@ https://pair-code.github.io/lit/demos/.
         the margin.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
-    *   Attention visualization.
     *   Counterfactual generators, including HotFlip for targeted adversarial
         perturbations.
 
 Tip: check out a case study for this demo on the public LIT website:
 https://pair-code.github.io/lit/tutorials/sentiment
 
-### Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} -->
-
-**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py)
-
-*   [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of
-    MultiNLI into 14 different languages.
-*   Specify `--languages=en,jp,hi,...` flag to select which languages to load.
-*   NLI as a three-way classification task with two-segment input (premise,
-    hypothesis).
-*   Fine-tuned multilingual BERT model.
-*   Salience methods work with non-whitespace-delimited text, by using the
-    model's wordpiece tokenization.
-
 --------------------------------------------------------------------------------
 
 ## Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} -->
@@ -63,7 +49,7 @@ https://pair-code.github.io/lit/tutorials/sentiment
 ### Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \
-**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
+**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
 
 *   STS-B textual similarity task, predicting scores on a range from 0
     (unrelated) to 5 (very similar).
@@ -74,7 +60,6 @@ https://pair-code.github.io/lit/tutorials/sentiment
         quick filtering.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
-    *   Attention visualization.
 
 --------------------------------------------------------------------------------
 
@@ -82,76 +67,20 @@ https://pair-code.github.io/lit/tutorials/sentiment
 
 ### Gemma <!-- DO NOT REMOVE {#gemma .demo-header} -->
 
-**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+**Code:**
+[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)
 
-*   Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
+*   Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
+    and Transformers (with PyTorch).
 *   Interactively debug LLM prompts using
     [sequence salience](./components.md#sequence-salience).
 *   Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-    granularities: token-, word-, sentence-, and paragraph-level.
+    granularities: token-, word-, line-, sentence-, and paragraph-level.
 
 Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
 Generative AI Toolkit.
 
-### T5 <!-- DO NOT REMOVE {#t5 .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \
-**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py)
-
-*   Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
-    formats.
-*   Visualize beam candidates and highlight diffs against references.
-*   Visualize per-token decoder hypotheses to see where the model veers away
-    from desired output.
-*   Filter examples by ROUGE score against reference.
-*   Embeddings from last layer of model, visualized with UMAP or PCA.
-*   Task wrappers to handle pre- and post-processing for summarization and
-    machine translation tasks.
-*   Pre-loaded eval sets for CNNDM and WMT.
-
-Tip: check out a case study for this demo on the public LIT website:
-https://pair-code.github.io/lit/tutorials/generation
-
---------------------------------------------------------------------------------
-
-## Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} -->
-
-### BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \
-**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py)
-
-*   Compare multiple BERT and GPT-2 models side-by-side on a variety of
-    plain-text corpora.
-*   LM visualization supports different modes:
-    *   BERT masked language model: click-to-mask, and query model at that
-        position.
-    *   GPT-2 shows left-to-right hypotheses for each target token.
-*   Embedding projector to show latent space of the model.
-
---------------------------------------------------------------------------------
-
-## Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} -->
-
-### Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \
-**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py)
-
-*   Gold-mention coreference model, trained on
-    [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19).
-*   Evaluate on the Winogender schemas
-    ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for
-    gendered associations with profession names.
-*   Visualizations of coreference edges, as well as binary classification
-    between two candidate referents.
-*   Stratified metrics for quantifying model bias as a function of pronoun
-    gender or Bureau of Labor Statistics profession data.
-
-Tip: check out a case study for this demo on the public LIT website:
-https://pair-code.github.io/lit/tutorials/coref
-
 --------------------------------------------------------------------------------
 
 ## Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} -->
@@ -159,7 +88,7 @@ https://pair-code.github.io/lit/tutorials/coref
 ### Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \
-**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py)
+**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py)
 
 *   Binary classification on
     [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins).
@@ -169,13 +98,3 @@ https://pair-code.github.io/lit/tutorials/coref
 *   Use binary classifier threshold setters to find best thresholds for slices
     of examples to achieve specific fairness constraints, such as demographic
     parity.
-
-### Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \
-**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py)
-
-*   Classification on ImageNet labels using a MobileNet model.
-*   Showing using of LIT on image data.
-*   Explore results of multiple gradient-based image saliency techniques in the
-    Salience Maps module.
diff --git a/docs/documentation/_sources/docker.md.txt b/docs/documentation/_sources/docker.md.txt
index c9cad085..ebf7eb7c 100644
--- a/docs/documentation/_sources/docker.md.txt
+++ b/docs/documentation/_sources/docker.md.txt
@@ -1,6 +1,6 @@
 # Running LIT in a Docker container
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-07-10' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-04' } *-->
 
 Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
@@ -13,22 +13,21 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or
 your preferred engine. This is how we run our
 [hosted demos](https://pair-code.github.io/lit/demos/).
 
-We provide a basic
-[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can
-use to build and run any of the demos in the `lit_nlp/examples` directory. The
-`Dockerfile` installs all necessary dependencies for LIT and builds the
+We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory.
+The `Dockerfile` installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as
 the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py).
 You can find a reference implementation in
-[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
+[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
 
-Use the following shell commands to build the default Docker image for LIT from
-the provided `Dockerfile`, and then run a container from that image. Comments
-are provided in-line to help explain what each step does.
+Use the following shell
+https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the
+default Docker image for LIT from the provided `Dockerfile`, and then run a
+container from that image. Comments are provided in-line to help explain what
+each step does.
 
 ```shell
 # Build the docker image using the -t argument to name the image. Remember to
@@ -48,14 +47,11 @@ below.
 ```shell
 # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME"
+#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as
-#
-#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
-#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
+# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -66,15 +62,15 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp
 # containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
 ```
 
 ## Integrating Custom LIT Instances with the Default Docker Image
 
 Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a `main()` method, as
-described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data).
+described in the [Python API docs](api.md#adding-models-and-data).
 
 These custom server scripts can be easily integrated with LIT's default image as
 long as your server meets two requirements:
diff --git a/docs/documentation/_sources/faq.md.txt b/docs/documentation/_sources/faq.md.txt
index de6a5b57..aa8bd03d 100644
--- a/docs/documentation/_sources/faq.md.txt
+++ b/docs/documentation/_sources/faq.md.txt
@@ -1,6 +1,6 @@
 # Frequently Asked Questions
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-19' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -17,9 +17,7 @@ works with any modern ML framework. For more information, see
 In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
 
-*   [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) -
-    image classification, using a Mobilenet model.
-*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) -
+*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the
     [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins)
@@ -32,13 +30,7 @@ For more details, see
 
 All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications. For examples, see:
-
-*   [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) -
-    cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
-    model.
-*   [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) -
-    includes WMT data for machine translation
+scripts should work without any modifications.
 
 ## Scale
 
@@ -99,8 +91,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir`
 is specified, may be cached to disk.
 
 LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to
-disk, then pass the `--demo_mode` runtime flag to the LIT server.
+disk. If you do not want the tool to be able to write edited datapoints to disk,
+then pass the `--demo_mode` runtime flag to the LIT server.
 
 ### I have proprietary data. Is LIT secure for my team to use?
 
@@ -177,7 +169,5 @@ official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT's `Dataset` objects to manage training data along with standard
 training APIs (such as Keras' `model.fit()`). See
-[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py)
-and/or
-[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
+[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
 for examples.
diff --git a/docs/documentation/_sources/frontend_development.md.txt b/docs/documentation/_sources/frontend_development.md.txt
index a51aed81..fe8a82bb 100644
--- a/docs/documentation/_sources/frontend_development.md.txt
+++ b/docs/documentation/_sources/frontend_development.md.txt
@@ -1,6 +1,6 @@
 # Frontend Developer Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go
 into greater detail later.
 
 The JS bundle entry point is
-[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which
-first imports the loaded, the `<lit-app>` web component is declared, and
-attaches itself to the DOM, waiting for the app to be initialized.
+[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first
+imports the loaded, the `<lit-app>` web component is declared, and attaches
+itself to the DOM, waiting for the app to be initialized.
 
 The second step is kicking off app initialization. The
 [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton
@@ -67,7 +67,7 @@ pre-configured layouts in
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `experimental`: A three-panel layout that puts exploratory data
+*   `three_panel`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -76,15 +76,15 @@ pre-configured layouts in
 
 You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT
 instance by defining one or more `LitCanonicalLayout` instances and passing them
-to the server. For an example, see `CUSTOM_LAYOUTS` in
-[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
+to the server. For an example, see
+[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py).
 
 Note: The pre-configured layouts are added to every `LitApp` instance using
 [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where
 the Mapping passed to the `LitApp` constructor overrides the pre-configured
 layouts `Mapping`. Thus, you can remove or change these pre-configured layouts
 as you like by passing a `Mapping` where the values of `simple`, `default`,
-and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance
+and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance
 (to override) as you desire.
 
 The actual layout of components in the LIT UI, see
diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt
index 80e4eae9..0ca605bf 100644
--- a/docs/documentation/_sources/getting_started.md.txt
+++ b/docs/documentation/_sources/getting_started.md.txt
@@ -1,6 +1,6 @@
 # Getting Started with LIT
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2022-08-17' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -72,8 +72,7 @@ variety of model types. The code for these is under [examples](https://github.co
 ;
 each is a small script that loads one or more models and starts a LIT server.
 
-Most demos can be run with a single blaze command. To run the default one, you
-can do:
+Most demos can be run with a single command. To run the default one, you can do:
 
 ```sh
 python -m lit_nlp.examples.glue.demo \
diff --git a/docs/documentation/_sources/ui_guide.md.txt b/docs/documentation/_sources/ui_guide.md.txt
index 4a38a88c..bb5028ae 100644
--- a/docs/documentation/_sources/ui_guide.md.txt
+++ b/docs/documentation/_sources/ui_guide.md.txt
@@ -1,6 +1,6 @@
 # UI Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 
 This is a user guide for the Learning Interpretability Tool (LIT) UI.
 
@@ -42,7 +42,7 @@ LIT provides three pre-configured layouts:
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `experimental`: A three-panel layout that puts exploratory data
+*   `three_panel`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -130,19 +130,26 @@ controls and information. The left side of the toolbar contains a set of menus
 for quickly controlling datapoint selection and coloring. This includes the
 following controls:
 
-*   The **"Select related"** option adds any datapoints "related" to the current
-    selection. In LIT, "related" is defined as datapoints created from some
-    source datapoint (through manual editing or a datapoint generator), or a
-    source datapoint that a selected datapoint was created from.
-*   The **"Clear selection"** button deselects all selected datapoints. The
-    dropdown also contains a color legend for the current color setting.
-*   The **Slices** option allows quick selection of an already-created slice of
-    datapoints.
-*   The **"Datapoint color"** menu enables setting of the color of each
-    datapoint in the modules that visualize all datapoints (such as the
-    *Embeddings* and *Scalars* modules) by any number of datapoint features or
-    model outputs on those datapoints (such as coloring by some categorical
-    input feature, or by prediction error for a regression task).
+*   The **"Select datapoint"** menu provides a drop-down of several options:
+    *   the **"Random"** option selects a random datapoint,
+    *   the **"All related"** option adds any datapoints "related" to the
+        current selection. In LIT, "related" is defined as datapoints created
+        from some source datapoint (through manual editing or a datapoint
+        generator), or a source datapoint that a selected datapoint was created
+        from,
+    *   the **"Parents"** option adds the source datapoints that the selected
+        datapoints were created from,
+    *   the **"Children"** option adds the datapoints created from the selected
+        datapoints (through manual editing or a datapoint generator),
+    *   the **Slices** option allows quick selection of an already-created slice
+        of datapoints,
+    *   the **"Clear selection"** button deselects all selected datapoints.
+*   The **"Color by"** menu enables setting of the color of each datapoint in
+    the modules that visualize all datapoints (such as the *Embeddings* and
+    *Scalars* modules) by any number of datapoint features or model outputs on
+    those datapoints (such as coloring by some categorical input feature, or by
+    prediction error for a regression task).
+*   The **Slices** menu allows adding/selecting/removing slices of datapoints.
 
 Next to the menus is a button for pinning/unpinning a datapoint. Pinning a
 datapoint puts LIT into datapoint comparison mode, where two datapoints can be
@@ -322,9 +329,9 @@ such as model predictions, enable them from the "Columns" dropdown.
 
 The datapoint editor shows the details of the primary selected datapoint, if one
 is selected. Any field can be edited, and a new datapoint created with those
-edits through the **"Make new datapoint"** button. Any edit to an existing
-datapoint must be saved as a new datapoint to be explored, to keep datapoints
-immutable for simplicity of use.
+edits through the **"Add"** button. Any edit to an existing datapoint must be
+saved as a new datapoint to be explored, to keep datapoints immutable for
+simplicity of use.
 
 When no datapoint is selected, the editor shows a blank datapoint that can be
 filled out by hand to create a completely new datapoint.
@@ -473,24 +480,15 @@ background of each text piece is colored by the salience of that piece on the
 prediction, and hovering on any piece will display the exact value calculated
 for that piece.
 
-There is an **"autorun"** button by each methodology. If it is checked, then
-that calculation is made when a new primary datapoint is selected. If it is
+There is an **"autorun"** button by each methodology on the right side of the
+bar (the methodoloy name is on the left side). If it is checked, then that
+calculation is made when a new primary datapoint is selected. If it is
 unchecked, the calculation isn't made until it is checked. This can be valuable
 so that expensive, long-running saliency calculations (such as LIME) aren't
 performed on every datapoint selection, but only when explicitly asked for.
 
 ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps")
 
-### Attention
-
-For models that return attention head information along with predictions, the
-attention module shows which tokens are attended to between layers of a model.
-Dropdowns allow you to select which layer and attention head is being
-visualized. Line opacity is controlled by the magnitude of the attention between
-those tokens.
-
-![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center}
-
 ## User Journeys
 
 In this section, we explore some example user journeys and how LIT enables them.
@@ -515,51 +513,11 @@ model.
 
 ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis")
 
-### Gender Bias in Coreference
-
-Does a system encode gendered associations, which might lead to incorrect
-predictions? We load a coreference model trained on OntoNotes, and load the
-Winogender dataset into LIT for evaluation. Each Winogender example has a
-pronoun and two candidate referents, one a occupation term like (“technician”)
-and one is an “other participant” (like “customer”). Our model predicts
-coreference probabilities for each candidate. We can explore the model’s
-sensitivity to pronouns by comparing two examples side-by-side (see structured
-predictions in screenshot). We can see how commonly the model makes similar
-errors by paging through the dataset, or by selecting specific slices of
-interest.
-
-![Winogender analysis](./images/lit-winogender.png "Winogender analysis")
-
-In the *Metrics* table, we can slice this selection by pronoun type and by the
-true referent. On the set of male-dominated occupations (< 25% female by BLS),
-we see the model performs well when the ground-truth agrees with the
-stereotype - e.g. when the answer is the occupation term, male pronouns are
-correctly resolved 83% of the time, compared to female pronouns only 37.5% of
-the time (screenshot section (c)).
-
-![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis")
-
-### Debugging Text Generation
-
-<!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a
-     example that fits better with the text generation debugging story -->
-
-Does the training data explain a particular error in text generation? We analyze
-an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module
-allows us to look at per-example ROUGE scores, and quickly select an example
-with middling performance (screenshot section (a)). We find the generated text
-(screenshot section (b)) contains an erroneous constituent: “alastair cook was
-replaced as captain by former captain ...”. We can dig deeper, using LIT’s
-language modeling module (screenshot section (c)) to see that the token “by” is
-predicted with high probability (28.7%).
-
-To find out how T5 arrived at this prediction, we utilize the “similarity
-searcher” component through the datapoint generator (screenshot section (d)).
-This performs a fast approximate nearest-neighbor lookup from a pre-built index
-over the training corpus, using embeddings from the T5 decoder. With one click,
-we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection.
-We see that the words “captain” and “former” appear 34 and 16 times in these
-examples–along with 3 occurrences of “replaced by” (screenshot section (e)),
-suggesting a strong prior toward our erroneous phrase.
-
-![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center}
+### Sequence salience
+
+Sequence salience generalizes token-based salience to text-to-text models,
+allowing you to explain the impact of the prompt tokens on parts of the model
+output.
+
+Check out [here](components.md#sequence-salience) for more details on how to
+navigate the Sequence Salience UI module.
diff --git a/docs/documentation/_static/basic.css b/docs/documentation/_static/basic.css
index 30fee9d0..f316efcb 100644
--- a/docs/documentation/_static/basic.css
+++ b/docs/documentation/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/documentation/_static/doctools.js b/docs/documentation/_static/doctools.js
index d06a71d7..4d67807d 100644
--- a/docs/documentation/_static/doctools.js
+++ b/docs/documentation/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/documentation/_static/language_data.js b/docs/documentation/_static/language_data.js
index 250f5665..367b8ed8 100644
--- a/docs/documentation/_static/language_data.js
+++ b/docs/documentation/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,7 @@
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
diff --git a/docs/documentation/_static/pygments.css b/docs/documentation/_static/pygments.css
index c2e07c71..02b4b128 100644
--- a/docs/documentation/_static/pygments.css
+++ b/docs/documentation/_static/pygments.css
@@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /*
 body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
 body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
 body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
-body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */
+body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */
 body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
 body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */
+body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */
 body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
 body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */
 body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */
 body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
 body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
 body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
-body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */
+body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */
 body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */
 body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */
 body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
@@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol
 body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
 body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
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diff --git a/docs/documentation/_static/scripts/furo.js b/docs/documentation/_static/scripts/furo.js
index 32e7c05b..0267c7e1 100644
--- a/docs/documentation/_static/scripts/furo.js
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get default export","webpack:///webpack/runtime/define property getters","webpack:///webpack/runtime/global","webpack:///webpack/runtime/hasOwnProperty shorthand","webpack:///./src/furo/assets/scripts/furo.js"],"sourcesContent":["/*!\n * gumshoejs v5.1.2 (patched by @pradyunsg)\n * A simple, framework-agnostic scrollspy script.\n * (c) 2019 Chris Ferdinandi\n * MIT License\n * http://github.com/cferdinandi/gumshoe\n */\n\n(function (root, factory) {\n  if (typeof define === \"function\" && define.amd) {\n    define([], function () {\n      return factory(root);\n    });\n  } else if (typeof exports === \"object\") {\n    module.exports = factory(root);\n  } else {\n    root.Gumshoe = factory(root);\n  }\n})(\n  typeof global !== \"undefined\"\n    ? global\n    : typeof window !== \"undefined\"\n      ? window\n      : this,\n  function (window) {\n    \"use strict\";\n\n    //\n    // Defaults\n    //\n\n    var defaults = {\n      // Active classes\n      navClass: \"active\",\n      contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) 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contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) {\n          if (current) {\n            deactivate(current, settings);\n            current = null;\n          }\n          return;\n        }\n\n        // If the active content is the one currently active, do nothing\n        if (current && active.content === current.content) return;\n\n        // Deactivate the current content and activate the new content\n        deactivate(current, settings);\n        activate(active, settings);\n\n        // Update the currently active content\n        current = active;\n      };\n\n      /**\n       * Detect the active content on scroll\n       * Debounced for performance\n       */\n      var scrollHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(publicAPIs.detect);\n      };\n\n      /**\n       * Update content sorting on resize\n       * Debounced for performance\n       */\n      var resizeHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(function () {\n          sortContents(contents);\n          publicAPIs.detect();\n        });\n      };\n\n      /**\n       * Destroy the current instantiation\n       */\n      publicAPIs.destroy = function () {\n        // Undo DOM changes\n        if (current) {\n          deactivate(current, settings);\n        }\n\n        // Remove event listeners\n        window.removeEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.removeEventListener(\"resize\", resizeHandler, false);\n        }\n\n        // Reset variables\n        contents = null;\n        navItems = null;\n        current = null;\n        timeout = null;\n        settings = null;\n      };\n\n      /**\n       * Initialize the current instantiation\n       */\n      var init = function () {\n        // Merge user options into defaults\n        settings = extend(defaults, options || {});\n\n        // Setup variables based on the current DOM\n        publicAPIs.setup();\n\n        // Find the currently active content\n        publicAPIs.detect();\n\n        // Setup event listeners\n        window.addEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.addEventListener(\"resize\", resizeHandler, false);\n        }\n      };\n\n      //\n      // Initialize and return the public APIs\n      //\n\n      init();\n      return publicAPIs;\n    };\n\n    //\n    // Return the Constructor\n    //\n\n    return Constructor;\n  },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n  if (Math.floor(header.getBoundingClientRect().top) == 0) {\n    header.classList.add(\"scrolled\");\n  } else {\n    header.classList.remove(\"scrolled\");\n  }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n  if (positionY < GO_TO_TOP_OFFSET) {\n    document.documentElement.classList.remove(\"show-back-to-top\");\n  } else {\n    if (positionY < lastScrollTop) {\n      document.documentElement.classList.add(\"show-back-to-top\");\n    } else if (positionY > lastScrollTop) {\n      document.documentElement.classList.remove(\"show-back-to-top\");\n    }\n  }\n  lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // top of page.\n  if (positionY == 0) {\n    tocScroll.scrollTo(0, 0);\n  } else if (\n    // bottom of page.\n    Math.ceil(positionY) >=\n    Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n  ) {\n    tocScroll.scrollTo(0, tocScroll.scrollHeight);\n  } else {\n    // somewhere in the middle.\n    const current = document.querySelector(\".scroll-current\");\n    if (current == null) {\n      return;\n    }\n\n    // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n    // // scroll the currently \"active\" heading in toc, into view.\n    // const rect = current.getBoundingClientRect();\n    // if (0 > rect.top) {\n    //   current.scrollIntoView(true); // the argument is \"alignTop\"\n    // } else if (rect.bottom > window.innerHeight) {\n    //   current.scrollIntoView(false);\n    // }\n  }\n}\n\nfunction scrollHandler(positionY) {\n  scrollHandlerForHeader();\n  scrollHandlerForBackToTop(positionY);\n  scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  document.body.dataset.theme = mode;\n  localStorage.setItem(\"theme\", mode);\n  console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n  const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n  const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n  if (prefersDark) {\n    // Auto (dark) -> Light -> Dark\n    if (currentTheme === \"auto\") {\n      setTheme(\"light\");\n    } else if (currentTheme == \"light\") {\n      setTheme(\"dark\");\n    } else {\n      setTheme(\"auto\");\n    }\n  } else {\n    // Auto (light) -> Dark -> Light\n    if (currentTheme === \"auto\") {\n      setTheme(\"dark\");\n    } else if (currentTheme == \"dark\") {\n      setTheme(\"light\");\n    } else {\n      setTheme(\"auto\");\n    }\n  }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n  // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n  let last_known_scroll_position = 0;\n  let ticking = false;\n\n  window.addEventListener(\"scroll\", function (e) {\n    last_known_scroll_position = window.scrollY;\n\n    if (!ticking) {\n      window.requestAnimationFrame(function () {\n        scrollHandler(last_known_scroll_position);\n        ticking = false;\n      });\n\n      ticking = true;\n    }\n  });\n  window.scroll();\n}\n\nfunction setupScrollSpy() {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // Scrollspy -- highlight table on contents, based on scroll\n  new Gumshoe(\".toc-tree a\", {\n    reflow: true,\n    recursive: true,\n    navClass: \"scroll-current\",\n    offset: () => {\n      let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n      return header.getBoundingClientRect().height + 2.5 * rem + 1;\n    },\n  });\n}\n\nfunction setupTheme() {\n  // Attach event handlers for toggling themes\n  const buttons = document.getElementsByClassName(\"theme-toggle\");\n  Array.from(buttons).forEach((btn) => {\n    btn.addEventListener(\"click\", cycleThemeOnce);\n  });\n}\n\nfunction setup() {\n  setupTheme();\n  setupScrollHandler();\n  setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n  document.body.parentNode.classList.remove(\"no-js\");\n\n  header = document.querySelector(\"header\");\n  tocScroll = document.querySelector(\".toc-scroll\");\n\n  setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/docs/documentation/_static/searchtools.js b/docs/documentation/_static/searchtools.js
index 7918c3fa..92da3f8b 100644
--- a/docs/documentation/_static/searchtools.js
+++ b/docs/documentation/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -99,7 +99,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms)
+            Search.makeSearchSummary(data, searchTerms, anchor)
           );
         // highlight search terms in the summary
         if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
@@ -116,8 +116,8 @@ const _finishSearch = (resultCount) => {
     );
   else
     Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
 };
 const _displayNextItem = (
   results,
@@ -137,6 +137,22 @@ const _displayNextItem = (
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -160,13 +176,26 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString) => {
+  htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
+    if (docContent) return docContent.textContent;
+
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
     );
     return "";
   },
@@ -239,16 +268,7 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
+  _parseQuery: (query) => {
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -284,16 +304,32 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase();
+    const queryLower = query.toLowerCase().trim();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
           let score = Math.round(100 * queryLower.length / title.length)
-          results.push([
+          normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
@@ -308,46 +344,47 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id] of foundEntries) {
-          let score = Math.round(100 * queryLower.length / entry.length)
-          results.push([
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-          ]);
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -361,7 +398,12 @@ const Search = {
       return acc;
     }, []);
 
-    results = results.reverse();
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
@@ -466,14 +508,18 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
       }
 
       // no match but word was a required one
@@ -496,9 +542,8 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
       });
     });
 
@@ -549,8 +594,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords) => {
-    const text = Search.htmlToText(htmlText);
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();
diff --git a/docs/documentation/_static/styles/furo.css b/docs/documentation/_static/styles/furo.css
index 3d29a218..e3d4e57b 100644
--- a/docs/documentation/_static/styles/furo.css
+++ b/docs/documentation/_static/styles/furo.css
@@ -1,2 +1,2 @@
-/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{-webkit-text-size-adjust:100%;line-height:1.15}body{margin:0}main{display:block}h1{font-size:2em;margin:.67em 0}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-family:monospace,monospace;font-size:1em}a{background-color:transparent}abbr[title]{border-bottom:none;text-decoration:underline;text-decoration:underline dotted}b,strong{font-weight:bolder}code,kbd,samp{font-family:monospace,monospace;font-size:1em}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}img{border-style:none}button,input,optgroup,select,textarea{font-family:inherit;font-size:100%;line-height:1.15;margin:0}button,input{overflow:visible}button,select{text-transform:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{border-style:none;padding:0}[type=button]:-moz-focusring,[type=reset]:-moz-focusring,[type=submit]:-moz-focusring,button:-moz-focusring{outline:1px dotted ButtonText}fieldset{padding:.35em .75em .625em}legend{box-sizing:border-box;color:inherit;display:table;max-width:100%;padding:0;white-space:normal}progress{vertical-align:baseline}textarea{overflow:auto}[type=checkbox],[type=radio]{box-sizing:border-box;padding:0}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}details{display:block}summary{display:list-item}[hidden],template{display:none}@media print{.content-icon-container,.headerlink,.mobile-header,.related-pages{display:none!important}.highlight{border:.1pt solid var(--color-foreground-border)}a,blockquote,dl,ol,pre,table,ul{page-break-inside:avoid}caption,figure,h1,h2,h3,h4,h5,h6,img{page-break-after:avoid;page-break-inside:avoid}dl,ol,ul{page-break-before:avoid}}.visually-hidden{clip:rect(0,0,0,0)!important;border:0!important;height:1px!important;margin:-1px!important;overflow:hidden!important;padding:0!important;position:absolute!important;white-space:nowrap!important;width:1px!important}:-moz-focusring{outline:auto}body{--font-stack:-apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji;--font-stack--monospace:"SFMono-Regular",Menlo,Consolas,Monaco,Liberation Mono,Lucida Console,monospace;--font-size--normal:100%;--font-size--small:87.5%;--font-size--small--2:81.25%;--font-size--small--3:75%;--font-size--small--4:62.5%;--sidebar-caption-font-size:var(--font-size--small--2);--sidebar-item-font-size:var(--font-size--small);--sidebar-search-input-font-size:var(--font-size--small);--toc-font-size:var(--font-size--small--3);--toc-font-size--mobile:var(--font-size--normal);--toc-title-font-size:var(--font-size--small--4);--admonition-font-size:0.8125rem;--admonition-title-font-size:0.8125rem;--code-font-size:var(--font-size--small--2);--api-font-size:var(--font-size--small);--header-height:calc(var(--sidebar-item-line-height) + var(--sidebar-item-spacing-vertical)*4);--header-padding:0.5rem;--sidebar-tree-space-above:1.5rem;--sidebar-caption-space-above:1rem;--sidebar-item-line-height:1rem;--sidebar-item-spacing-vertical:0.5rem;--sidebar-item-spacing-horizontal:1rem;--sidebar-item-height:calc(var(--sidebar-item-line-height) + var(--sidebar-item-spacing-vertical)*2);--sidebar-expander-width:var(--sidebar-item-height);--sidebar-search-space-above:0.5rem;--sidebar-search-input-spacing-vertical:0.5rem;--sidebar-search-input-spacing-horizontal:0.5rem;--sidebar-search-input-height:1rem;--sidebar-search-icon-size:var(--sidebar-search-input-height);--toc-title-padding:0.25rem 0;--toc-spacing-vertical:1.5rem;--toc-spacing-horizontal:1.5rem;--toc-item-spacing-vertical:0.4rem;--toc-item-spacing-horizontal:1rem;--icon-search:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><circle cx="10" cy="10" r="7"/><path d="m21 21-6-6"/></svg>');--icon-pencil:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>');--icon-abstract:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z"/></svg>');--icon-info:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2z"/></svg>');--icon-flame:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 0 1-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z"/></svg>');--icon-question:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m15.07 11.25-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 0 0-2-2 2 2 0 0 0-2 2H8a4 4 0 0 1 4-4 4 4 0 0 1 4 4 3.2 3.2 0 0 1-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10c0-5.53-4.5-10-10-10z"/></svg>');--icon-warning:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z"/></svg>');--icon-failure:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z"/></svg>');--icon-spark:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m11.5 20 4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 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);--color-sidebar-search-text:var(--color-foreground-primary);--color-sidebar-search-background:var(--color-background-secondary);--color-sidebar-search-background--focus:var(--color-background-primary);--color-sidebar-search-border:var(--color-background-border);--color-sidebar-search-icon:var(--color-foreground-muted);--color-toc-background:var(--color-background-primary);--color-toc-title-text:var(--color-foreground-muted);--color-toc-item-text:var(--color-foreground-secondary);--color-toc-item-text--hover:var(--color-foreground-primary);--color-toc-item-text--active:var(--color-brand-primary);--color-content-foreground:var(--color-foreground-primary);--color-content-background:transparent;--color-link:var(--color-brand-content);--color-link--hover:var(--color-brand-content);--color-link-underline:var(--color-background-border);--color-link-underline--hover:var(--color-foreground-border)}.only-light{display:block!important}html body .only-dark{display:none!important}@media not print{body[data-theme=dark]{--color-problematic:#ee5151;--color-foreground-primary:#ffffffcc;--color-foreground-secondary:#9ca0a5;--color-foreground-muted:#81868d;--color-foreground-border:#666;--color-background-primary:#131416;--color-background-secondary:#1a1c1e;--color-background-hover:#1e2124;--color-background-hover--transparent:#1e212400;--color-background-border:#303335;--color-background-item:#444;--color-announcement-background:#000000dd;--color-announcement-text:#eeebee;--color-brand-primary:#2b8cee;--color-brand-content:#368ce2;--color-highlighted-background:#083563;--color-guilabel-background:#08356380;--color-guilabel-border:#13395f80;--color-api-keyword:var(--color-foreground-secondary);--color-highlight-on-target:#330;--color-admonition-background:#18181a;--color-card-border:var(--color-background-secondary);--color-card-background:#18181a;--color-card-marginals-background:var(--color-background-hover)}html body[data-theme=dark] 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.only-dark{display:block!important}@media(prefers-color-scheme:dark){body:not([data-theme=light]){--color-problematic:#ee5151;--color-foreground-primary:#ffffffcc;--color-foreground-secondary:#9ca0a5;--color-foreground-muted:#81868d;--color-foreground-border:#666;--color-background-primary:#131416;--color-background-secondary:#1a1c1e;--color-background-hover:#1e2124;--color-background-hover--transparent:#1e212400;--color-background-border:#303335;--color-background-item:#444;--color-announcement-background:#000000dd;--color-announcement-text:#eeebee;--color-brand-primary:#2b8cee;--color-brand-content:#368ce2;--color-highlighted-background:#083563;--color-guilabel-background:#08356380;--color-guilabel-border:#13395f80;--color-api-keyword:var(--color-foreground-secondary);--color-highlight-on-target:#330;--color-admonition-background:#18181a;--color-card-border:var(--color-background-secondary);--color-card-background:#18181a;--color-card-marginals-background:var(--color-background-hover)}html 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto\n    display: block\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #646776; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2962ff;\n  --color-brand-content: #2a5adf;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #ffffffcc; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2b8cee;\n  --color-brand-content: #368ce2;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  vertical-align: middle\n\n.theme-toggle\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  vertical-align: middle\n  height: 1rem\n  width: 1rem\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  float: left\n  padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: 1rem\n    width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page svg\n    color: inherit\n    height: 1rem\n    width: 1rem\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n  .theme-toggle-header\n    display: block\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: 1.25rem\n    width: 1.25rem\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: var(--header-height)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Center the page, and accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n  .content\n    margin-left: auto\n    margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  .content\n    padding: 0 $content-padding--small\n    // Don't float sidebars to the right.\n  article aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 3.5rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\n.versionmodified\n  font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(0.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current >.reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  .reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling.Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n  text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n  border-style: none;\n  padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n  outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n  padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto-light\n    display: block\n\n  @media (prefers-color-scheme: dark)\n    .theme-toggle svg.theme-icon-when-auto-dark\n      display: block\n    .theme-toggle svg.theme-icon-when-auto-light\n      display: none\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n  --font-stack--headings: var(--font-stack);\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #6b6f76; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #0a4bff;\n  --color-brand-content: #2757dd;\n  --color-brand-visited: #872ee0;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n\n  --color-api-added: #21632c;\n  --color-api-added-border: #38a84d;\n  --color-api-changed: #046172;\n  --color-api-changed-border: #06a1bc;\n  --color-api-deprecated: #605706;\n  --color-api-deprecated-border: #f0d90f;\n  --color-api-removed: #b30000;\n  --color-api-removed-border: #ff5c5c;\n\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline--hover: var(--color-foreground-border);\n\n  --color-link--visited: var(--color-brand-visited);\n  --color-link-underline--visited: var(--color-background-border);\n  --color-link--visited--hover: var(--color-brand-visited);\n  --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #cfd0d0; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #3d94ff;\n  --color-brand-content: #5ca5ff;\n  --color-brand-visited: #b27aeb;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  --color-api-added: #3db854;\n  --color-api-added-border: #267334;\n  --color-api-changed: #09b0ce;\n  --color-api-changed-border: #056d80;\n  --color-api-deprecated: #b1a10b;\n  --color-api-deprecated-border: #6e6407;\n  --color-api-removed: #ff7575;\n  --color-api-removed-border: #b03b3b;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-family: var(--font-stack--headings)\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:visited\n    color: var(--color-link--visited)\n    text-decoration-color: var(--color-link-underline--visited)\n    &:hover\n      color: var(--color-link--visited--hover)\n      text-decoration-color: var(--color-link-underline--visited--hover)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link--hover)\n      text-decoration-color: var(--color-link-underline--hover)\n      &:visited\n        color: var(--color-link--visited--hover)\n        text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n.skip-to-content\n  position: fixed\n  padding: 1rem\n  border-radius: 1rem\n  left: 0.25rem\n  top: 0.25rem\n  z-index: 40\n  background: var(--color-background-primary)\n  color: var(--color-foreground-primary)\n\n  transform: translateY(-200%)\n  transition: transform 300ms ease-in-out\n\n  &:focus-within\n    transform: translateY(0%)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  vertical-align: middle\n\n.theme-toggle\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  vertical-align: middle\n  height: $icon-size\n  width: $icon-size\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  float: left\n  padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: $icon-size\n    width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page, .view-this-page\n    svg\n      color: inherit\n      height: $icon-size\n      width: $icon-size\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n  .theme-toggle-header\n    display: block\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: $icon-size\n    width: $icon-size\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Center the page, and accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n  .content\n    margin-left: auto\n    margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  .content\n    padding: 0 $content-padding--small\n    // Don't float sidebars to the right.\n  article aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 4.25rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n  border-left: 0.1875rem solid\n  border-radius: 0.125rem\n\n  padding-left: 0.75rem\n\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\ndiv.versionadded\n  border-color: var(--color-api-added-border)\n  .versionmodified\n    color: var(--color-api-added)\n\ndiv.versionchanged\n  border-color: var(--color-api-changed-border)\n  .versionmodified\n    color: var(--color-api-changed)\n\ndiv.deprecated\n  border-color: var(--color-api-deprecated-border)\n  .versionmodified\n    color: var(--color-api-deprecated)\n\ndiv.versionremoved\n  border-color: var(--color-api-removed-border)\n  .versionmodified\n    color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(2.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      color: var(--color-sidebar-link-text)\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current > .reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  a.reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling. Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/docs/documentation/api.html b/docs/documentation/api.html
index 41efe94a..dcfbbd60 100644
--- a/docs/documentation/api.html
+++ b/docs/documentation/api.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Frontend Developer Guide" href="frontend_development.html" /><link rel="prev" title="Components and Features" href="components.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>LIT Python API - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
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     <title>Light mode</title>
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     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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@@ -92,7 +92,7 @@
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     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
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 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
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           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/api.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
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 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,13 +263,13 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="lit-python-api">
-<h1>LIT Python API<a class="headerlink" href="#lit-python-api" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<h1>LIT Python API<a class="headerlink" href="#lit-python-api" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="design-overview">
-<h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to this heading">#</a></h2>
+<h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to this heading">¶</a></h2>
 <p>LIT is a modular system, comprising a collection of backend components (written
 in Python) and frontend modules (written in TypeScript). Most users will develop
 against the Python API, which is documented below and allows LIT to be extended
@@ -231,7 +294,7 @@ <h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to
 <a class="reference internal" href="frontend_development.html"><span class="std std-doc">frontend developer guide</span></a>.</p>
 </section>
 <section id="adding-models-and-data">
-<h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" title="Link to this heading">#</a></h2>
+<h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" title="Link to this heading">¶</a></h2>
 <p>To run LIT with your own models and data, you can create a custom <code class="docutils literal notranslate"><span class="pre">demo.py</span></code>
 script that passes these to the LIT server. For example:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">_</span><span class="p">):</span>
@@ -261,7 +324,7 @@ <h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" t
 <p>For pre-built <code class="docutils literal notranslate"><span class="pre">demo.py</span></code> examples, check out
 <a class="reference external" href="https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples">https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples</a></p>
 <section id="validating-models-and-data">
-<h3>Validating Models and Data<a class="headerlink" href="#validating-models-and-data" title="Link to this heading">#</a></h3>
+<h3>Validating Models and Data<a class="headerlink" href="#validating-models-and-data" title="Link to this heading">¶</a></h3>
 <p>Datasets and models can optionally be validated by LIT to ensure that dataset
 examples match their spec and that model output values match their spec.
 This can be very helpful during development of new model and dataset wrappers
@@ -279,7 +342,7 @@ <h3>Validating Models and Data<a class="headerlink" href="#validating-models-and
 </section>
 </section>
 <section id="datasets">
-<h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">#</a></h2>
+<h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">¶</a></h2>
 <p>Datasets (<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a>) are
 just a list of examples, with associated type information following LIT’s
 <a class="reference internal" href="#type-system">type system</a>.</p>
@@ -341,7 +404,7 @@ <h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">
 these features for slicing, binning, etc., and they will also be available to
 interpretation components such as custom metrics.</p>
 <section id="transformations">
-<h3>Transformations<a class="headerlink" href="#transformations" title="Link to this heading">#</a></h3>
+<h3>Transformations<a class="headerlink" href="#transformations" title="Link to this heading">¶</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class also supports a limited set of transformations, similar to
 TensorFlow’s
 <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset">tf.data.Dataset</a>
@@ -360,7 +423,7 @@ <h3>Transformations<a class="headerlink" href="#transformations" title="Link to
 </section>
 </section>
 <section id="models">
-<h2>Models<a class="headerlink" href="#models" title="Link to this heading">#</a></h2>
+<h2>Models<a class="headerlink" href="#models" title="Link to this heading">¶</a></h2>
 <p>Models (<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code></a>) are
 functions which take inputs and produce outputs, with associated type
 information following LIT’s <a class="reference internal" href="#type-system">type system</a>. The core API consists
@@ -422,7 +485,7 @@ <h2>Models<a class="headerlink" href="#models" title="Link to this heading">#</a
 <p>If your model is on a remote server, consider using the <code class="docutils literal notranslate"><span class="pre">BatchedRemoteModel</span></code>
 base class, which implements parallel batched requests using a thread pool.</p>
 <section id="adding-more-outputs">
-<h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="Link to this heading">#</a></h3>
+<h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="Link to this heading">¶</a></h3>
 <p>The above example defined a black-box model, with predictions but no access to
 internals. If we want a richer view into the model’s behavior, we can add
 additional return fields corresponding to hidden-state activations, gradients,
@@ -471,7 +534,7 @@ <h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="
 types and their semantics.</p>
 </section>
 <section id="optional-inputs">
-<h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to this heading">#</a></h3>
+<h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to this heading">¶</a></h3>
 <p>By default, LIT treats <code class="docutils literal notranslate"><span class="pre">input_spec</span></code> fields as required. However, this can be set
 to false if you wish to define optional model inputs. For example, a model that
 can accept pre-tokenized inputs might have the following spec:</p>
@@ -495,7 +558,7 @@ <h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to
 </section>
 </section>
 <section id="interpretation-components">
-<h2>Interpretation Components<a class="headerlink" href="#interpretation-components" title="Link to this heading">#</a></h2>
+<h2>Interpretation Components<a class="headerlink" href="#interpretation-components" title="Link to this heading">¶</a></h2>
 <p>Backend interpretation components include metrics, salience maps, visualization
 aids like <a class="reference external" href="https://umap-learn.readthedocs.io/en/latest/">UMAP</a>, and
 counterfactual generator plug-ins.</p>
@@ -509,7 +572,7 @@ <h2>Interpretation Components<a class="headerlink" href="#interpretation-compone
 additionally requires a <code class="docutils literal notranslate"><span class="pre">TokenEmbeddings</span></code> input and corresponding output, as
 well as a label field <code class="docutils literal notranslate"><span class="pre">Target</span></code> to pin the gradient target to the same class as
 an input and corresponding output. See the
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py">GLUE models class</a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">GLUE models class</a>
 for an example of these spec requirements.</p>
 <p>The core API involves implementing the <code class="docutils literal notranslate"><span class="pre">run()</span></code> method:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
@@ -568,7 +631,7 @@ <h2>Interpretation Components<a class="headerlink" href="#interpretation-compone
 benefit is flexibility - Python can be used to specify complex dependencies
 between fields, and multiple outputs can be easily supported in a loop.</p>
 <section id="metrics">
-<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
+<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶</a></h3>
 <p>For metrics, the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/metrics.py"><code class="docutils literal notranslate"><span class="pre">SimpleMetrics</span></code></a>
 class implements the spec-matching and input-unpacking logic to satisfy the
@@ -602,7 +665,7 @@ <h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#<
 on the unpacked values.</p>
 </section>
 <section id="generators">
-<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">#</a></h3>
+<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">¶</a></h3>
 <p>Conceptually, a generator is just an interpreter that returns new input
 examples. These may depend on the input only, as for techniques such as back-
 translation, or can involve feedback from the model, such as for adversarial
@@ -638,7 +701,7 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
 through <code class="docutils literal notranslate"><span class="pre">config</span></code>, such as the list of substitutions for the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py">word replacer</a>.</p>
 <section id="backtranslator-generator">
-<h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generator" title="Link to this heading">#</a></h4>
+<h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generator" title="Link to this heading">¶</a></h4>
 <p>The <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/backtranslator.py">backtranslator</a>
 generator translates text segment inputs into foreign languages and back to the
 source language in order to create paraphrases.
@@ -652,7 +715,7 @@ <h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generato
 </section>
 </section>
 <section id="configuration-ui">
-<h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link to this heading">#</a></h3>
+<h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link to this heading">¶</a></h3>
 <p>Interpreter components support an optional <code class="docutils literal notranslate"><span class="pre">config</span></code> option to specify run-time
 options, such as the number of samples for LIME or the pivot languages for
 back-translation. LIT provides a simple DSL to define these options, which will
@@ -708,7 +771,7 @@ <h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link t
 </section>
 </section>
 <section id="type-system">
-<h2>Type System<a class="headerlink" href="#type-system" title="Link to this heading">#</a></h2>
+<h2>Type System<a class="headerlink" href="#type-system" title="Link to this heading">¶</a></h2>
 <p>LIT passes data around (e.g., between the server and the web app) as flat
 records with <code class="docutils literal notranslate"><span class="pre">string</span></code> keys. In Python types these are <code class="docutils literal notranslate"><span class="pre">Mapping[str,</span> <span class="pre">...]</span></code> and in
 TypeScript types these are <code class="docutils literal notranslate"><span class="pre">{[key:</span> <span class="pre">string]:</span> <span class="pre">unknown}</span></code>. LIT serializes these
@@ -731,7 +794,7 @@ <h2>Type System<a class="headerlink" href="#type-system" title="Link to this hea
 inheritance; the canonical types can be found in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>, with
 parallel implementations in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts">lit_types.ts</a>.</p>
 <section id="conventions">
-<h3>Conventions<a class="headerlink" href="#conventions" title="Link to this heading">#</a></h3>
+<h3>Conventions<a class="headerlink" href="#conventions" title="Link to this heading">¶</a></h3>
 <p>LIT supports several different “kinds” of <code class="docutils literal notranslate"><span class="pre">Spec</span></code>s (input vs output vs meta,
 etc.), and their use in context has specific implications, described
 per base class below.</p>
@@ -747,7 +810,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 in the dataset loading section of the web app’s Global Settings.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.model.Model</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.model.Model</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.input_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the shape required of all records
 passed into the <code class="docutils literal notranslate"><span class="pre">Model.predict()</span></code> function via the <code class="docutils literal notranslate"><span class="pre">inputs</span></code> argument. LIT
@@ -762,7 +825,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 the model loading section of the web app’s Global Settings.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.[Interpreter</span> <span class="pre">|</span> <span class="pre">Generator]</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.[Interpreter</span> <span class="pre">|</span> <span class="pre">Generator]</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.config_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the user-configurable parameters
 for running this component. Returning an empty <code class="docutils literal notranslate"><span class="pre">Spec</span></code> means that this
@@ -778,7 +841,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 because each input may enable the generation of one or more new examples.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.Metrics</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.Metrics</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.config_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the user-configurable parameters
 for running this component. Returning an empty <code class="docutils literal notranslate"><span class="pre">Spec</span></code> means that this
@@ -818,7 +881,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 <li><p>A field that appears in <em>both</em> the model’s input and output specs is assumed
 to represent the same value. This pattern is used for model-based input
 manipulation. For example, a
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py">language model</a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">language model</a>
 might output <code class="docutils literal notranslate"><span class="pre">'tokens':</span> <span class="pre">lit_types.Tokens(...)</span></code>, and accept as (optional)
 input <code class="docutils literal notranslate"><span class="pre">'tokens':</span> <span class="pre">lit_types.Tokens(required=False,</span> <span class="pre">...)</span></code>. An interpretability
 component could take output from the former, swap one or more tokens (e.g.
@@ -827,17 +890,17 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 </ul>
 </section>
 <section id="compatibility-checks">
-<h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title="Link to this heading">#</a></h3>
+<h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title="Link to this heading">¶</a></h3>
 <p>LIT’s type system plays a critical role in ensuring reliability of and
 interoperability between the <code class="docutils literal notranslate"><span class="pre">Model</span></code>, <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code>, <code class="docutils literal notranslate"><span class="pre">Generator</span></code>, and
 <code class="docutils literal notranslate"><span class="pre">Metrics</span></code> classes:</p>
 <ul class="simple">
 <li><p>The <strong>Model-Dataset compatibility check</strong> ensures that the
 <code class="docutils literal notranslate"><span class="pre">Model.input_spec()</span></code> is a subset of the <code class="docutils literal notranslate"><span class="pre">Dataset.spec()</span></code>. The base
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code> class</a> provides a robust and universal implementation of
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code> class</a> provides a robust and universal implementation of
 this check in the <code class="docutils literal notranslate"><span class="pre">is_compatible_with_dataset()</span></code> API, but you can override
 this method in your <code class="docutils literal notranslate"><span class="pre">Model</span></code> subclass if you so choose.</p></li>
-<li><p>All <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components</span></code> classes</a> provide an
+<li><p>All <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components</span></code> classes</a> provide an
 <code class="docutils literal notranslate"><span class="pre">is_compatible</span></code> API to check their compatibility against <code class="docutils literal notranslate"><span class="pre">Model</span></code>s and
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s, as appropriate. For example, the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py"><code class="docutils literal notranslate"><span class="pre">WordReplacer</span></code> generator</a> only checks against the <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>
@@ -855,11 +918,9 @@ <h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title
 this can cause jitter (UI modules appearing, disappearing, reordering, resizing,
 etc.) when switching between models or datasets with heterogeneous <code class="docutils literal notranslate"><span class="pre">Spec</span></code>s.</p>
 <p>When implementing your own LIT components and modules, you can use
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys()</span></code></a>
-(Python) and
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">findSpecKeys()</span></code></a>
-(TypeScript) to identify fields of interest in a <code class="docutils literal notranslate"><span class="pre">Spec</span></code>. These methods recognize
-and respect subclasses. For example,
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py"><code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys()</span></code></a> (Python) and
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">findSpecKeys()</span></code></a> (TypeScript) to identify fields of interest in a
+<code class="docutils literal notranslate"><span class="pre">Spec</span></code>. These methods recognize and respect subclasses. For example,
 <code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys(spec,</span> <span class="pre">Scalar)</span></code> will also match any <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>
 fields, but <code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys(spec,</span> <span class="pre">RegressionScore)</span></code> will not return all
 <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> fields in the <code class="docutils literal notranslate"><span class="pre">Spec</span></code>.</p>
@@ -872,7 +933,7 @@ <h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title
 herrings.</p>
 </section>
 <section id="an-in-depth-example">
-<h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="Link to this heading">#</a></h3>
+<h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="Link to this heading">¶</a></h3>
 <p>Consider the following example from the <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">MNLI demo</a>. The
 <a class="reference external" href="https://cims.nyu.edu/~sbowman/multinli/">MultiNLI</a> dataset might define the following <code class="docutils literal notranslate"><span class="pre">Spec</span></code>.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Dataset.spec()</span>
@@ -938,9 +999,14 @@ <h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="
 <p><em>See the <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples">examples</a> for more.</em></p>
 </section>
 <section id="available-types">
-<h3>Available types<a class="headerlink" href="#available-types" title="Link to this heading">#</a></h3>
-<p>The full set of <code class="docutils literal notranslate"><span class="pre">LitType</span></code>s is defined in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>. Numeric types such as <code class="docutils literal notranslate"><span class="pre">Integer</span></code> and <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> have predefined ranges that can be overridden using corresponding <code class="docutils literal notranslate"><span class="pre">min_val</span></code> and <code class="docutils literal notranslate"><span class="pre">max_val</span></code> attributes as seen <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438">here</a>. The different types available in LIT are summarized
-in the table below.</p>
+<h3>Available types<a class="headerlink" href="#available-types" title="Link to this heading">¶</a></h3>
+<p>The full set of <code class="docutils literal notranslate"><span class="pre">LitType</span></code>s is defined in
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>. Numeric types
+such as <code class="docutils literal notranslate"><span class="pre">Integer</span></code> and <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> have predefined ranges that can be overridden
+using corresponding <code class="docutils literal notranslate"><span class="pre">min_val</span></code> and <code class="docutils literal notranslate"><span class="pre">max_val</span></code> attributes as seen in
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py">penguin data</a>
+<code class="docutils literal notranslate"><span class="pre">INPUT_SPEC</span></code>. The different types available in LIT are summarized in the table
+below.</p>
 <p>Note: Bracket syntax, such as <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[num_tokens]</span></code>, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.</p>
 <div class="table-wrapper colwidths-auto docutils container">
@@ -1084,11 +1150,11 @@ <h3>Available types<a class="headerlink" href="#available-types" title="Link to
 </section>
 </section>
 <section id="server-configuration">
-<h2>Server Configuration<a class="headerlink" href="#server-configuration" title="Link to this heading">#</a></h2>
+<h2>Server Configuration<a class="headerlink" href="#server-configuration" title="Link to this heading">¶</a></h2>
 <p>Some properties of the LIT frontend can be configured from Python as
 <strong>arguments to <code class="docutils literal notranslate"><span class="pre">dev_server.Server()</span></code></strong>. These include:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">page_title</span></code>: set a custom page title, such as “Coreference Demo”.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">page_title</span></code>: set a custom page title.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">canonical_url</span></code>: set a “canonical” URL (such as a shortlink) that will be
 used as the base when copying links from the LIT UI.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">default_layout</span></code>: set the default UI layout, by name. See <code class="docutils literal notranslate"><span class="pre">layout.ts</span></code> and
@@ -1108,25 +1174,19 @@ <h2>Server Configuration<a class="headerlink" href="#server-configuration" title
 a <code class="docutils literal notranslate"><span class="pre">description()</span></code> method which can be used to specify a human-readable
 description or help text that will appear in the UI.</p>
 <section id="customizing-the-layout">
-<h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" title="Link to this heading">#</a></h3>
+<h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" title="Link to this heading">¶</a></h3>
 <p>You can specify custom web app layouts from Python via the <code class="docutils literal notranslate"><span class="pre">layouts=</span></code> attribute.
 The value should be a <code class="docutils literal notranslate"><span class="pre">Mapping[str,</span> <span class="pre">LitCanonicalLayout]</span></code>, such as:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LM_LAYOUT</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">LitCanonicalLayout</span><span class="p">(</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">PENGUIN_LAYOUT</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">LitCanonicalLayout</span><span class="p">(</span>
     <span class="n">upper</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;Main&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="n">modules</span><span class="o">.</span><span class="n">EmbeddingsModule</span><span class="p">,</span>
+        <span class="s1">&#39;Main&#39;</span><span class="p">:</span> <span class="p">[</span>
+            <span class="n">modules</span><span class="o">.</span><span class="n">DiveModule</span><span class="p">,</span>
             <span class="n">modules</span><span class="o">.</span><span class="n">DataTableModule</span><span class="p">,</span>
             <span class="n">modules</span><span class="o">.</span><span class="n">DatapointEditorModule</span><span class="p">,</span>
         <span class="p">]</span>
     <span class="p">},</span>
-    <span class="n">lower</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;Predictions&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="n">modules</span><span class="o">.</span><span class="n">LanguageModelPredictionModule</span><span class="p">,</span>
-            <span class="n">modules</span><span class="o">.</span><span class="n">ConfusionMatrixModule</span><span class="p">,</span>
-        <span class="p">],</span>
-        <span class="s2">&quot;Counterfactuals&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">modules</span><span class="o">.</span><span class="n">GeneratorModule</span><span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Custom layout for language models.&quot;</span><span class="p">,</span>
+    <span class="n">lower</span><span class="o">=</span><span class="n">layout</span><span class="o">.</span><span class="n">STANDARD_LAYOUT</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s1">&#39;Custom layout for the Palmer Penguins demo.&#39;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -1135,13 +1195,12 @@ <h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" t
     <span class="n">models</span><span class="p">,</span>
     <span class="n">datasets</span><span class="p">,</span>
     <span class="c1"># other args...</span>
-    <span class="n">layouts</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lm&quot;</span><span class="p">:</span> <span class="n">LM_LAYOUT</span><span class="p">},</span>
+    <span class="n">layouts</span><span class="o">=</span><span class="n">layout</span><span class="o">.</span><span class="n">DEFAULT_LAYOUTS</span> <span class="o">|</span> <span class="p">{</span><span class="s1">&#39;penguins&#39;</span><span class="p">:</span> <span class="n">PENGUIN_LAYOUT</span><span class="p">},</span>
+    <span class="n">default_layout</span><span class="o">=</span><span class="s1">&#39;penguins&#39;</span><span class="p">,</span>
     <span class="o">**</span><span class="n">server_flags</span><span class="o">.</span><span class="n">get_flags</span><span class="p">())</span>
 <span class="k">return</span> <span class="n">lit_demo</span><span class="o">.</span><span class="n">serve</span><span class="p">()</span>
 </pre></div>
 </div>
-<p>For a full example, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
 <p>You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py"><code class="docutils literal notranslate"><span class="pre">layout.py</span></code></a>. A
@@ -1171,7 +1230,7 @@ <h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" t
 </section>
 </section>
 <section id="accessing-the-lit-ui-in-notebooks">
-<h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-lit-ui-in-notebooks" title="Link to this heading">#</a></h2>
+<h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-lit-ui-in-notebooks" title="Link to this heading">¶</a></h2>
 <p>As an alternative to running a LIT server and connecting to it through a web
 browser, LIT can be used directly inside of python notebook environments, such
 as <a class="reference external" href="https://colab.research.google.com/">Colab</a> and
@@ -1197,20 +1256,20 @@ <h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-
 <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb">example notebook</a>.</p>
 </section>
 <section id="using-lit-components-outside-of-lit">
-<h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-components-outside-of-lit" title="Link to this heading">#</a></h2>
+<h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-components-outside-of-lit" title="Link to this heading">¶</a></h2>
 <p>All LIT Python components (models, datasets, interpreters, metrics, generators,
 etc.) are standalone classes that do not depend on the serving framework. You
 can easily use them from Colab, in scripts, or in your libraries. This can
 also be handy for development, as you can test new models or components without
 needing to reload the server or click the UI.</p>
 <p>For example, to view examples in a dataset:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.datasets</span> <span class="kn">import</span> <span class="n">glue</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">glue</span><span class="o">.</span><span class="n">SST2Data</span><span class="p">(</span><span class="s1">&#39;validation&#39;</span><span class="p">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.glue</span> <span class="kn">import</span> <span class="n">data</span> <span class="k">as</span> <span class="n">glue_data</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">glue_data</span><span class="o">.</span><span class="n">SST2Data</span><span class="p">(</span><span class="s1">&#39;validation&#39;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">examples</span><span class="p">)</span>  <span class="c1"># list of records {&quot;sentence&quot;: ..., &quot;label&quot;: ...}</span>
 </pre></div>
 </div>
 <p>And to run inference on a few of them:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.models</span> <span class="kn">import</span> <span class="n">glue_models</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.glue</span> <span class="kn">import</span> <span class="n">models</span> <span class="k">as</span> <span class="n">glue_models</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">glue_models</span><span class="o">.</span><span class="n">SST2Model</span><span class="p">(</span><span class="s2">&quot;/path/to/model/files&quot;</span><span class="p">)</span>
 <span class="n">preds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">examples</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
@@ -1357,8 +1416,8 @@ <h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-co
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/components.html b/docs/documentation/components.html
index bfbf62a6..27f7401b 100644
--- a/docs/documentation/components.html
+++ b/docs/documentation/components.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="LIT Python API" href="api.html" /><link rel="prev" title="UI Guide" href="ui_guide.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Components and Features - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/components.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,13 +263,13 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="components-and-features">
-<h1>Components and Features<a class="headerlink" href="#components-and-features" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
+<h1>Components and Features<a class="headerlink" href="#components-and-features" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="framework-and-model-support">
-<h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-support" title="Link to this heading">#</a></h2>
+<h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-support" title="Link to this heading">¶</a></h2>
 <p>LIT is framework-agnostic and is compatible with any model that can be wrapped
 in a Python class for inference. In particular, we’ve tested with TF1.x, TF2,
 JAX, and PyTorch, as well as models that use custom C++ inference code (wrapped
@@ -219,7 +282,7 @@ <h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-
 <a class="reference internal" href="api.html#adding-models-and-data"><span class="std std-ref">model and dataset classes</span></a>, but we also have
 out-of-the-box support for a few modeling frameworks, described below.</p>
 <section id="huggingface-transformers">
-<h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformers" title="Link to this heading">#</a></h3>
+<h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformers" title="Link to this heading">¶</a></h3>
 <p>Many of the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/">open-source LIT examples</a> use
 HuggingFace Transformers via their TF2/Keras model classes. These give easy
@@ -230,7 +293,7 @@ <h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformer
 working on academic projects.</p>
 </section>
 <section id="tf1-x-estimator">
-<h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to this heading">#</a></h3>
+<h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to this heading">¶</a></h3>
 <p>LIT supports Estimator and other TF1.x models, but the model wrappers can be
 more involved due to the need to explicitly manage the graph and sessions. (In
 particular: <code class="docutils literal notranslate"><span class="pre">Estimator.predict()</span></code> cannot be used because it reloads the model on
@@ -242,13 +305,12 @@ <h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to
 directly.</p></li>
 </ul>
 <p>Alternatively, you can export to a <code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> and load this in an eager mode
-runtime. This leads to much simpler code (see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&amp;amp;rcl=378713625">this example</a>),
-but may require changes to your <code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> exporter in order to access model
-internals like embeddings, gradients, or attention.</p>
+runtime. This leads to much simpler code, but may require changes to your
+<code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> exporter in order to access model internals like embeddings,
+gradients, or attention.</p>
 </section>
 <section id="remote-or-hosted-models">
-<h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models" title="Link to this heading">#</a></h3>
+<h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models" title="Link to this heading">¶</a></h3>
 <p>LIT can easily interact with models hosted via an RPC or HTTP endpoint,
 including Servomatic. In this usage, the model weights and computation remain on
 the server, while the LIT <code class="docutils literal notranslate"><span class="pre">Model</span></code> implementation simply manages the RPC stub and
@@ -259,7 +321,7 @@ <h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models"
 </ul>
 </section>
 <section id="static-predictions">
-<h3>Static predictions<a class="headerlink" href="#static-predictions" title="Link to this heading">#</a></h3>
+<h3>Static predictions<a class="headerlink" href="#static-predictions" title="Link to this heading">¶</a></h3>
 <p>LIT works best when the model can be queried interactively, but this isn’t
 always possible for all situations. The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/static_preds.py"><code class="docutils literal notranslate"><span class="pre">StaticPredictions</span></code></a>
@@ -270,7 +332,7 @@ <h3>Static predictions<a class="headerlink" href="#static-predictions" title="Li
 as LIME which involve querying the model on new examples.</p>
 </section>
 <section id="data-loading">
-<h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this heading">#</a></h3>
+<h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this heading">¶</a></h3>
 <p>LIT can load data from almost any format, including TFRecord, Capacitor,
 SSTable, or even SQL queries, via a custom Python class that implements the
 <a class="reference internal" href="api.html#datasets"><span class="std std-ref">Dataset API</span></a>. Many of our demos use TFDS, and the LIT loader
@@ -283,7 +345,7 @@ <h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this h
 </section>
 </section>
 <section id="input-and-output-types">
-<h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" title="Link to this heading">#</a></h2>
+<h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" title="Link to this heading">¶</a></h2>
 <p>LIT uses an extensible system of semantic types to describe data and models.
 This allows for flexible support of a number of different input and output
 modalities common to NLP and other domains. For a full reference, see the
@@ -297,13 +359,11 @@ <h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" t
 output type, many LIT features - such as datapoint exploration, side-by-side
 functionality, and counterfactual generation - are available for any model.</p>
 <section id="classification">
-<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading">#</a></h3>
+<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading">¶</a></h3>
 <p>LIT supports many features for classification tasks, including common metrics,
 confusion matrices, and custom thresholding via the UI. Classification is
 implemented with the <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> and <code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code> types.</p>
 <ul class="simple">
-<li><p>For a basic example on a binary sentiment task, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py">examples/simple_tf2_demo.py</a>.</p></li>
 <li><p>Models should define a <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> field in their output spec with the
 <code class="docutils literal notranslate"><span class="pre">vocab=</span></code> attribute as the set of class labels, and for each example should
 return a vector of probabilities for each class.</p></li>
@@ -313,9 +373,7 @@ <h3>Classification<a class="headerlink" href="#classification" title="Link to th
 <li><p>A negative class can be designated using the <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> attribute of
 <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> (most commonly, <code class="docutils literal notranslate"><span class="pre">null_idx=0</span></code>), and metrics such as
 precision, recall, F1 will be computed for the remaining classes. AUC and
-AUCPR will be computed for binary classification tasks. For an example, see
-the
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&amp;amp;rcl=386779180">comment toxicity model</a>.</p></li>
+AUCPR will be computed for binary classification tasks.</p></li>
 <li><p>If <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> is set and there is only one other class, the other class
 (often, class <code class="docutils literal notranslate"><span class="pre">1</span></code>) is treated as a positive class, and the LIT UI can be
 used to change the classification threshold. If <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> is set and there
@@ -325,7 +383,7 @@ <h3>Classification<a class="headerlink" href="#classification" title="Link to th
 <p><a class="reference internal" href="_images/classification-results.png"><img alt="Classification Results Module" class="align-center" src="_images/classification-results.png" style="width: 600px;" /></a></p>
 </section>
 <section id="regression-scoring">
-<h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="Link to this heading">#</a></h3>
+<h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="Link to this heading">¶</a></h3>
 <p>Regression or scoring models also are well-supported with metrics, bucketed
 faceting, and scatterplots of scalar output. Regression is implemented with the
 <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> (input) and <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code> (output) types.</p>
@@ -336,17 +394,15 @@ <h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="
 which contains numerical targets, and the model’s <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code> field
 should set <code class="docutils literal notranslate"><span class="pre">parent=</span></code> to the name of this field.</p></li>
 <li><p>For an example, see the STS-B textual similarity task in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a>.</p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a>.</p></li>
 </ul>
 </section>
 <section id="multi-label-classification">
-<h3>Multi-label classification<a class="headerlink" href="#multi-label-classification" title="Link to this heading">#</a></h3>
+<h3>Multi-label classification<a class="headerlink" href="#multi-label-classification" title="Link to this heading">¶</a></h3>
 <p>LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 <code class="docutils literal notranslate"><span class="pre">SparseMultilabelPreds</span></code> and <code class="docutils literal notranslate"><span class="pre">SparseMultilabel</span></code> types.</p>
 <ul class="simple">
-<li><p>For a basic example on an image labeling task, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a>.</p></li>
 <li><p>Models should define a <code class="docutils literal notranslate"><span class="pre">SparseMultilabelPreds</span></code> field in their output spec
 with the<code class="docutils literal notranslate"><span class="pre">vocab=</span></code> attribute as the set of class labels, and for each example
 should return a list of class score tuples. Each tuple contains two
@@ -359,7 +415,7 @@ <h3>Multi-label classification<a class="headerlink" href="#multi-label-classific
 </ul>
 </section>
 <section id="seq2seq-generation">
-<h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="Link to this heading">#</a></h3>
+<h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="Link to this heading">¶</a></h3>
 <p>LIT has a number of features for sequence generation models, though support is
 not quite as mature as for classification or regression. In particular, LIT can
 display single generations as well as scored candidates from beam search, and
@@ -378,13 +434,11 @@ <h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="
 the model can also output a <code class="docutils literal notranslate"><span class="pre">ReferenceScores</span></code> field (with values as
 <code class="docutils literal notranslate"><span class="pre">list[float]</span></code>) with <code class="docutils literal notranslate"><span class="pre">parent=</span></code> set to reference a <code class="docutils literal notranslate"><span class="pre">TextSegment</span></code> or
 <code class="docutils literal notranslate"><span class="pre">ReferenceTexts</span></code> field from the input.</p></li>
-<li><p>For modeling examples, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py">examples/models/t5.py</a></p></li>
 </ul>
 <p><a class="reference internal" href="_images/generation-results.png"><img alt="Generated Text Module" class="align-center" src="_images/generation-results.png" style="width: 600px;" /></a></p>
 </section>
 <section id="span-labeling-and-structured-prediction">
-<h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-labeling-and-structured-prediction" title="Link to this heading">#</a></h3>
+<h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-labeling-and-structured-prediction" title="Link to this heading">¶</a></h3>
 <p>LIT can support a variety of structured prediction types, and provides rich,
 interactive visualizations.</p>
 <ul class="simple">
@@ -404,7 +458,7 @@ <h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-lab
 <p><a class="reference internal" href="_images/structured-preds.png"><img alt="Structured Predictions Module" class="align-center" src="_images/structured-preds.png" style="width: 400px;" /></a></p>
 </section>
 <section id="multiple-input-segments">
-<h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments" title="Link to this heading">#</a></h3>
+<h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments" title="Link to this heading">¶</a></h3>
 <p>LIT can easily handle multiple text fields, or a mix of text, categorical,
 scalar, and other input features. LIT does not explicitly “privilege” one input
 field, and metadata in the model spec can be used to align gradients, attention,
@@ -414,11 +468,11 @@ <h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments"
 <a class="reference internal" href="api.html#datasets"><span class="std std-ref">Dataset class documentation</span></a> and the corresponding
 <a class="reference internal" href="api.html#models"><span class="std std-ref">Model</span></a>.</p></li>
 <li><p>For a more involved code example including per-token gradients, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a>.</p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a>.</p></li>
 </ul>
 </section>
 <section id="tabular-data">
-<h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this heading">#</a></h3>
+<h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this heading">¶</a></h3>
 <p>LIT can be used as a replacement for the <a class="reference external" href="https://whatif-tool.dev">What-If Tool</a>
 but with more extensibility, when working with predictions over tabular data.</p>
 <p>Some interpreters, such as Kernel SHAP, require models that use tabular data. In
@@ -432,10 +486,10 @@ <h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this h
 (<code class="docutils literal notranslate"><span class="pre">SparseMultilabel</span></code>) field.</p></li>
 </ul>
 <p>For a demo using a penguin stats dataset/binary classification task, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">examples/penguin_demo.py</a>.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">examples/penguin/demo.py</a>.</p>
 </section>
 <section id="images">
-<h3>Images<a class="headerlink" href="#images" title="Link to this heading">#</a></h3>
+<h3>Images<a class="headerlink" href="#images" title="Link to this heading">¶</a></h3>
 <p>LIT also contains support for models with images as input features or generated
 images as model output. The LIT type <code class="docutils literal notranslate"><span class="pre">ImageBytes</span></code> can be used as a feature in
 datasets and as part of an input spec or output spec for a model. That feature’s
@@ -443,17 +497,10 @@ <h3>Images<a class="headerlink" href="#images" title="Link to this heading">#</a
 <p>NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don’t break existing checked-in code with
 such a change.</p>
-<ul class="simple">
-<li><p>See <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py">examples/datasets/open_images.py</a> for a
-dataset containing images, including converting images to base64 encoded
-strings.</p></li>
-<li><p>For a demo of an image classifier, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a>.</p></li>
-</ul>
 </section>
 </section>
 <section id="token-based-salience">
-<h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title="Link to this heading">#</a></h2>
+<h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title="Link to this heading">¶</a></h2>
 <p>LIT supports several methods for token-based input salience, including
 gradient-based methods as well as black-box techniques like LIME that don’t
 require any access to model internals. Output is rendered in the Salience Maps
@@ -465,7 +512,7 @@ <h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title
 regression (<code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) and generation (<code class="docutils literal notranslate"><span class="pre">GeneratedText</span></code> or
 <code class="docutils literal notranslate"><span class="pre">GeneratedTextCandidates</span></code>) outputs.</p>
 <section id="gradient-norm">
-<h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this heading">#</a></h3>
+<h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this heading">¶</a></h3>
 <p>This is a simple method, in which salience scores are proportional to the L2
 norm of the gradient, i.e. the score for token <span class="math notranslate nohighlight">\(i\)</span> is:</p>
 <div class="math-wrapper docutils container">
@@ -490,7 +537,7 @@ <h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this
 class.</p>
 </section>
 <section id="gradient-dot-input">
-<h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Link to this heading">#</a></h3>
+<h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Link to this heading">¶</a></h3>
 <p>In this method, salience scores are proportional to the dot product of the input
 embeddings and their gradients, i.e. for token <span class="math notranslate nohighlight">\(i\)</span> we compute:</p>
 <div class="math-wrapper docutils container">
@@ -499,8 +546,8 @@ <h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Li
 </div>
 <p>Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-<span class="math notranslate nohighlight">\(\hat{y}\)</span>, while a negative score suggests that the prediction would be stronger
-if that token was removed.</p>
+<span class="math notranslate nohighlight">\(\hat{y}\)</span>, while a negative score suggests that the prediction would be
+stronger if that token was removed.</p>
 <p>To enable this method, your model should, as part of the
 <a class="reference internal" href="api.html#models"><span class="std std-ref">output spec and <code class="docutils literal notranslate"><span class="pre">predict()</span></code> implementation</span></a>:</p>
 <ul class="simple">
@@ -520,7 +567,7 @@ <h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Li
 appropriate normalization.</p>
 </section>
 <section id="integrated-gradients">
-<h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title="Link to this heading">#</a></h3>
+<h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title="Link to this heading">¶</a></h3>
 <p>Integrated gradients is a more robust method for estimating feature
 contribution, based on integrating a gradients along a path in embedding space.
 See <a class="reference external" href="https://arxiv.org/abs/1703.01365">Sundararajan et al. 2017</a> for additional
@@ -559,11 +606,11 @@ <h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title
 </div>
 <p>For a more concrete example that also supports multiple segments with separate
 gradients, see our
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py">BERT classifier demo model</a>,
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">BERT classifier demo model</a>,
 or contact the LIT team for assistance.</p>
 </section>
 <section id="lime">
-<h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">#</a></h3>
+<h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">¶</a></h3>
 <p><a class="reference external" href="https://arxiv.org/abs/1602.04938">LIME</a> is a black-box salience method that
 does not require access to any model internals. It works by generating a set of
 perturbed inputs - generally, by dropping out or masking tokens - and training a
@@ -578,7 +625,7 @@ <h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">#</a></h
 regression/scoring (<code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) model.</p>
 </section>
 <section id="target-selection-on-classification-output">
-<h3>Target Selection on Classification Output<a class="headerlink" href="#target-selection-on-classification-output" title="Link to this heading">#</a></h3>
+<h3>Target Selection on Classification Output<a class="headerlink" href="#target-selection-on-classification-output" title="Link to this heading">¶</a></h3>
 <p>For all salience methods, we require that the class to explain is given as a
 label field in the input. For example, if the input example is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="s2">&quot;this movie was terrible!&quot;</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;0&quot;</span><span class="p">}</span>
@@ -615,7 +662,7 @@ <h3>Target Selection on Classification Output<a class="headerlink" href="#target
 </section>
 </section>
 <section id="sequence-salience">
-<h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">#</a></h2>
+<h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">¶</a></h2>
 <p>Sequence salience generalizes token-based salience to text-to-text models,
 allowing you to explain the impact of the prompt tokens on parts of the model
 output.</p>
@@ -625,7 +672,7 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 <p><a class="reference internal" href="_images/sequence-salience-2.png"><img alt="Sequence salience - visualization" class="align-center" src="_images/sequence-salience-2.png" style="width: 650px;" /></a></p>
 <p>The UI supports multiple options for analysis, including:</p>
 <ul class="simple">
-<li><p>Select from pre-defined target sequences, or explain generations from the
+<li><p>Select from predefined target sequences, or explain generations from the
 model.</p></li>
 <li><p>Different salience methods, including <a class="reference internal" href="#gradient-norm">Gradient Norm</a> and
 <a class="reference internal" href="#gradient-dot-input">Gradient-dot-Input</a>.</p></li>
@@ -633,7 +680,7 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 to words, sentences, lines, or paragraphs. Quickly switch between different
 views to refine your analysis to different parts of a prompt.</p></li>
 <li><p>Display density options to enable working with longer sequences, such as
-document text, few-shot eaxmples, or chain-of-thought prompts.</p></li>
+document text, few-shot examples, or chain-of-thought prompts.</p></li>
 </ul>
 <p>For a walkthrough of how to use sequence salience to debug LLMs, check out the
 Responsible Generative AI Toolkit at
@@ -655,15 +702,15 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 <li><p>LIT-for-Gemma Colab:
 <a class="reference external" href="https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb"><code class="docutils literal notranslate"><span class="pre">lit_gemma.ipynb</span></code></a></p></li>
 <li><p>Demo binary:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_salience_demo.py</span></code></a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py</a></p></li>
 <li><p>KerasNLP model wrappers:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py"><code class="docutils literal notranslate"><span class="pre">instrumented_keras_lms.py</span></code></a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py</a></p></li>
 <li><p>Transformers model wrappers:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py"><code class="docutils literal notranslate"><span class="pre">pretrained_lms.py</span></code></a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py</a></p></li>
 </ul>
 </section>
 <section id="salience-clustering">
-<h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="Link to this heading">#</a></h2>
+<h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="Link to this heading">¶</a></h2>
 <p>LIT includes a basic implementation of the salience clustering method from
 <a class="reference external" href="https://arxiv.org/abs/2211.05485">Ebert et al. 2022</a>, which uses k-means on a
 salience-weighted bag-of-words representation to find patterns in model
@@ -676,7 +723,7 @@ <h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="
 <a class="reference external" href="https://arxiv.org/pdf/2211.05485.pdf">the paper</a>.</p>
 </section>
 <section id="tabular-feature-attribution">
-<h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attribution" title="Link to this heading">#</a></h2>
+<h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attribution" title="Link to this heading">¶</a></h2>
 <p>Tabular feature attribution seeks to understand the importance of a column of
 data on a model’s predictions. LIT’s tabular feature attribution module supports
 this analysis using the <a class="reference external" href="https://github.com/slundberg/shap">SHAP interpreter</a>.
@@ -687,7 +734,7 @@ <h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attr
 <p><a class="reference internal" href="_images/tabular-feature-attribution.png"><img alt="Tabular feature attribution module module" class="align-center" src="_images/tabular-feature-attribution.png" style="width: 500px;" /></a></p>
 </section>
 <section id="pixel-based-salience">
-<h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title="Link to this heading">#</a></h2>
+<h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title="Link to this heading">¶</a></h2>
 <p>LIT also supports pixel-based salience methods, for models that take images as
 inputs. Output is rendered in the Salience Maps module in the LIT UI, which
 allows for comparison of multiple methods at once.</p>
@@ -707,9 +754,7 @@ <h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title
 the gradients with respect to each pixel in each color channel in the 2D
 input image.</p>
 <p>The model should be able to accept input images as numpy arrays in addition
-to accepting base64 URL encoded format. See
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py">mobilenet.py</a>
-for an example.</p>
+to accepting base64 URL encoded format.</p>
 </li>
 </ul>
 <p>A variety of image saliency techniques are implemented for models that return
@@ -720,33 +765,8 @@ <h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title
 <p>Each of these techniques returns a saliency map image as a base64-encoded string
 through the <code class="docutils literal notranslate"><span class="pre">ImageSalience</span></code> type.</p>
 </section>
-<section id="attention">
-<h2>Attention<a class="headerlink" href="#attention" title="Link to this heading">#</a></h2>
-<p>LIT can display a visualization of attention heads from transformers and other
-models:</p>
-<p><a class="reference internal" href="_images/attention.png"><img alt="Attention Visualization" class="align-center" src="_images/attention.png" style="width: 400px;" /></a></p>
-<p>To enable this, your model should return one or more fields of the type
-<code class="docutils literal notranslate"><span class="pre">AttentionHeads</span></code>, with values as arrays of shape <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[num_heads,</span> <span class="pre">num_tokens,</span> <span class="pre">num_tokens]</span></code>. Each field represents a set of heads at a single layer of the
-model, so models will often have more than one:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>   <span class="k">def</span> <span class="nf">output_spec</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">types</span><span class="o">.</span><span class="n">Spec</span><span class="p">:</span>
-     <span class="k">return</span> <span class="p">{</span>
-         <span class="c1"># ...</span>
-         <span class="s2">&quot;tokens&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">Tokens</span><span class="p">(</span><span class="n">parent</span><span class="o">=</span><span class="s2">&quot;input_text&quot;</span><span class="p">),</span>
-         <span class="s2">&quot;layer_0/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
-         <span class="s2">&quot;layer_1/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
-         <span class="s2">&quot;layer_2/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
-         <span class="c1"># ...</span>
-     <span class="p">}</span>
-</pre></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">align_in</span></code> and <code class="docutils literal notranslate"><span class="pre">align_out</span></code> attributes are the names of <code class="docutils literal notranslate"><span class="pre">Tokens</span></code> fields for
-the source and target tokens for that layer. For self-attention (such as in
-BERT) these would name the same field, but for encoder-decoder attention they
-could reference different token sets (such as “input_tokens” and
-“target_tokens”).</p>
-</section>
 <section id="embedding-projector">
-<h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">#</a></h2>
+<h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">¶</a></h2>
 <p>LIT includes a version of the
 <a class="reference external" href="https://projector.tensorflow.org/">embedding projector</a> which can be used to
 visualize the latent space of your model, in order to find clusters or patterns
@@ -762,16 +782,16 @@ <h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="
 <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[emb_dim]</span></code> for each example.</p>
 </section>
 <section id="aggregate-analysis">
-<h2>Aggregate Analysis<a class="headerlink" href="#aggregate-analysis" title="Link to this heading">#</a></h2>
+<h2>Aggregate Analysis<a class="headerlink" href="#aggregate-analysis" title="Link to this heading">¶</a></h2>
 <section id="metrics">
-<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
+<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶</a></h3>
 <p>LIT includes common metrics for classification, regression, and seq2seq (BLEU)
 by default, which will appear in the table when the appropriate types are
 present in the model output and input data. Metrics can be computed on the whole
 dataset, a selected subset, or on facets defined by particular features. For
 example, we could facet by class label:</p>
 <p><img alt="Metrics Table" src="_images/metrics-table.png" /></p>
-<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Performance” tab.</p>
+<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Metrics” tab.</p>
 <p>To enable metrics, your model should set the <code class="docutils literal notranslate"><span class="pre">parent</span></code> attribute on one or more
 output fields, pointing to the name of the input field that it should be
 evaluated against. For example, for classification, the data spec might have:</p>
@@ -796,19 +816,19 @@ <h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#<
 <a class="reference internal" href="api.html#metrics"><span class="std std-ref">API documentation</span></a> for more.</p>
 </section>
 <section id="confusion-matrix">
-<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">#</a></h3>
+<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">¶</a></h3>
 <p>LIT includes a powerful and flexible confusion matrix, which can be used to
 compare predictions to gold labels as well as to compare between two models or
 between different categorical features. You can click cells or row/column
 headers to select a subset of examples, which is useful for intersectional
 analysis.</p>
 <p><a class="reference internal" href="_images/confusion-matrix.png"><img alt="Confusion Matrix" class="align-center" src="_images/confusion-matrix.png" style="width: 600px;" /></a></p>
-<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Performance” tab.</p>
+<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Metrics” tab.</p>
 <p>The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (<code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code>).</p>
 </section>
 <section id="scalar-plots">
-<h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this heading">#</a></h3>
+<h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this heading">¶</a></h3>
 <p>LIT includes scatterplots for scalar features, including plain scalars (<code class="docutils literal notranslate"><span class="pre">Scalar</span></code>
 or <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) as well as per-class probabilities from classification
 output (<code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code>).</p>
@@ -820,7 +840,7 @@ <h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this h
 <p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Predictions” tab.</p>
 </section>
 <section id="binary-classification-thresholds">
-<h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classification-thresholds" title="Link to this heading">#</a></h3>
+<h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classification-thresholds" title="Link to this heading">¶</a></h3>
 <p>For binary classification models, LIT contains a module for setting
 classification thresholds, which determine at what score for the positive class
 the model determines that an example should be classified as belonging to the
@@ -856,7 +876,7 @@ <h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classifi
 <p><img alt="Binary Classifier Thresholds Module" src="_images/lit-thresholder.png" /></p>
 </section>
 <section id="partial-dependence-plots">
-<h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">#</a></h3>
+<h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">¶</a></h3>
 <p>For classification or regression models with <code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code> or <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> input
 features, the Partial Dependence Plots module shows plots indicating the effect
 that changing those individual features has on model output.</p>
@@ -874,9 +894,10 @@ <h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plot
 <p>If no datapoints are selected, then the calculations are done across all
 datapoints, giving a global view of feature effects.</p>
 <p><a class="reference internal" href="_images/lit-pdps.png"><img alt="Partial Dependence Plots Module" class="align-center" src="_images/lit-pdps.png" style="width: 400px;" /></a></p>
+<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/penguins.html">https://pair-code.github.io/lit/demos/penguins.html</a> and navigate to the “Predictions” tab.</p>
 </section>
 <section id="dive">
-<h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">#</a></h3>
+<h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">¶</a></h3>
 <p>Dive is a visualization module, inspired by our prior work on
 <a class="reference external" href="https://pair-code.github.io/facets/">Facets Dive</a> and its use in the
 <a class="reference external" href="https://pair-code.github.io/what-if-tool/">What-If Tool</a>, that enables
@@ -900,7 +921,7 @@ <h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">#</a></h
 </section>
 </section>
 <section id="tcav">
-<h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">#</a></h2>
+<h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">¶</a></h2>
 <p>Many interpretability methods provide importance values per input feature (e.g,
 token). By contrast, <a class="reference external" href="https://arxiv.org/abs/1711.11279">TCAV</a> shows the
 importance of high-level concepts (e.g., color, gender, race) for a prediction
@@ -918,7 +939,7 @@ <h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">#</a></h
 layer, the predicted class, and the corresponding gradient values for that layer
 and class.</p>
 <section id="example">
-<h3>Example<a class="headerlink" href="#example" title="Link to this heading">#</a></h3>
+<h3>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h3>
 <p>1.) To use TCAV, begin by creating one or more ‘concept’ slices.</p>
 <p>Every dataset/model is different, but for images, as low as 15 data points are
 shown to be sufficient. Start by adding at least 3 data points and add more as
@@ -944,7 +965,7 @@ <h3>Example<a class="headerlink" href="#example" title="Link to this heading">#<
 <p><a class="reference internal" href="_images/tcav-results-table.png"><img alt="TCAV2" class="align-center" src="_images/tcav-results-table.png" style="width: 800px;" /></a></p>
 </section>
 <section id="statistical-significance">
-<h3>Statistical Significance<a class="headerlink" href="#statistical-significance" title="Link to this heading">#</a></h3>
+<h3>Statistical Significance<a class="headerlink" href="#statistical-significance" title="Link to this heading">¶</a></h3>
 <p>One of the pitfalls with the TCAV method is the potential generating meaningless
 CAVs, since any randomly chosen set of images will still produce a CAV (even if
 it is not meaningful).</p>
@@ -968,13 +989,13 @@ <h3>Statistical Significance<a class="headerlink" href="#statistical-significanc
 statistical testing (which is indicated in the UI).</p>
 </section>
 <section id="sorting-by-cosine-similarity">
-<h3>Sorting by Cosine Similarity<a class="headerlink" href="#sorting-by-cosine-similarity" title="Link to this heading">#</a></h3>
+<h3>Sorting by Cosine Similarity<a class="headerlink" href="#sorting-by-cosine-similarity" title="Link to this heading">¶</a></h3>
 <p>The option to sort examples by cosine similarity to a CAV will be available in
 an upcoming release.</p>
 </section>
 </section>
 <section id="counterfactual-analysis">
-<h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis" title="Link to this heading">#</a></h2>
+<h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis" title="Link to this heading">¶</a></h2>
 <p>While aggregate metrics can give a picture of overall behavior, and salience
 maps can give quick insight into a model’s local behavior, many questions about
 model behavior are best answered in a counterfactual setting: “How does my model
@@ -985,7 +1006,7 @@ <h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis"
 explore this, both through manual edits and through automatic “generator”
 components.</p>
 <section id="manual-editing">
-<h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to this heading">#</a></h3>
+<h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to this heading">¶</a></h3>
 <p>Examples can be edited manually in the Datapoint Editor module:</p>
 <p><a class="reference internal" href="_images/manual-edit.png"><img alt="Manual Edit in the Datapoint Editor" class="align-center" src="_images/manual-edit.png" style="width: 400px;" /></a></p>
 <p>The “Add and Compare” button can be used to enter comparison mode, which will
@@ -999,7 +1020,7 @@ <h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to th
 <p><a class="reference internal" href="_images/pair-selection.png"><img alt="Pair Selection Controls" class="align-center" src="_images/pair-selection.png" style="width: 700px;" /></a></p>
 </section>
 <section id="generators">
-<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">#</a></h3>
+<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">¶</a></h3>
 <p>The <strong>Generator Module</strong> supports automatic generation of counterfactuals
 through a variety of plug-in components:</p>
 <p><img alt="Generator Module" src="_images/generator-module.png" /></p>
@@ -1122,7 +1143,6 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
 <li><a class="reference internal" href="#salience-clustering">Salience Clustering</a></li>
 <li><a class="reference internal" href="#tabular-feature-attribution">Tabular Feature Attribution</a></li>
 <li><a class="reference internal" href="#pixel-based-salience">Pixel-based Salience</a></li>
-<li><a class="reference internal" href="#attention">Attention</a></li>
 <li><a class="reference internal" href="#embedding-projector">Embedding Projector</a></li>
 <li><a class="reference internal" href="#aggregate-analysis">Aggregate Analysis</a><ul>
 <li><a class="reference internal" href="#metrics">Metrics</a></li>
@@ -1156,9 +1176,9 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     </body>
diff --git a/docs/documentation/demos.html b/docs/documentation/demos.html
index b1a613d1..3a4e4f48 100644
--- a/docs/documentation/demos.html
+++ b/docs/documentation/demos.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="UI Guide" href="ui_guide.html" /><link rel="prev" title="Getting Started with LIT" href="getting_started.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Demos - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/demos.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,10 +263,10 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="demos">
-<h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">#</a></h1>
-<!-- freshness: { owner: 'lit-dev' reviewed: '2023-08-29' } -->
+<h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">¶</a></h1>
+<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>The LIT team maintains a number of hosted demos, as well as pre-built launchers
 for some common tasks and model types.</p>
@@ -211,11 +274,11 @@ <h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">#</a><
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>.</p>
 <hr class="docutils" />
 <section id="classification">
-<h2>Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --><a class="headerlink" href="#classification" title="Link to this heading">#</a></h2>
+<h2>Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --><a class="headerlink" href="#classification" title="Link to this heading">¶</a></h2>
 <section id="sentiment-and-nli">
-<h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="headerlink" href="#sentiment-and-nli" title="Link to this heading">#</a></h3>
+<h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="headerlink" href="#sentiment-and-nli" title="Link to this heading">¶</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
 <ul class="simple">
 <li><p>Multi-task demo:</p>
 <ul>
@@ -238,7 +301,6 @@ <h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="heade
 the margin.</p></li>
 <li><p>Embedding projector to find clusters in representation space.</p></li>
 <li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
-<li><p>Attention visualization.</p></li>
 <li><p>Counterfactual generators, including HotFlip for targeted adversarial
 perturbations.</p></li>
 </ul>
@@ -247,28 +309,14 @@ <h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="heade
 <p>Tip: check out a case study for this demo on the public LIT website:
 <a class="reference external" href="https://pair-code.github.io/lit/tutorials/sentiment">https://pair-code.github.io/lit/tutorials/sentiment</a></p>
 </section>
-<section id="multilingual-xnli">
-<h3>Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} --><a class="headerlink" href="#multilingual-xnli" title="Link to this heading">#</a></h3>
-<p><strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py">examples/xnli_demo.py</a></p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://cims.nyu.edu/~sbowman/xnli/">XNLI</a> dataset translates a subset of
-MultiNLI into 14 different languages.</p></li>
-<li><p>Specify <code class="docutils literal notranslate"><span class="pre">--languages=en,jp,hi,...</span></code> flag to select which languages to load.</p></li>
-<li><p>NLI as a three-way classification task with two-segment input (premise,
-hypothesis).</p></li>
-<li><p>Fine-tuned multilingual BERT model.</p></li>
-<li><p>Salience methods work with non-whitespace-delimited text, by using the
-model’s wordpiece tokenization.</p></li>
-</ul>
-</section>
 </section>
 <hr class="docutils" />
 <section id="regression-scoring">
-<h2>Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --><a class="headerlink" href="#regression-scoring" title="Link to this heading">#</a></h2>
+<h2>Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --><a class="headerlink" href="#regression-scoring" title="Link to this heading">¶</a></h2>
 <section id="textual-similarity-sts-b">
-<h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a class="headerlink" href="#textual-similarity-sts-b" title="Link to this heading">#</a></h3>
+<h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a class="headerlink" href="#textual-similarity-sts-b" title="Link to this heading">¶</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;amp;dataset=stsb_dev">https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;dataset=stsb_dev</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
 <ul class="simple">
 <li><p>STS-B textual similarity task, predicting scores on a range from 0
 (unrelated) to 5 (very similar).</p></li>
@@ -280,7 +328,6 @@ <h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a cla
 quick filtering.</p></li>
 <li><p>Embedding projector to find clusters in representation space.</p></li>
 <li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
-<li><p>Attention visualization.</p></li>
 </ul>
 </li>
 </ul>
@@ -288,91 +335,31 @@ <h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a cla
 </section>
 <hr class="docutils" />
 <section id="sequence-to-sequence">
-<h2>Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --><a class="headerlink" href="#sequence-to-sequence" title="Link to this heading">#</a></h2>
+<h2>Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --><a class="headerlink" href="#sequence-to-sequence" title="Link to this heading">¶</a></h2>
 <section id="gemma">
-<h3>Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --><a class="headerlink" href="#gemma" title="Link to this heading">#</a></h3>
-<p><strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py">examples/lm_salience_demo.py</a></p>
+<h3>Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --><a class="headerlink" href="#gemma" title="Link to this heading">¶</a></h3>
+<p><strong>Code:</strong>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py">examples/prompt_debugging/server.py</a></p>
 <ul class="simple">
-<li><p>Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.</p></li>
+<li><p>Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
+and Transformers (with PyTorch).</p></li>
 <li><p>Interactively debug LLM prompts using
 <a class="reference internal" href="components.html#sequence-salience"><span class="std std-ref">sequence salience</span></a>.</p></li>
 <li><p>Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-granularities: token-, word-, sentence-, and paragraph-level.</p></li>
+granularities: token-, word-, line-, sentence-, and paragraph-level.</p></li>
 </ul>
 <p>Tip: check out the in-depth walkthrough at
 <a class="reference external" href="https://ai.google.dev/responsible/model_behavior">https://ai.google.dev/responsible/model_behavior</a>, part of the Responsible
 Generative AI Toolkit.</p>
 </section>
-<section id="t5">
-<h3>T5 <!-- DO NOT REMOVE {#t5 .demo-header} --><a class="headerlink" href="#t5" title="Link to this heading">#</a></h3>
-<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/t5.html">https://pair-code.github.io/lit/demos/t5.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py">examples/t5_demo.py</a></p>
-<ul class="simple">
-<li><p>Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
-formats.</p></li>
-<li><p>Visualize beam candidates and highlight diffs against references.</p></li>
-<li><p>Visualize per-token decoder hypotheses to see where the model veers away
-from desired output.</p></li>
-<li><p>Filter examples by ROUGE score against reference.</p></li>
-<li><p>Embeddings from last layer of model, visualized with UMAP or PCA.</p></li>
-<li><p>Task wrappers to handle pre- and post-processing for summarization and
-machine translation tasks.</p></li>
-<li><p>Pre-loaded eval sets for CNNDM and WMT.</p></li>
-</ul>
-<p>Tip: check out a case study for this demo on the public LIT website:
-<a class="reference external" href="https://pair-code.github.io/lit/tutorials/generation">https://pair-code.github.io/lit/tutorials/generation</a></p>
-</section>
-</section>
-<hr class="docutils" />
-<section id="language-modeling">
-<h2>Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --><a class="headerlink" href="#language-modeling" title="Link to this heading">#</a></h2>
-<section id="bert-and-gpt-2">
-<h3>BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} --><a class="headerlink" href="#bert-and-gpt-2" title="Link to this heading">#</a></h3>
-<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/lm.html">https://pair-code.github.io/lit/demos/lm.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py">examples/lm_demo.py</a></p>
-<ul class="simple">
-<li><p>Compare multiple BERT and GPT-2 models side-by-side on a variety of
-plain-text corpora.</p></li>
-<li><p>LM visualization supports different modes:</p>
-<ul>
-<li><p>BERT masked language model: click-to-mask, and query model at that
-position.</p></li>
-<li><p>GPT-2 shows left-to-right hypotheses for each target token.</p></li>
-</ul>
-</li>
-<li><p>Embedding projector to show latent space of the model.</p></li>
-</ul>
-</section>
-</section>
-<hr class="docutils" />
-<section id="structured-prediction">
-<h2>Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --><a class="headerlink" href="#structured-prediction" title="Link to this heading">#</a></h2>
-<section id="gender-bias-in-coreference">
-<h3>Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} --><a class="headerlink" href="#gender-bias-in-coreference" title="Link to this heading">#</a></h3>
-<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/coref.html">https://pair-code.github.io/lit/demos/coref.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py">examples/coref/coref_demo.py</a></p>
-<ul class="simple">
-<li><p>Gold-mention coreference model, trained on
-<a class="reference external" href="https://catalog.ldc.upenn.edu/LDC2013T19">OntoNotes</a>.</p></li>
-<li><p>Evaluate on the Winogender schemas
-(<a class="reference external" href="https://arxiv.org/abs/1804.09301">Rudinger et al. 2018</a>) which test for
-gendered associations with profession names.</p></li>
-<li><p>Visualizations of coreference edges, as well as binary classification
-between two candidate referents.</p></li>
-<li><p>Stratified metrics for quantifying model bias as a function of pronoun
-gender or Bureau of Labor Statistics profession data.</p></li>
-</ul>
-<p>Tip: check out a case study for this demo on the public LIT website:
-<a class="reference external" href="https://pair-code.github.io/lit/tutorials/coref">https://pair-code.github.io/lit/tutorials/coref</a></p>
-</section>
 </section>
 <hr class="docutils" />
 <section id="multimodal">
-<h2>Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --><a class="headerlink" href="#multimodal" title="Link to this heading">#</a></h2>
+<h2>Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --><a class="headerlink" href="#multimodal" title="Link to this heading">¶</a></h2>
 <section id="tabular-data-penguin-classification">
-<h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --><a class="headerlink" href="#tabular-data-penguin-classification" title="Link to this heading">#</a></h3>
+<h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --><a class="headerlink" href="#tabular-data-penguin-classification" title="Link to this heading">¶</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/penguins.html">https://pair-code.github.io/lit/demos/penguins.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">examples/penguin_demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">examples/penguin/demo.py</a></p>
 <ul class="simple">
 <li><p>Binary classification on
 <a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">penguin dataset</a>.</p></li>
@@ -384,17 +371,6 @@ <h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-head
 parity.</p></li>
 </ul>
 </section>
-<section id="image-classification-with-mobilenet">
-<h3>Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} --><a class="headerlink" href="#image-classification-with-mobilenet" title="Link to this heading">#</a></h3>
-<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/images.html">https://pair-code.github.io/lit/demos/images.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a></p>
-<ul class="simple">
-<li><p>Classification on ImageNet labels using a MobileNet model.</p></li>
-<li><p>Showing using of LIT on image data.</p></li>
-<li><p>Explore results of multiple gradient-based image saliency techniques in the
-Salience Maps module.</p></li>
-</ul>
-</section>
 </section>
 </section>
 
@@ -456,7 +432,6 @@ <h3>Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-hea
 <li><a class="reference internal" href="#">Demos</a><ul>
 <li><a class="reference internal" href="#classification">Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#sentiment-and-nli">Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --></a></li>
-<li><a class="reference internal" href="#multilingual-xnli">Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} --></a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#regression-scoring">Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --></a><ul>
@@ -465,20 +440,10 @@ <h3>Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-hea
 </li>
 <li><a class="reference internal" href="#sequence-to-sequence">Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#gemma">Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --></a></li>
-<li><a class="reference internal" href="#t5">T5 <!-- DO NOT REMOVE {#t5 .demo-header} --></a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#language-modeling">Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --></a><ul>
-<li><a class="reference internal" href="#bert-and-gpt-2">BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} --></a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#structured-prediction">Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --></a><ul>
-<li><a class="reference internal" href="#gender-bias-in-coreference">Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} --></a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#multimodal">Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#tabular-data-penguin-classification">Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --></a></li>
-<li><a class="reference internal" href="#image-classification-with-mobilenet">Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} --></a></li>
 </ul>
 </li>
 </ul>
@@ -493,8 +458,8 @@ <h3>Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-hea
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   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
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diff --git a/docs/documentation/docker.html b/docs/documentation/docker.html
index 0671a8d4..54ae0324 100644
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+++ b/docs/documentation/docker.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Glossary" href="glossary.html" /><link rel="prev" title="Frontend Developer Guide" href="frontend_development.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Running LIT in a Docker container - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
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-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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       <path d="M13 6h1" />
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+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
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+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
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         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
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         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/docker.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
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@@ -200,34 +263,33 @@
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           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="running-lit-in-a-docker-container">
-<h1>Running LIT in a Docker container<a class="headerlink" href="#running-lit-in-a-docker-container" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-07-10' } *-->
+<h1>Running LIT in a Docker container<a class="headerlink" href="#running-lit-in-a-docker-container" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-04' } *-->
 <p>Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>. This doc describes the basic usage of
 LIT’s built-in demos, how to integrate your custom demo into this</p>
 <section id="basic-usage">
-<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this heading">#</a></h2>
+<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this heading">¶</a></h2>
 <p>LIT can be run as a containerized app using <a class="reference external" href="https://www.docker.com/">Docker</a> or
 your preferred engine. This is how we run our
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">hosted demos</a>.</p>
-<p>We provide a basic
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile"><code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code></a> that you can
-use to build and run any of the demos in the <code class="docutils literal notranslate"><span class="pre">lit_nlp/examples</span></code> directory. The
-<code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code> installs all necessary dependencies for LIT and builds the
+<p>We provide a basic Dockerfile <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/Dockerfile">https://github.com/PAIR-code/lit/blob/main/Dockerfile</a> that you can use to build and run any of the demos in the <code class="docutils literal notranslate"><span class="pre">lit_nlp/examples</span></code> directory.
+The <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code> installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs <a class="reference external" href="https://gunicorn.org/">gunicorn</a> as
 the HTTP server, invoking the <code class="docutils literal notranslate"><span class="pre">get_wsgi_app()</span></code> method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py"><code class="docutils literal notranslate"><span class="pre">gunicorn_config.py</span></code></a>.
 You can find a reference implementation in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code class="docutils literal notranslate"><span class="pre">glue_demo.py</span></code></a> or
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
-<p>Use the following shell commands to build the default Docker image for LIT from
-the provided <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code>, and then run a container from that image. Comments
-are provided in-line to help explain what each step does.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code class="docutils literal notranslate"><span class="pre">glue/demo.py</span></code></a>.</p>
+<p>Use the following shell
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml">https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml</a> commands to build the
+default Docker image for LIT from the provided <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code>, and then run a
+container from that image. Comments are provided in-line to help explain what
+each step does.</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build the docker image using the -t argument to name the image. Remember to</span>
 <span class="c1"># include the trailing . so Docker knows where to look for the Dockerfile.</span>
 docker<span class="w"> </span>build<span class="w"> </span>--file<span class="w"> </span>Dockerfile<span class="w"> </span>--tag<span class="w"> </span>lit-nlp<span class="w"> </span>.
@@ -243,14 +305,11 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this hea
 below.</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># DEMO_NAME is used to complete the Python module path</span>
 <span class="c1">#</span>
-<span class="c1">#     &quot;lit_nlp.examples.$DEMO_NAME&quot;</span>
+<span class="c1">#     &quot;lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()&quot;</span>
 <span class="c1">#</span>
 <span class="c1"># Therefore, valid values for DEMO_NAME are Python module paths in the</span>
-<span class="c1"># lit_nlp/examples directory, such as</span>
-<span class="c1">#</span>
-<span class="c1">#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.</span>
-<span class="c1">#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.</span>
-docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>lm_demo<span class="w"> </span>lit-nlp
+<span class="c1"># lit_nlp/examples directory, such as glue, penguin, tydi, etc.</span>
+docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>penguin<span class="w"> </span>lit-nlp
 
 <span class="c1"># Use the DEMO_PORT environment variable as to change the port that LIT uses in</span>
 <span class="c1"># the container. Be sure to also change the -p option to map the container&#39;s</span>
@@ -261,16 +320,16 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this hea
 <span class="c1"># containers on your machine using the combination of the DEMO_NAME and</span>
 <span class="c1"># DEMO_PORT arguments, and docker run with the -d flag to run the container in</span>
 <span class="c1"># the background.</span>
-docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>t5_demo<span class="w"> </span>lit-nlp
-docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">2345</span>:2345<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>lm_demo<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_PORT</span><span class="o">=</span><span class="m">2345</span><span class="w"> </span>lit-nlp
+docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>penguin<span class="w"> </span>lit-nlp
+docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">2345</span>:2345<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>tydi<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_PORT</span><span class="o">=</span><span class="m">2345</span><span class="w"> </span>lit-nlp
 </pre></div>
 </div>
 </section>
 <section id="integrating-custom-lit-instances-with-the-default-docker-image">
-<h2>Integrating Custom LIT Instances with the Default Docker Image<a class="headerlink" href="#integrating-custom-lit-instances-with-the-default-docker-image" title="Link to this heading">#</a></h2>
+<h2>Integrating Custom LIT Instances with the Default Docker Image<a class="headerlink" href="#integrating-custom-lit-instances-with-the-default-docker-image" title="Link to this heading">¶</a></h2>
 <p>Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a <code class="docutils literal notranslate"><span class="pre">main()</span></code> method, as
-described in the <a class="reference external" href="https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data">Python API docs</a>.</p>
+described in the <a class="reference internal" href="api.html#adding-models-and-data"><span class="std std-ref">Python API docs</span></a>.</p>
 <p>These custom server scripts can be easily integrated with LIT’s default image as
 long as your server meets two requirements:</p>
 <ol class="arabic simple">
@@ -333,7 +392,7 @@ <h2>Integrating Custom LIT Instances with the Default Docker Image<a class="head
 </div>
 </section>
 <section id="building-your-own-image">
-<h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image" title="Link to this heading">#</a></h2>
+<h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image" title="Link to this heading">¶</a></h2>
 <p>Coming soon.</p>
 </section>
 </section>
@@ -409,8 +468,8 @@ <h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image"
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/faq.html b/docs/documentation/faq.html
index 18038110..32bd2624 100644
--- a/docs/documentation/faq.html
+++ b/docs/documentation/faq.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="prev" title="Glossary" href="glossary.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Frequently Asked Questions - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/faq.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,25 +263,23 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="frequently-asked-questions">
-<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-19' } *-->
+<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>Looking for help? Submit bugs, ask questions, suggest content, and request
 features on our
 <a class="reference external" href="https://github.com/pair-code/lit/issues/">Github issues list</a>.</p>
 <section id="model-and-data-types">
-<h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title="Link to this heading">#</a></h2>
+<h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title="Link to this heading">¶</a></h2>
 <p>LIT can handle a variety of models with different input and output types, and
 works with any modern ML framework. For more information, see
 <a class="reference internal" href="components.html#framework-and-model-support"><span class="std std-ref">Framework &amp; Model Support</span></a>.</p>
 <p>In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:</p>
 <ul class="simple">
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">Image demo</a> -
-image classification, using a Mobilenet model.</p></li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">Tabular demo</a> -
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">Tabular demo</a> -
 multi-class classification on tabular (numeric and categorical string) data,
 using the
 <a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">Palmer Penguins</a>
@@ -228,22 +289,15 @@ <h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title
 <a class="reference internal" href="api.html#type-system"><span class="std std-ref">the features guide to input and output types</span></a>.</p>
 </section>
 <section id="languages">
-<h2>Languages<a class="headerlink" href="#languages" title="Link to this heading">#</a></h2>
+<h2>Languages<a class="headerlink" href="#languages" title="Link to this heading">¶</a></h2>
 <p>All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications. For examples, see:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py">XNLI demo</a> -
-cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
-model.</p></li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py">T5 demo</a> -
-includes WMT data for machine translation</p></li>
-</ul>
+scripts should work without any modifications.</p>
 </section>
 <section id="scale">
-<h2>Scale<a class="headerlink" href="#scale" title="Link to this heading">#</a></h2>
+<h2>Scale<a class="headerlink" href="#scale" title="Link to this heading">¶</a></h2>
 <section id="dataset-size">
-<h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this heading">#</a></h3>
+<h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this heading">¶</a></h3>
 <p>LIT can comfortably handle 10k-100k datapoints, depending on the speed of the
 server (for hosting the model) and your local machine (for viewing the UI). When
 working with large datasets, there are a couple caveats:</p>
@@ -269,7 +323,7 @@ <h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this h
 data files at runtime using the settings (⚙️) menu.</p>
 </section>
 <section id="large-models">
-<h3>Large Models<a class="headerlink" href="#large-models" title="Link to this heading">#</a></h3>
+<h3>Large Models<a class="headerlink" href="#large-models" title="Link to this heading">¶</a></h3>
 <p>LIT can work with large or slow models, as long as you can wrap them into the
 model API. If you have more than a few preloaded datapoints, however, you’ll
 probably want to use <code class="docutils literal notranslate"><span class="pre">warm_start=1</span></code> (or pass <code class="docutils literal notranslate"><span class="pre">--warm_start=1</span></code> as a flag) to
@@ -288,17 +342,17 @@ <h3>Large Models<a class="headerlink" href="#large-models" title="Link to this h
 </section>
 </section>
 <section id="privacy-and-security">
-<h2>Privacy and Security<a class="headerlink" href="#privacy-and-security" title="Link to this heading">#</a></h2>
+<h2>Privacy and Security<a class="headerlink" href="#privacy-and-security" title="Link to this heading">¶</a></h2>
 <p>LIT allows users to query the model, as well as to view the loaded evaluation
 data. The LIT UI state is ephemeral and exists only in the browser window;
 however, model predictions and any newly-generated examples (including as
 manually entered in the web UI) are stored in server memory, and if <code class="docutils literal notranslate"><span class="pre">--data_dir</span></code>
 is specified, may be cached to disk.</p>
 <p>LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to
-disk, then pass the <code class="docutils literal notranslate"><span class="pre">--demo_mode</span></code> runtime flag to the LIT server.</p>
+disk. If you do not want the tool to be able to write edited datapoints to disk,
+then pass the <code class="docutils literal notranslate"><span class="pre">--demo_mode</span></code> runtime flag to the LIT server.</p>
 <section id="i-have-proprietary-data-is-lit-secure-for-my-team-to-use">
-<h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerlink" href="#i-have-proprietary-data-is-lit-secure-for-my-team-to-use" title="Link to this heading">#</a></h3>
+<h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerlink" href="#i-have-proprietary-data-is-lit-secure-for-my-team-to-use" title="Link to this heading">¶</a></h3>
 <p>We don’t store, collect or share datasets, models or any other information
 loaded into LIT. When you run a LIT server, anyone with access to the web
 address of the server will be able to see data from the loaded datasets and
@@ -312,9 +366,9 @@ <h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerli
 </section>
 </section>
 <section id="workflow-and-integrations">
-<h2>Workflow and Integrations<a class="headerlink" href="#workflow-and-integrations" title="Link to this heading">#</a></h2>
+<h2>Workflow and Integrations<a class="headerlink" href="#workflow-and-integrations" title="Link to this heading">¶</a></h2>
 <section id="sending-examples-from-another-tool">
-<h3>Sending examples from another tool<a class="headerlink" href="#sending-examples-from-another-tool" title="Link to this heading">#</a></h3>
+<h3>Sending examples from another tool<a class="headerlink" href="#sending-examples-from-another-tool" title="Link to this heading">¶</a></h3>
 <p>LIT can read input fields directly from the URL; they should be encoded as
 <code class="docutils literal notranslate"><span class="pre">data_&lt;fieldname&gt;=&lt;value&gt;</span></code>, and field names should match those in the (default)
 dataset.</p>
@@ -323,7 +377,7 @@ <h3>Sending examples from another tool<a class="headerlink" href="#sending-examp
 but using <code class="docutils literal notranslate"><span class="pre">data0</span></code>, <code class="docutils literal notranslate"><span class="pre">data1</span></code>, <code class="docutils literal notranslate"><span class="pre">data2</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">data0_&lt;fieldname&gt;=&lt;value&gt;</span></code>.</p>
 </section>
 <section id="downloading-or-exporting-data">
-<h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exporting-data" title="Link to this heading">#</a></h3>
+<h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exporting-data" title="Link to this heading">¶</a></h3>
 <p>Currently, there are three ways to export data from the LIT UI:</p>
 <ul class="simple">
 <li><p>In the Data Table, you can copy or download the current view in CSV format -
@@ -337,7 +391,7 @@ <h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exp
 </ul>
 </section>
 <section id="loading-data-from-the-ui">
-<h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-ui" title="Link to this heading">#</a></h3>
+<h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-ui" title="Link to this heading">¶</a></h3>
 <p>There is limited support for this via the settings (⚙️) menu. Select a dataset,
 and enter a path to load from:</p>
 <p><img alt="Load data from the UI" src="_images/lit-load-data.png" /></p>
@@ -346,7 +400,7 @@ <h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-u
 appropriate format.</p>
 </section>
 <section id="using-components-outside-the-lit-ui">
-<h3>Using components outside the LIT UI<a class="headerlink" href="#using-components-outside-the-lit-ui" title="Link to this heading">#</a></h3>
+<h3>Using components outside the LIT UI<a class="headerlink" href="#using-components-outside-the-lit-ui" title="Link to this heading">¶</a></h3>
 <p>Python components such as models, datasets, and generators are designed to
 support standalone use. These don’t depend on the LIT serving framework, and you
 can treat them as any other Python class and use from Colab, regular scripts,
@@ -362,15 +416,13 @@ <h3>Using components outside the LIT UI<a class="headerlink" href="#using-compon
 then be re-used in other environments.</p>
 </section>
 <section id="training-models-with-lit">
-<h3>Training models with LIT<a class="headerlink" href="#training-models-with-lit" title="Link to this heading">#</a></h3>
+<h3>Training models with LIT<a class="headerlink" href="#training-models-with-lit" title="Link to this heading">¶</a></h3>
 <p>LIT is primarily an evaluation/inference-time tool, so we don’t provide any
 official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT’s <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> objects to manage training data along with standard
 training APIs (such as Keras’ <code class="docutils literal notranslate"><span class="pre">model.fit()</span></code>). See
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py"><code class="docutils literal notranslate"><span class="pre">quickstart_sst_demo.py</span></code></a>
-and/or
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py"><code class="docutils literal notranslate"><span class="pre">glue_models.py</span></code></a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py"><code class="docutils literal notranslate"><span class="pre">glue/models.py</span></code></a>
 for examples.</p>
 </section>
 </section>
@@ -455,8 +507,8 @@ <h3>Training models with LIT<a class="headerlink" href="#training-models-with-li
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/frontend_development.html b/docs/documentation/frontend_development.html
index f3b9bbea..de68d9af 100644
--- a/docs/documentation/frontend_development.html
+++ b/docs/documentation/frontend_development.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Running LIT in a Docker container" href="docker.html" /><link rel="prev" title="LIT Python API" href="api.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Frontend Developer Guide - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/frontend_development.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,15 +263,15 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="frontend-developer-guide">
-<h1>Frontend Developer Guide<a class="headerlink" href="#frontend-developer-guide" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<h1>Frontend Developer Guide<a class="headerlink" href="#frontend-developer-guide" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>This document aims to describe the current LIT frontend system, including
 conventions, best practices, and gotchas.</p>
 <section id="high-level-overview">
-<h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="Link to this heading">#</a></h2>
+<h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="Link to this heading">¶</a></h2>
 <p>LIT is powered by two central pieces of tech -
 <a class="reference external" href="https://lit-element.polymer-project.org/">lit-element</a> for components and HTML
 rendering, and <a class="reference external" href="https://mobx.js.org/README.html">mobx</a> for observable-oriented
@@ -223,13 +286,13 @@ <h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="
 like Angular.</p>
 </section>
 <section id="application-architecture">
-<h2>Application Architecture<a class="headerlink" href="#application-architecture" title="Link to this heading">#</a></h2>
+<h2>Application Architecture<a class="headerlink" href="#application-architecture" title="Link to this heading">¶</a></h2>
 <p>The LIT client frontend is roughly divided into three conceptual groups -
 <strong>Modules</strong> (which render visualizations), <strong>Services</strong> (which manage data), and
 the <strong>App</strong> itself (which coordinates initialization of services and determines
 which modules to render).</p>
 <section id="bootstrapping">
-<h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this heading">#</a></h3>
+<h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this heading">¶</a></h3>
 <p>The LIT app bootstrapping takes place in two steps: First, the served
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/static/index.html"><code class="docutils literal notranslate"><span class="pre">index.html</span></code></a>
 page contains a single web component for the
@@ -241,16 +304,16 @@ <h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this
 and rendering the various <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> components, a process about which we’ll go
 into greater detail later.</p>
 <p>The JS bundle entry point is
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts"><code class="docutils literal notranslate"><span class="pre">main.ts</span></code></a>, which
-first imports the loaded, the <code class="docutils literal notranslate"><span class="pre">&lt;lit-app&gt;</span></code> web component is declared, and
-attaches itself to the DOM, waiting for the app to be initialized.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts"><code class="docutils literal notranslate"><span class="pre">main.ts</span></code></a>, which first
+imports the loaded, the <code class="docutils literal notranslate"><span class="pre">&lt;lit-app&gt;</span></code> web component is declared, and attaches
+itself to the DOM, waiting for the app to be initialized.</p>
 <p>The second step is kicking off app initialization. The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts"><code class="docutils literal notranslate"><span class="pre">LitApp</span></code></a> singleton
 class is provided with a layout declaring which <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> components to use,
 then builds the app services and kicks off app initialization and loading data.</p>
 </section>
 <section id="layout">
-<h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">#</a></h3>
+<h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">¶</a></h3>
 <p>A layout defines the arraignment of <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> classes in the UI. Layouts are
 specified in Python as <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instances, and LIT includes three
 pre-configured layouts in
@@ -261,7 +324,7 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">#</a
 <li><p><code class="docutils literal notranslate"><span class="pre">default</span></code>: The original LIT layout with a single group of modules on top for
 exploring and selecting data, and a collection of tabs supporting different
 analytical tasks on the bottom; and</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">experimental</span></code>: A three-panel layout that puts exploratory data
+<li><p><code class="docutils literal notranslate"><span class="pre">three_panel</span></code>: A three-panel layout that puts exploratory data
 visualizations at full-page height on the left, tools for inspecting and
 manipulating examples and their associated predictions in the upper right,
 and a collection of tabs supporting different analytical tasks in the lower
@@ -270,14 +333,14 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">#</a
 </ul>
 <p>You can also add <a class="reference internal" href="api.html#customizing-the-layout"><span class="std std-ref">custom layouts</span></a> to your LIT
 instance by defining one or more <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instances and passing them
-to the server. For an example, see <code class="docutils literal notranslate"><span class="pre">CUSTOM_LAYOUTS</span></code> in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
+to the server. For an example, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py"><code class="docutils literal notranslate"><span class="pre">prompt_debugging/layouts.py</span></code></a>.</p>
 <p>Note: The pre-configured layouts are added to every <code class="docutils literal notranslate"><span class="pre">LitApp</span></code> instance using
 <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict">dictionary updates</a> where
 the Mapping passed to the <code class="docutils literal notranslate"><span class="pre">LitApp</span></code> constructor overrides the pre-configured
 layouts <code class="docutils literal notranslate"><span class="pre">Mapping</span></code>. Thus, you can remove or change these pre-configured layouts
 as you like by passing a <code class="docutils literal notranslate"><span class="pre">Mapping</span></code> where the values of <code class="docutils literal notranslate"><span class="pre">simple</span></code>, <code class="docutils literal notranslate"><span class="pre">default</span></code>,
-and/or <code class="docutils literal notranslate"><span class="pre">experimental</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (to remove) or a <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instance
+and/or <code class="docutils literal notranslate"><span class="pre">three_panel</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (to remove) or a <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instance
 (to override) as you desire.</p>
 <p>The actual layout of components in the LIT UI, see
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/modules.ts"><code class="docutils literal notranslate"><span class="pre">&lt;lit-modules&gt;</span></code></a>,
@@ -289,7 +352,7 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">#</a
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/services/modules_service.ts"><code class="docutils literal notranslate"><span class="pre">modules_service</span></code></a>.</p>
 </section>
 <section id="initialization">
-<h3>Initialization<a class="headerlink" href="#initialization" title="Link to this heading">#</a></h3>
+<h3>Initialization<a class="headerlink" href="#initialization" title="Link to this heading">¶</a></h3>
 <p>Finally, the LIT App initializes by building the various service classes and
 starting the initial load of data from the server. This process consists of:</p>
 <ol class="arabic simple">
@@ -301,7 +364,7 @@ <h3>Initialization<a class="headerlink" href="#initialization" title="Link to th
 </section>
 </section>
 <section id="modules-litmodule">
-<h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Link to this heading">#</a></h2>
+<h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Link to this heading">¶</a></h2>
 <p>The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts"><code class="docutils literal notranslate"><span class="pre">LitModule</span></code></a>
 is the base class from which all module components derive. It provides a number
@@ -373,7 +436,7 @@ <h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Li
 necessary static properties and many of the most common patterns found in the
 LIT app.</p>
 <section id="setup">
-<h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a></h3>
+<h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">¶</a></h3>
 <p>First, a <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> must declare a static <code class="docutils literal notranslate"><span class="pre">title</span></code> string (1) and <code class="docutils literal notranslate"><span class="pre">template</span></code>
 function (2). The <code class="docutils literal notranslate"><span class="pre">template</span></code> function determines how the modules layout renders
 the component template and passes in module properties, such as the name of the
@@ -406,7 +469,7 @@ <h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a><
 other TypeScript files in different build units.</p>
 </section>
 <section id="functionality">
-<h3>Functionality<a class="headerlink" href="#functionality" title="Link to this heading">#</a></h3>
+<h3>Functionality<a class="headerlink" href="#functionality" title="Link to this heading">¶</a></h3>
 <p>The above module has a very simple task - When the user selects input data, it
 makes a request to an API service to fetch and display a pig latin translation
 of the data. Since we’re using mobx observables to store and compute our state,
@@ -445,7 +508,7 @@ <h3>Functionality<a class="headerlink" href="#functionality" title="Link to this
 components.</p>
 </section>
 <section id="escape-hatches">
-<h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to this heading">#</a></h3>
+<h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to this heading">¶</a></h3>
 <p>Finally, it’s worth noting that the declarative template-based rendering setup,
 while effective for handling most component render logic, is sometimes
 inadequate for more advanced visualizations. In particular, the template
@@ -475,7 +538,7 @@ <h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to th
 </div>
 </section>
 <section id="stateful-child-elements">
-<h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements" title="Link to this heading">#</a></h3>
+<h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements" title="Link to this heading">¶</a></h3>
 <p>Some modules may contain stateful child elements, where the element has some
 internal state that can have an effect on the module that contains it. Examples
 of this include any modules that contain the
@@ -514,7 +577,7 @@ <h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements"
 </section>
 </section>
 <section id="style-guide">
-<h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this heading">#</a></h2>
+<h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this heading">¶</a></h2>
 <ul>
 <li><p>Please disable clang-format on <code class="docutils literal notranslate"><span class="pre">lit-html</span></code> templates and format these
 manually instead:</p>
@@ -549,7 +612,7 @@ <h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this hea
 </ul>
 </section>
 <section id="development-tips-open-source">
-<h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-open-source" title="Link to this heading">#</a></h2>
+<h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-open-source" title="Link to this heading">¶</a></h2>
 <p>If you’re modifying any TypeScript code, you’ll need to re-build the frontend.
 You can have yarn do this automatically. In one terminal, run:</p>
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>~/lit/lit_nlp
@@ -579,7 +642,7 @@ <h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-
 be sure to remove any stale cache files.</p>
 </section>
 <section id="custom-client-modules">
-<h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" title="Link to this heading">#</a></h2>
+<h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" title="Link to this heading">¶</a></h2>
 <p>The LIT frontend can be extended with custom visualizations or interactive
 modules, though this is currently provided as “best effort” support and the API
 is not as mature as for Python extensions.</p>
@@ -711,8 +774,8 @@ <h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" t
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/genindex.html b/docs/documentation/genindex.html
index 71297246..898845ed 100644
--- a/docs/documentation/genindex.html
+++ b/docs/documentation/genindex.html
@@ -4,9 +4,9 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="#" /><link rel="search" title="Search" href="search.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 --><title>Index - 🔥LIT 1.0 documentation</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 --><title>Index - 🔥LIT 1.0 documentation</title>
 <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -67,7 +67,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -82,7 +82,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -90,7 +90,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -100,6 +100,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -111,6 +165,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -128,7 +184,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -187,7 +244,8 @@
           <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -197,7 +255,7 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           
 <section class="genindex-section">
   <h1 id="index">Index</h1>
@@ -236,8 +294,8 @@ <h1 id="index">Index</h1>
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/getting_started.html b/docs/documentation/getting_started.html
index 400e6844..9d1c0f87 100644
--- a/docs/documentation/getting_started.html
+++ b/docs/documentation/getting_started.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Demos" href="demos.html" /><link rel="prev" title="Learning Interpretability Tool (LIT)" href="index.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Getting Started with LIT - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
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+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
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+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
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+      <path d="M17 17l-2 2l2 2" /
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+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/getting_started.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,13 +263,13 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="getting-started-with-lit">
-<h1>Getting Started with LIT<a class="headerlink" href="#getting-started-with-lit" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2022-08-17' } *-->
+<h1>Getting Started with LIT<a class="headerlink" href="#getting-started-with-lit" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h2>
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
 <p>Using pip:</p>
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>lit-nlp
 </pre></div>
@@ -215,7 +278,7 @@ <h2>Installation<a class="headerlink" href="#installation" title="Link to this h
 <a class="reference external" href="https://github.com/pair-code/lit#download-and-installation">GitHub</a>.</p>
 </section>
 <section id="hosted-demos">
-<h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this heading">#</a></h2>
+<h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this heading">¶</a></h2>
 <p>If you want to jump in and start playing with the LIT UI, check out
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a> for links to our hosted demos.</p>
 <p>For a guide to the many features available, check out the
@@ -223,7 +286,7 @@ <h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this h
 <a class="reference external" href="https://www.youtube.com/watch?v=j0OfBWFUqIE">short video</a>.</p>
 </section>
 <section id="lit-with-your-model">
-<h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerlink" href="#lit-with-your-model" title="Link to this heading">#</a></h2>
+<h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerlink" href="#lit-with-your-model" title="Link to this heading">¶</a></h2>
 <p>LIT provides a simple <a class="reference internal" href="api.html"><span class="std std-doc">Python API</span></a> for use with custom models and
 data, as well as components such as metrics and counterfactual generators. Most
 LIT users will take this route, which involves writing a short <code class="docutils literal notranslate"><span class="pre">demo.py</span></code> binary
@@ -245,7 +308,7 @@ <h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerli
 for your task.</p>
 </section>
 <section id="using-lit-in-notebooks">
-<h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink" href="#using-lit-in-notebooks" title="Link to this heading">#</a></h2>
+<h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink" href="#using-lit-in-notebooks" title="Link to this heading">¶</a></h2>
 <p>LIT can also be used directly from Colab and Jupyter notebooks, with the LIT UI
 rendered in an output cell. See <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/dev/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb">LIT_sentiment_classifier.ipynb</a> for an example.</p>
 <p>Note: if you see a 403 error in the output cell where LIT should render, you may
@@ -253,7 +316,7 @@ <h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink"
 <code class="docutils literal notranslate"><span class="pre">LitWidget</span></code> constructor.</p>
 </section>
 <section id="stand-alone-components">
-<h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerlink" href="#stand-alone-components" title="Link to this heading">#</a></h2>
+<h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerlink" href="#stand-alone-components" title="Link to this heading">¶</a></h2>
 <p>Many LIT components - such as models, datasets, metrics, and salience methods -
 are stand-alone Python classes and can be easily used outside of the LIT UI. For
 additional details, see the
@@ -261,14 +324,13 @@ <h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerl
 at <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/dev/lit_nlp/examples/notebooks/LIT_components_example.ipynb">LIT_components_example.ipynb</a>.</p>
 </section>
 <section id="run-an-existing-example">
-<h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="headerlink" href="#run-an-existing-example" title="Link to this heading">#</a></h2>
+<h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="headerlink" href="#run-an-existing-example" title="Link to this heading">¶</a></h2>
 <p>The <a class="reference internal" href="demos.html"><span class="std std-doc">demos page</span></a> lists some of the pre-built demos available for a
 variety of model types. The code for these is under <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples">examples</a>
 ;
 each is a small script that loads one or more models and starts a LIT server.</p>
-<p>Most demos can be run with a single blaze command. To run the default one, you
-can do:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>lit_nlp.examples.glue_demo<span class="w"> </span><span class="se">\</span>
+<p>Most demos can be run with a single command. To run the default one, you can do:</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>lit_nlp.examples.glue.demo<span class="w"> </span><span class="se">\</span>
 <span class="w">  </span>--quickstart<span class="w"> </span>--port<span class="o">=</span><span class="m">4321</span><span class="w"> </span>--alsologtostderr
 </pre></div>
 </div>
@@ -356,8 +418,8 @@ <h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="heade
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/glossary.html b/docs/documentation/glossary.html
index 3af25e68..43af1927 100644
--- a/docs/documentation/glossary.html
+++ b/docs/documentation/glossary.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Frequently Asked Questions" href="faq.html" /><link rel="prev" title="Running LIT in a Docker container" href="docker.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>Glossary - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
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     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
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     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
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+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
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+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
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+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
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+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
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+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
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+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/glossary.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,9 +263,9 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="glossary">
-<h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">#</a></h1>
+<h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">¶</a></h1>
 <p>There are a few commonly-overloaded terms which refer to specific things in the
 LIT APIs and codebase:</p>
 <ul class="simple">
@@ -300,8 +363,8 @@ <h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/includes/highlight_demos.html b/docs/documentation/includes/highlight_demos.html
index 1050e3d9..56f05c2a 100644
--- a/docs/documentation/includes/highlight_demos.html
+++ b/docs/documentation/includes/highlight_demos.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>&lt;no title&gt; - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="../_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="../_sources/includes/highlight_demos.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,7 +263,7 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           
         </article>
       </div>
@@ -234,8 +297,8 @@
     </aside>
   </div>
 </div><script src="../_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="../_static/doctools.js?v=888ff710"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="../_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/index.html b/docs/documentation/index.html
index dad9c69b..9f1f5286 100644
--- a/docs/documentation/index.html
+++ b/docs/documentation/index.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Getting Started with LIT" href="getting_started.html" />
 
-    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
         <title>🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,6 +102,60 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
+    <title>Auto light/dark, in light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
+      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
+      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
+      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
+      <line x1="19" y1="14.05" x2="20.414" y2="15.464"/>
+      <line x1="8.2" y1="9.55" x2="6.2" y2="9.55"/>
+      <line x1="20.8" y1="9.55" x2="22.8" y2="9.55"/>
+      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
+      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
+      <circle cx="14.5" cy="9.55" r="3.6"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
+      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
+      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
+      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
+      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
+      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
+      <line style="opacity: 50%" x1="20.711" y1="4.789" x2="21.563" y2="3.937"/>
+      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-pencil" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-pencil-code">
+      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
+      <path d="M13.5 6.5l4 4" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" /
+    </svg>
+  </symbol>
+  <symbol id="svg-eye" viewBox="0 0 24 24">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-eye-code">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M10 12a2 2 0 1 0 4 0a2 2 0 0 0 -4 0" />
+      <path
+        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
+      <path d="M20 21l2 -2l-2 -2" />
+      <path d="M17 17l-2 2l2 2" />
+    </svg>
+  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -113,6 +167,8 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
+<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
+
 
 
 <div class="page">
@@ -130,7 +186,8 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -186,11 +243,17 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          
+          <div class="view-this-page">
+  <a class="muted-link" href="_sources/index.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,15 +263,15 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="learning-interpretability-tool-lit">
-<h1>Learning Interpretability Tool (LIT)<a class="headerlink" href="#learning-interpretability-tool-lit" title="Link to this heading">#</a></h1>
+<h1>Learning Interpretability Tool (LIT)<a class="headerlink" href="#learning-interpretability-tool-lit" title="Link to this heading">¶</a></h1>
 <!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-21' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>Welcome to 🔥LIT, the Learning Interpretability Tool!</p>
 <p>If you want to jump in and start playing with the LIT UI, check out the hosted demos at <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>.</p>
 <section id="research">
-<h2>Research<a class="headerlink" href="#research" title="Link to this heading">#</a></h2>
+<h2>Research<a class="headerlink" href="#research" title="Link to this heading">¶</a></h2>
 <p>Found LIT useful in your research? Please cite our
 <a class="reference external" href="https://aclanthology.org/2020.emnlp-demos.15/">system demonstration paper</a>!</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@misc</span><span class="p">{</span><span class="n">tenney2020language</span><span class="p">,</span>
@@ -238,8 +301,6 @@ <h2>Research<a class="headerlink" href="#research" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="demos.html#classification">Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --></a></li>
 <li class="toctree-l2"><a class="reference internal" href="demos.html#regression-scoring">Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --></a></li>
 <li class="toctree-l2"><a class="reference internal" href="demos.html#sequence-to-sequence">Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --></a></li>
-<li class="toctree-l2"><a class="reference internal" href="demos.html#language-modeling">Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --></a></li>
-<li class="toctree-l2"><a class="reference internal" href="demos.html#structured-prediction">Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --></a></li>
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 </ul>
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@@ -262,7 +323,6 @@ <h2>Research<a class="headerlink" href="#research" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="components.html#salience-clustering">Salience Clustering</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#tabular-feature-attribution">Tabular Feature Attribution</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#pixel-based-salience">Pixel-based Salience</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="components.html#tcav">TCAV</a></li>
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index 1e5289dc..3c64769b 100644
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+  <head>
+<meta charset="utf-8"/>
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     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="#" />
 
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-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+<meta name="robots" content="noindex" />
+<title>Search - 🔥LIT 1.0 documentation</title><link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
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index 0945b12d..2f5108ca 100644
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             <button class="theme-toggle">
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+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -200,16 +263,16 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main">
+        <article role="main" id="furo-main-content">
           <section class="tex2jax_ignore mathjax_ignore" id="ui-guide">
-<h1>UI Guide<a class="headerlink" href="#ui-guide" title="Link to this heading">#</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
+<h1>UI Guide<a class="headerlink" href="#ui-guide" title="Link to this heading">¶</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
 <p>This is a user guide for the Learning Interpretability Tool (LIT) UI.</p>
 <p>For a quick video tour of LIT, check out this
 <a class="reference external" href="https://www.youtube.com/watch?v=CuRI_VK83dU">video</a>.</p>
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="general-layout">
-<h2>General Layout<a class="headerlink" href="#general-layout" title="Link to this heading">#</a></h2>
+<h2>General Layout<a class="headerlink" href="#general-layout" title="Link to this heading">¶</a></h2>
 <p>LIT lives inside a single page web application, comprised of multiple toolbars
 and a main section consisting of individual modules. Modules will automatically
 display if they are applicable to the current model and dataset; for example,
@@ -227,7 +290,7 @@ <h2>General Layout<a class="headerlink" href="#general-layout" title="Link to th
 visualization vs. counterfactual example generation), but you can adopt whatever
 organizational scheme you desire in your custom layouts.</p>
 <section id="layout-options">
-<h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to this heading">#</a></h3>
+<h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to this heading">¶</a></h3>
 <!--
   TODO: Add an image of the 3 LIT layouts side by side.
 -->
@@ -238,7 +301,7 @@ <h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to th
 <li><p><code class="docutils literal notranslate"><span class="pre">default</span></code>: The original LIT layout with a single group of modules on top for
 exploring and selecting data, and a collection of tabs supporting different
 analytical tasks on the bottom; and</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">experimental</span></code>: A three-panel layout that puts exploratory data
+<li><p><code class="docutils literal notranslate"><span class="pre">three_panel</span></code>: A three-panel layout that puts exploratory data
 visualizations at full-page height on the left, tools for inspecting and
 manipulating examples and their associated predictions in the upper right,
 and a collection of tabs supporting different analytical tasks in the lower
@@ -248,7 +311,7 @@ <h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to th
 </section>
 </section>
 <section id="datapoint-selections">
-<h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title="Link to this heading">#</a></h2>
+<h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title="Link to this heading">¶</a></h2>
 <p>LIT displays a loaded dataset and its model results across the set of selected
 models. Users can dive into detailed results by selecting datapoints from the
 dataset.</p>
@@ -278,16 +341,16 @@ <h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title
 <em><a class="reference internal" href="#metrics-table">Metrics Module</a></em> section.</p>
 </section>
 <section id="toolbars">
-<h2>Toolbars<a class="headerlink" href="#toolbars" title="Link to this heading">#</a></h2>
+<h2>Toolbars<a class="headerlink" href="#toolbars" title="Link to this heading">¶</a></h2>
 <p>There are three toolbars provided in LIT. The top bar includes the selected
 model(s) and dataset, a settings button, and URL sharing functionality. Below
 that is the main toolbar with the menus and controls for navigation and
 selection. At the bottom of the page is a status bar.</p>
 <p><img alt="LIT toolbars" src="_images/lit-toolbars.png" /></p>
 <section id="top-bar">
-<h3>Top Bar<a class="headerlink" href="#top-bar" title="Link to this heading">#</a></h3>
+<h3>Top Bar<a class="headerlink" href="#top-bar" title="Link to this heading">¶</a></h3>
 <section id="global-settings">
-<h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to this heading">#</a></h4>
+<h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to this heading">¶</a></h4>
 <p>The global settings dialog is accessible through the <strong>“Configure”</strong> button in
 the top bar.</p>
 <p>LIT can be launched with a set of models and datasets. The settings screen
@@ -302,7 +365,7 @@ <h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to
 <p><img alt="LIT global settings" src="_images/lit-settings.png" /></p>
 </section>
 <section id="url-sharing">
-<h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this heading">#</a></h4>
+<h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this heading">¶</a></h4>
 <p>Much of the LIT app’s state — the loaded models and datasets, selected
 datapoints, minimized and/or full-screen modules — is stored in URL
 parameters. The <strong>“Copy Link”</strong> button in the top bar allows a user to share
@@ -313,25 +376,35 @@ <h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this hea
 </section>
 </section>
 <section id="main-toolbar">
-<h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this heading">#</a></h3>
+<h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this heading">¶</a></h3>
 <p>The main toolbar is right below the top bar and contains a number of different
 controls and information. The left side of the toolbar contains a set of menus
 for quickly controlling datapoint selection and coloring. This includes the
 following controls:</p>
 <ul class="simple">
-<li><p>The <strong>“Select related”</strong> option adds any datapoints “related” to the current
-selection. In LIT, “related” is defined as datapoints created from some
-source datapoint (through manual editing or a datapoint generator), or a
-source datapoint that a selected datapoint was created from.</p></li>
-<li><p>The <strong>“Clear selection”</strong> button deselects all selected datapoints. The
-dropdown also contains a color legend for the current color setting.</p></li>
-<li><p>The <strong>Slices</strong> option allows quick selection of an already-created slice of
-datapoints.</p></li>
-<li><p>The <strong>“Datapoint color”</strong> menu enables setting of the color of each
-datapoint in the modules that visualize all datapoints (such as the
-<em>Embeddings</em> and <em>Scalars</em> modules) by any number of datapoint features or
-model outputs on those datapoints (such as coloring by some categorical
-input feature, or by prediction error for a regression task).</p></li>
+<li><p>The <strong>“Select datapoint”</strong> menu provides a drop-down of several options:</p>
+<ul>
+<li><p>the <strong>“Random”</strong> option selects a random datapoint,</p></li>
+<li><p>the <strong>“All related”</strong> option adds any datapoints “related” to the
+current selection. In LIT, “related” is defined as datapoints created
+from some source datapoint (through manual editing or a datapoint
+generator), or a source datapoint that a selected datapoint was created
+from,</p></li>
+<li><p>the <strong>“Parents”</strong> option adds the source datapoints that the selected
+datapoints were created from,</p></li>
+<li><p>the <strong>“Children”</strong> option adds the datapoints created from the selected
+datapoints (through manual editing or a datapoint generator),</p></li>
+<li><p>the <strong>Slices</strong> option allows quick selection of an already-created slice
+of datapoints,</p></li>
+<li><p>the <strong>“Clear selection”</strong> button deselects all selected datapoints.</p></li>
+</ul>
+</li>
+<li><p>The <strong>“Color by”</strong> menu enables setting of the color of each datapoint in
+the modules that visualize all datapoints (such as the <em>Embeddings</em> and
+<em>Scalars</em> modules) by any number of datapoint features or model outputs on
+those datapoints (such as coloring by some categorical input feature, or by
+prediction error for a regression task).</p></li>
+<li><p>The <strong>Slices</strong> menu allows adding/selecting/removing slices of datapoints.</p></li>
 </ul>
 <p>Next to the menus is a button for pinning/unpinning a datapoint. Pinning a
 datapoint puts LIT into datapoint comparison mode, where two datapoints can be
@@ -349,7 +422,7 @@ <h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this h
 to easily select all or none of the datapoints, respectively.</p>
 </section>
 <section id="status-bar">
-<h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this heading">#</a></h3>
+<h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this heading">¶</a></h3>
 <p>The status bar at the bottom of the tool contains a text area on the left side.
 If the tool is currently waiting on the results of a call to the backend (such
 as for running predictions or getting embeddings), this information will be
@@ -362,7 +435,7 @@ <h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this headi
 </section>
 </section>
 <section id="comparing-models">
-<h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link to this heading">#</a></h2>
+<h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link to this heading">¶</a></h2>
 <p>By loading more than one model in the global settings controls, LIT can compare
 multiple models. A subset of modules that show per-model information are then
 duplicated to allow easy comparison across two models. Other modules, such the
@@ -371,7 +444,7 @@ <h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link t
 <p><img alt="LIT model comparison" src="_images/lit-model-compare.png" /></p>
 </section>
 <section id="comparing-datapoints">
-<h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title="Link to this heading">#</a></h2>
+<h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title="Link to this heading">¶</a></h2>
 <p>Pinning a datapoint, through either the toolbar button or controls in modules
 (e.g., the pin icons in Data Table rows), puts LIT into <strong>datapoint comparison
 mode</strong>. In this mode, the pinned datapoint is used as a reference to compare the
@@ -386,7 +459,7 @@ <h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title
 <p><img alt="LIT datapoint comparison" src="_images/lit-datapoint-compare.png" /></p>
 </section>
 <section id="slices">
-<h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">#</a></h2>
+<h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">¶</a></h2>
 <p>The <em>Slice Editor</em> allow users to create, edit, select, and delete slices. The
 current selection can be saved as a slice by giving it a name and clicking
 “Create slice”. The slice list allows you to select any of the previously-saved
@@ -405,14 +478,14 @@ <h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">#</a
 <p><img alt="LIT slice controls" src="_images/lit-slices.png" /></p>
 </section>
 <section id="module-details">
-<h2>Module Details<a class="headerlink" href="#module-details" title="Link to this heading">#</a></h2>
+<h2>Module Details<a class="headerlink" href="#module-details" title="Link to this heading">¶</a></h2>
 <p>This section contains details on using and interacting with individual modules
 that are built into LIT. Note that this list may not be complete and additional
 modules can be created and used in LIT by clients.</p>
 <p>All modules can be toggled to be shown full-screen through use of the
 full-screen button in the top-right of each module.</p>
 <section id="embedding-projector">
-<h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">#</a></h3>
+<h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">¶</a></h3>
 <p>When using LIT with a model that returns embeddings (or activations) in addition
 to predictions, the embedding projector will show all datapoints by their
 embeddings projected down to 3 dimensions. This is useful for exploring and
@@ -430,7 +503,7 @@ <h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="
 toolbar.</p>
 </section>
 <section id="data-table">
-<h3>Data Table<a class="headerlink" href="#data-table" title="Link to this heading">#</a></h3>
+<h3>Data Table<a class="headerlink" href="#data-table" title="Link to this heading">¶</a></h3>
 <p>The data table shows all datapoints in a simple table. Datapoints can be
 selected or unselected through a click. Shift+click allows selecting a set of
 consecutive datapoints, and control+click allows selecting a set of individual
@@ -483,12 +556,12 @@ <h3>Data Table<a class="headerlink" href="#data-table" title="Link to this headi
 such as model predictions, enable them from the “Columns” dropdown.</p>
 </section>
 <section id="datapoint-editor">
-<h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link to this heading">#</a></h3>
+<h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link to this heading">¶</a></h3>
 <p>The datapoint editor shows the details of the primary selected datapoint, if one
 is selected. Any field can be edited, and a new datapoint created with those
-edits through the <strong>“Make new datapoint”</strong> button. Any edit to an existing
-datapoint must be saved as a new datapoint to be explored, to keep datapoints
-immutable for simplicity of use.</p>
+edits through the <strong>“Add”</strong> button. Any edit to an existing datapoint must be
+saved as a new datapoint to be explored, to keep datapoints immutable for
+simplicity of use.</p>
 <p>When no datapoint is selected, the editor shows a blank datapoint that can be
 filled out by hand to create a completely new datapoint.</p>
 <p>Features shown with a “(*)” next to their name are required as model input and
@@ -496,7 +569,7 @@ <h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link t
 <p><a class="reference internal" href="_images/lit-datapoint-editor.png"><img alt="LIT datapoint editor" class="align-center" src="_images/lit-datapoint-editor.png" style="width: 500px;" /></a></p>
 </section>
 <section id="datapoint-generator">
-<h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="Link to this heading">#</a></h3>
+<h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="Link to this heading">¶</a></h3>
 <p>The datapoint generator module allows creation of new datapoints from all
 currently-selected datapoints (or the entire dataset if no datapoints are
 selected) through a set of counterfactual datapoint generators. These generators
@@ -531,7 +604,7 @@ <h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="
 <p><img alt="LIT datapoint generator" src="_images/lit-datapoint-generator.png" /></p>
 </section>
 <section id="metrics-table">
-<h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this heading">#</a></h3>
+<h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this heading">¶</a></h3>
 <p>The metrics table shows model metrics for each model in a table format. The
 exact metric types are determined by the python metrics component that
 calculates metrics given the model types being evaluated. These can include
@@ -553,7 +626,7 @@ <h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this
 <p><img alt="LIT metrics" src="_images/lit-metrics.png" /></p>
 </section>
 <section id="confusion-matrix">
-<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">#</a></h3>
+<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">¶</a></h3>
 <p>The confusion matrix buckets all datapoints from the dataset (or the current
 selection, if one is made) into buckets in a 2D matrix. This is normally used to
 compare classification predictions on a model versus the ground truth classes of
@@ -571,7 +644,7 @@ <h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link t
 <p><img alt="LIT confusion matrix" src="_images/lit-conf-matrix.png" /></p>
 </section>
 <section id="scalars">
-<h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">#</a></h3>
+<h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">¶</a></h3>
 <p>The scalars module shows a set of scatter or jitter plots, one for each scalar
 output of a loaded model (such as a regression score, or a classification score
 for a specific class). Each of them contains all datapoints in the dataset, laid
@@ -595,7 +668,7 @@ <h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">#<
 <p><img alt="LIT prediction scores" src="_images/lit-pred-score.png" /></p>
 </section>
 <section id="model-output">
-<h3>Model Output<a class="headerlink" href="#model-output" title="Link to this heading">#</a></h3>
+<h3>Model Output<a class="headerlink" href="#model-output" title="Link to this heading">¶</a></h3>
 <p>Model output modules show the result of a model on the primary selected
 datapoint. The visuals of these modules depend on the model task being
 performed. For a simple classification task, it will show the class scores from
@@ -608,7 +681,7 @@ <h3>Model Output<a class="headerlink" href="#model-output" title="Link to this h
 <p><a class="reference internal" href="_images/lit-structured-prediction.png"><img alt="LIT structured prediction" class="align-center" src="_images/lit-structured-prediction.png" style="width: 800px;" /></a></p>
 </section>
 <section id="salience-maps">
-<h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this heading">#</a></h3>
+<h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this heading">¶</a></h3>
 <p>Salience maps show the influence of different parts of inputs features on a
 model’s prediction on the primary selection. This module can contain multiple
 methodologies for calculating this salience, depending on the capabilities of
@@ -618,28 +691,20 @@ <h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this
 background of each text piece is colored by the salience of that piece on the
 prediction, and hovering on any piece will display the exact value calculated
 for that piece.</p>
-<p>There is an <strong>“autorun”</strong> button by each methodology. If it is checked, then
-that calculation is made when a new primary datapoint is selected. If it is
+<p>There is an <strong>“autorun”</strong> button by each methodology on the right side of the
+bar (the methodoloy name is on the left side). If it is checked, then that
+calculation is made when a new primary datapoint is selected. If it is
 unchecked, the calculation isn’t made until it is checked. This can be valuable
 so that expensive, long-running saliency calculations (such as LIME) aren’t
 performed on every datapoint selection, but only when explicitly asked for.</p>
 <p><img alt="LIT saliency maps" src="_images/lit-salience.png" /></p>
 </section>
-<section id="attention">
-<h3>Attention<a class="headerlink" href="#attention" title="Link to this heading">#</a></h3>
-<p>For models that return attention head information along with predictions, the
-attention module shows which tokens are attended to between layers of a model.
-Dropdowns allow you to select which layer and attention head is being
-visualized. Line opacity is controlled by the magnitude of the attention between
-those tokens.</p>
-<p><a class="reference internal" href="_images/lit-attention.png"><img alt="LIT attention" class="align-center" src="_images/lit-attention.png" style="width: 500px;" /></a></p>
-</section>
 </section>
 <section id="user-journeys">
-<h2>User Journeys<a class="headerlink" href="#user-journeys" title="Link to this heading">#</a></h2>
+<h2>User Journeys<a class="headerlink" href="#user-journeys" title="Link to this heading">¶</a></h2>
 <p>In this section, we explore some example user journeys and how LIT enables them.</p>
 <section id="sentiment-analysis">
-<h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Link to this heading">#</a></h3>
+<h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Link to this heading">¶</a></h3>
 <p>How well does a sentiment classifier handle negation? We load the development
 set of the Stanford Sentiment Treebank, and use the search function in LIT’s
 data table to find the 56 datapoints containing the word “not”. Looking at the
@@ -656,48 +721,13 @@ <h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Li
 model.</p>
 <p><img alt="Sentiment analysis" src="_images/lit-sentiment-analysis.png" /></p>
 </section>
-<section id="gender-bias-in-coreference">
-<h3>Gender Bias in Coreference<a class="headerlink" href="#gender-bias-in-coreference" title="Link to this heading">#</a></h3>
-<p>Does a system encode gendered associations, which might lead to incorrect
-predictions? We load a coreference model trained on OntoNotes, and load the
-Winogender dataset into LIT for evaluation. Each Winogender example has a
-pronoun and two candidate referents, one a occupation term like (“technician”)
-and one is an “other participant” (like “customer”). Our model predicts
-coreference probabilities for each candidate. We can explore the model’s
-sensitivity to pronouns by comparing two examples side-by-side (see structured
-predictions in screenshot). We can see how commonly the model makes similar
-errors by paging through the dataset, or by selecting specific slices of
-interest.</p>
-<p><img alt="Winogender analysis" src="_images/lit-winogender.png" /></p>
-<p>In the <em>Metrics</em> table, we can slice this selection by pronoun type and by the
-true referent. On the set of male-dominated occupations (&lt; 25% female by BLS),
-we see the model performs well when the ground-truth agrees with the
-stereotype - e.g. when the answer is the occupation term, male pronouns are
-correctly resolved 83% of the time, compared to female pronouns only 37.5% of
-the time (screenshot section (c)).</p>
-<p><img alt="Winogender analysis" src="_images/lit-winogender-metrics.png" /></p>
-</section>
-<section id="debugging-text-generation">
-<h3>Debugging Text Generation<a class="headerlink" href="#debugging-text-generation" title="Link to this heading">#</a></h3>
-<!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a
-     example that fits better with the text generation debugging story -->
-<p>Does the training data explain a particular error in text generation? We analyze
-an older T5 model on the CNN-DM summarization task. LIT’s <em>Scalars</em> module
-allows us to look at per-example ROUGE scores, and quickly select an example
-with middling performance (screenshot section (a)). We find the generated text
-(screenshot section (b)) contains an erroneous constituent: “alastair cook was
-replaced as captain by former captain …”. We can dig deeper, using LIT’s
-language modeling module (screenshot section (c)) to see that the token “by” is
-predicted with high probability (28.7%).</p>
-<p>To find out how T5 arrived at this prediction, we utilize the “similarity
-searcher” component through the datapoint generator (screenshot section (d)).
-This performs a fast approximate nearest-neighbor lookup from a pre-built index
-over the training corpus, using embeddings from the T5 decoder. With one click,
-we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection.
-We see that the words “captain” and “former” appear 34 and 16 times in these
-examples–along with 3 occurrences of “replaced by” (screenshot section (e)),
-suggesting a strong prior toward our erroneous phrase.</p>
-<p><a class="reference internal" href="_images/lit-s2s-journey.png"><img alt="LIT sequence-to-sequence analysis" class="align-center" src="_images/lit-s2s-journey.png" style="width: 500px;" /></a></p>
+<section id="sequence-salience">
+<h3>Sequence salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">¶</a></h3>
+<p>Sequence salience generalizes token-based salience to text-to-text models,
+allowing you to explain the impact of the prompt tokens on parts of the model
+output.</p>
+<p>Check out <a class="reference internal" href="components.html#sequence-salience"><span class="std std-ref">here</span></a> for more details on how to
+navigate the Sequence Salience UI module.</p>
 </section>
 </section>
 </section>
@@ -786,13 +816,11 @@ <h3>Debugging Text Generation<a class="headerlink" href="#debugging-text-generat
 <li><a class="reference internal" href="#scalars">Scalars</a></li>
 <li><a class="reference internal" href="#model-output">Model Output</a></li>
 <li><a class="reference internal" href="#salience-maps">Salience Maps</a></li>
-<li><a class="reference internal" href="#attention">Attention</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#user-journeys">User Journeys</a><ul>
 <li><a class="reference internal" href="#sentiment-analysis">Sentiment Analysis</a></li>
-<li><a class="reference internal" href="#gender-bias-in-coreference">Gender Bias in Coreference</a></li>
-<li><a class="reference internal" href="#debugging-text-generation">Debugging Text Generation</a></li>
+<li><a class="reference internal" href="#sequence-salience">Sequence salience</a></li>
 </ul>
 </li>
 </ul>
@@ -807,8 +835,8 @@ <h3>Debugging Text Generation<a class="headerlink" href="#debugging-text-generat
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/index.html b/docs/index.html
index d6b44ac4..65c7ceaa 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -110,7 +110,6 @@
   <h3>Built-in capabilities</h3>
   <div>
 <p>Salience maps</p>
-<p>Attention visualization</p>
 <p>Metrics calculations</p>
 <p>Counterfactual generation</p>
 <p>Model and datapoint comparison</p>
@@ -151,7 +150,7 @@ <h2>What's the latest</h2>
 <div class="mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--8-col-phone">
   <img class="home-card-image" src="/lit/assets/images/LIT_Updates.png"/>
   <div class="home-card-action">UPDATES</div>
-  <h3 class="home-card-title">Version 1.1</h3>
+  <h3 class="home-card-title">Version 1.2</h3>
   <div class="home-card-desc">Input salience for text-to-text LLMs, with wrappers for HuggingFace Transformers and KerasNLP models.</div>
   <div class="home-card-cta-button"><a href="https://github.com/PAIR-code/lit/blob/main/RELEASE.md" target="_blank" >See release notes</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
 </div>
diff --git a/docs/tutorials/coref/index.html b/docs/tutorials/coref/index.html
deleted file mode 100644
index ea57ac9d..00000000
--- a/docs/tutorials/coref/index.html
+++ /dev/null
@@ -1,187 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <!-- Do we need additional share metadata included here? -->
-
-    <!-- Global site tag (gtag.js) - Google Analytics -->
-<script async src="https://www.googletagmanager.com/gtag/js?id=G-Q74F5RJLXB"></script>
-<script>
-  window.dataLayer = window.dataLayer || [];
-  function gtag(){dataLayer.push(arguments);}
-  gtag('js', new Date());
-
-  gtag('config', 'G-Q74F5RJLXB');
-</script>
-
-    <title>Gender Bias in Coreference</title>
-    
-        <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
-    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
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-    <link rel="stylesheet" href="/lit/assets/css/prism-material-light.css">
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-    <link href="https://fonts.googleapis.com/css?family=Manrope:400,400italic,500,500italic,700,700italic" rel="stylesheet" type="text/css">
-
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-    <link href='/lit/assets/css/new.css' rel='stylesheet' type='text/css'>
-    <link rel="icon" href="/lit/assets/images/favicon.png" type="image/png"/>
-
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-  <body>
-    <div class="mdl-layout mdl-layout--no-desktop-drawer-button mdl-js-layout mdl-layout--fixed-header">
-      
-      <header class="mdl-layout__header">
-        <div class="mdl-layout__header-row">
-          <!-- Title -->
-          <img class='status-emoji' src="/lit/assets/images/favicon.png"></img>
-          <span class="mdl-layout__title">
-            <a href="/lit/">
-              Learning Interpretability Tool
-            </a>
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-          <!-- Navigation. We hide it in small screens. -->
-          <nav class="mdl-navigation mdl-layout--large-screen-only">
-            <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
-            <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
-            <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
-            <a class="mdl-navigation__link" href="/lit/documentation/">DOCUMENTATION</a>
-            <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
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-      </header>
-      <div class="mdl-layout__drawer">
-        <span class="mdl-layout__title"><a href="/lit/">Learning Interpretability Tool</a></span>
-        <nav class="mdl-navigation">
-          <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
-          <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
-          <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
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-      
-      <main class="mdl-layout__content hero-banner">
-        <div class="tutorial-page-container mdl-grid">
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-            <div class="tutorial-breadcrumbs">
-  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Coreference
-</div>
-            <h2>Gender Bias in Coreference</h2>
-<div class="link-out"><a href="../../demos/coref.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py"><code>examples/coref/coref_demo.py</code></a></p>
-<p>Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence &quot;The technician told the customer that they could pay with cash.&quot;, we understand from the context that &quot;they&quot; refers to &quot;the customer&quot;, the one paying.</p>
-<p>The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:</p>
-<ul>
-<li>&quot;The technician told the customer that <strong>he</strong> could pay with cash.&quot;</li>
-<li>&quot;The technician told the customer that <strong>she</strong> could pay with cash.&quot;</li>
-</ul>
-<p>In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.</p>
-<p>We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-pred.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-pred.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: A coreference prediction.</div>
-</div>
-<p>We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the &quot;Predictions&quot; tab below. To look at two predictions side-by-side, we can enable &quot;Compare datapoints&quot; mode in the toolbar, which will pin our first selection as a &quot;reference&quot; and allow us to select another point to compare:</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-select.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-select.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Selecting two datapoints to compare.</div>
-</div>
-<p>We see that LIT automatically replicates the predictions view, allowing us to see how our model handles &quot;he&quot; and &quot;she&quot; differently on these two sentences:</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-compare.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-compare.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Comparing coreference predictions of two datapoints.</div>
-</div>
-<p>To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-data.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-data.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Datapoints with extra informational feature columns.</div>
-</div>
-<p>We see that &quot;technician&quot; is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify &quot;he&quot; as the technician in the example above.</p>
-<p>Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the &quot;Performance&quot; tab, we see that our model gets around 63% accuracy overall.</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-metric-top.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-metric-top.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Overall model accuracy in the metrics table.</div>
-</div>
-<p>Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (&lt; 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-coref-metrics.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-coref-metrics.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Metrics faceted into sub-groups and scalar results plots.</div>
-</div>
-<p>We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.</p>
-
-          </div>
-          <div class="mdl-cell--4-col hide-me">
-            <div class="tutorial-info-container">
-              <div class="tutorial-info-header">time to read</div>
-              <div class="tutorial-info-copy">10 minutes</div>
-              <div class="tutorial-info-header">takeaways</div>
-              <div class="tutorial-info-copy">Learn about how to explore fairness using datapoint comparison and metrics comparisons.</div>
-            </div>
-          </div>
-          <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-            The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our <a href="https://github.com/pair-code/lit/issues/new" target="_blank">Github</a>.
-          </div>
-        </div>
-
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-  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone">
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-    <a href="https://medium.com/people-ai-research" target="_blank"><img src="/lit/assets/images/medium.png"/></a>
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diff --git a/docs/tutorials/generation/index.html b/docs/tutorials/generation/index.html
deleted file mode 100644
index 6647a8b1..00000000
--- a/docs/tutorials/generation/index.html
+++ /dev/null
@@ -1,149 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <!-- Do we need additional share metadata included here? -->
-
-    <!-- Global site tag (gtag.js) - Google Analytics -->
-<script async src="https://www.googletagmanager.com/gtag/js?id=G-Q74F5RJLXB"></script>
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-  function gtag(){dataLayer.push(arguments);}
-  gtag('js', new Date());
-
-  gtag('config', 'G-Q74F5RJLXB');
-</script>
-
-    <title>Debugging a Text Generator</title>
-    
-        <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
-    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
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-              Learning Interpretability Tool
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-            <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
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-            <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
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-            <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
-            <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="-_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
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-      <div class="mdl-layout__drawer">
-        <span class="mdl-layout__title"><a href="/lit/">Learning Interpretability Tool</a></span>
-        <nav class="mdl-navigation">
-          <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
-          <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
-          <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
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-          <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
-          <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
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-      <main class="mdl-layout__content hero-banner">
-        <div class="tutorial-page-container mdl-grid">
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-            <div class="tutorial-breadcrumbs">
-  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Generator
-</div>
-            <h2>Debugging a Text Generator</h2>
-<div class="link-out"><a href="../../demos/t5.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py"><code>examples/t5_demo.py</code></a></p>
-<p>Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-t5.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-t5.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: The datapoint of interest, with its inaccurate summarization.</div>
-</div>
-<p>We find the generated text contains an erroneous subject: &quot;[A]lastair [C]ook was replaced as captain by former captain ...&quot;. In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-sim-search.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-sim-search.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: Finding the most similar datapoints from the training set.</div>
-</div>
-<p>To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings
-from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.</p>
-<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
-  <img class="tutorial-image" src="/lit/assets/images/lit-datatable-search.png"
-    onclick="event.target.nextElementSibling.showPopover()" />
-  <div class="tutorial-image-popover" popover>
-    <img class="tutorial-image" src="/lit/assets/images/lit-datatable-search.png"
-      onclick="event.target.parentElement.hidePopover()" />
-  </div>
-  <div class="tutorial-caption">Above: An example of the existence of "by former" in a similar datapoint from the training set.</div>
-</div>
-
-          </div>
-          <div class="mdl-cell--4-col hide-me">
-            <div class="tutorial-info-container">
-              <div class="tutorial-info-header">time to read</div>
-              <div class="tutorial-info-copy">3 minutes</div>
-              <div class="tutorial-info-header">takeaways</div>
-              <div class="tutorial-info-copy">Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set.</div>
-            </div>
-          </div>
-          <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-            The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our <a href="https://github.com/pair-code/lit/issues/new" target="_blank">Github</a>.
-          </div>
-        </div>
-
-        <div class="footer-container mdl-grid">
-  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone"><a href="https://pair.withgoogle.com/" target="_blank"><img src="/lit/assets/images/pair-logo.svg"/></a></div>
-  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone">
-    <a href="https://research.google/teams/language/" target="_blank">Google Research - Language</a>
-  </div>
-  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone"><a href="https://github.com/pair-code" target="_blank">Github</a></div>
-  <div class="footer-icons mdl-cell mdl-cell--4-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-    <a href="mailto:peopleai@google.com" target="_blank"><img src="/lit/assets/images/mail.png"/></a>
-    <a href="https://medium.com/people-ai-research" target="_blank"><img src="/lit/assets/images/medium.png"/></a>
-    <a href="https://www.youtube.com/channel/UCnnns-uu4yy9BXfYSPIX5AA" target="_blank"><img src="/lit/assets/images/youtube.png"/></a>
-  </div>
-</div>
-
-      </main>
-
-    </div>
-  </body>
-
-  
-
-  <script defer src="/lit/assets/js/material.min.js"></script>
-  <script defer src="/lit/assets/js/material-components-web.min.js"></script>
-</html>
diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html
index d1d43d44..c415c6ce 100644
--- a/docs/tutorials/index.html
+++ b/docs/tutorials/index.html
@@ -115,14 +115,6 @@ <h4 class="tutorial-link-title"><a href="/lit/tutorials/tcav">Global Model Analy
   <h4 class="tutorial-link-title"><a href="/lit/tutorials/sentiment">Exploring a Sentiment Classifier</a></h4>
   <div class="tutorial-link-copy">Learn about how we used LIT to analyze a sentiment classifier.</div>
 </div>
-<div class="tutorial-link-element">
-  <h4 class="tutorial-link-title"><a href="/lit/tutorials/generation">Debugging a Text Generator</a></h4>
-  <div class="tutorial-link-copy">Learn about how we used LIT to debug summarization by a text generation model.</div>
-</div>
-<div class="tutorial-link-element">
-  <h4 class="tutorial-link-title"><a href="/lit/tutorials/coref">Gender Bias in Coreference</a></h4>
-  <div class="tutorial-link-copy">Learn how we used LIT to explore gendered associations in a pronoun resolution model.</div>
-</div>
 <div class="spacer" style="height:50px;"></div>
 </div>
 
diff --git a/docs/tutorials/sentiment/index.html b/docs/tutorials/sentiment/index.html
index 017f1694..1ee96c27 100644
--- a/docs/tutorials/sentiment/index.html
+++ b/docs/tutorials/sentiment/index.html
@@ -77,7 +77,7 @@
 </div>
             <h2>Exploring a Sentiment Classifier</h2>
 <div class="link-out"><a href="../../demos/glue.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code>examples/glue_demo.py</code></a></p>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code>examples/glue/demo.py</code></a></p>
 <p>How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.</p>
 <p>Using the search function in LIT’s data table, we find the 67 datapoints containing the word “not”. By selecting these datapoints and looking at the Metrics Table, we find that our BERT model gets 91% of these correct, which is slightly higher than the accuracy across the entire dataset.</p>
 <div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
diff --git a/docs/tutorials/sequence-salience/index.html b/docs/tutorials/sequence-salience/index.html
index 9e76d614..e499ef9a 100644
--- a/docs/tutorials/sequence-salience/index.html
+++ b/docs/tutorials/sequence-salience/index.html
@@ -14,7 +14,7 @@
   gtag('config', 'G-Q74F5RJLXB');
 </script>
 
-    <title>Prompt Engineering with Sequence Salience</title>
+    <title>Prompt Debugging with Sequence Salience</title>
     
         <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
     <link rel="stylesheet" href="/lit/assets/css/material.min.css">
@@ -73,11 +73,11 @@
         <div class="tutorial-page-container mdl-grid">
           <div class="mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
             <div class="tutorial-breadcrumbs">
-  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Prompt Engineering with Sequence Salience
+  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Prompt Debugging with Sequence Salience
 </div>
-            <h2>Prompt Engineering with Sequence Salience</h2>
+            <h2>Prompt Debugging with Sequence Salience</h2>
 <div class="link-out"><a href="https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb" target="_blank">Follow along in Google Colab.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run this locally with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py"><code>examples/lm_salience_demo.py</code></a></p>
+<p>Or, run this locally with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py"><code>examples/prompt_debugging/server.py</code></a></p>
 <p>Large language models (LLMs), such as <a href="https://gemini.google.com/">Gemini</a> and <a href="https://arxiv.org/abs/2303.08774">GPT-4</a>, have
 become ubiquitous. Recent releases of &quot;open weights&quot; models, including
 <a href="https://llama.meta.com/">Llama 2</a>, <a href="https://mistral.ai/news/announcing-mistral-7b/">Mistral</a>, and <a href="https://ai.google.dev/gemma">Gemma</a>, have made it easier
@@ -294,8 +294,7 @@ <h2>Case Study 2: Assessing Constitutional Principles in Prompts</h2>
 them in prompt engineering use cases.</p>
 <h2>Case Study 3: Side-by-Side Behavior Comparisons</h2>
 <p>LIT support a <a href="../../documentation/ui_guide.html#comparing-datapoints">side-by-side (SxS) mode</a> that can be used to compare two
-models, or here, compare model behavior on two related examples. (Check out the
-<a href="../../tutorials/coref/">gender bias tutorial</a> for another case study in SxS analysis.)
+models, or here, compare model behavior on two related examples.
 Let's see how we can use this to understand differences in prompt designs with
 Sequence Salience.</p>
 <p><a href="https://github.com/openai/grade-school-math">GSM8K</a> is a benchmark dataset of grade school math problems commonly
@@ -441,8 +440,8 @@ <h2>Conclusion</h2>
 <a href="https://dl.acm.org/doi/full/10.1145/3639372">research</a> area. The LIT team has provided reference
 implementations for computing gradient-based salience—
 <a href="https://aclanthology.org/P18-1032/">Grad L2 Norm</a> and <a href="https://arxiv.org/abs/1412.6815">Grad · Input</a>—for LLMs in two
-popular frameworks: <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py">KerasNLP</a> and
-<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py">HuggingFace Transformers</a>.</p>
+popular frameworks: <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py">KerasNLP</a> and
+<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py">HuggingFace Transformers</a>.</p>
 <p>There is considerable opportunity to research how the model analysis foundations
 described in this tutorial can support richer workflows, particularly as they
 relate to aggregate analysis of salience results over many examples, and the
@@ -469,7 +468,7 @@ <h3>Further Reading</h3>
               <div class="tutorial-info-header">time to read</div>
               <div class="tutorial-info-copy">20 minutes</div>
               <div class="tutorial-info-header">takeaways</div>
-              <div class="tutorial-info-copy">Learn to use LIT's Sequence Salience module for prompt engineering.</div>
+              <div class="tutorial-info-copy">Learn to use LIT's Sequence Salience module for prompt debugging.</div>
             </div>
           </div>
           <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
diff --git a/docs/tutorials/tab-feat-attr/index.html b/docs/tutorials/tab-feat-attr/index.html
index 5a6d8e6a..777b5603 100644
--- a/docs/tutorials/tab-feat-attr/index.html
+++ b/docs/tutorials/tab-feat-attr/index.html
@@ -78,7 +78,7 @@
             <h2>Tabular Feature Attribution</h2>
 <div class="link-out"><a href="../../demos/penguins.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
 <p>Or, run your own with
-<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py"><code>examples/penguin_demo.py</code></a></p>
+<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py"><code>examples/penguin/demo.py</code></a></p>
 <p>LIT supports many techniques like salience maps and counterfactual generators
 for text data. But what if you have a tabular dataset? You might want to find
 out which features (columns) are most relevant to the model’s predictions. LIT's
diff --git a/docs/tutorials/text-salience/index.html b/docs/tutorials/text-salience/index.html
index aa5cabf7..58e807a8 100644
--- a/docs/tutorials/text-salience/index.html
+++ b/docs/tutorials/text-salience/index.html
@@ -77,7 +77,7 @@
 </div>
             <h2>Salience Maps for Text</h2>
 <div class="link-out"><a href="../../demos/glue.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code>examples/glue_demo.py</code></a></p>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code>examples/glue/demo.py</code></a></p>
 <p>LIT enables users to analyze individual predictions for text input using
 salience maps, for which gradient-based and/or blackbox methods are available.
 In this tutorial, we will explore how to use salience maps to analyze a text

From 57c3ca3918f0b05ee722f818c0f3f9a7e4a02580 Mon Sep 17 00:00:00 2001
From: Bin Du <dubin@google.com>
Date: Tue, 25 Jun 2024 18:06:28 -0700
Subject: [PATCH 47/47] LIT 1.2 release notes.

PiperOrigin-RevId: 646672631
---
 RELEASE.md                                    |  63 +++++
 docs/demos/index.html                         |  55 ++++
 docs/documentation/_images/attention.png      | Bin 0 -> 188417 bytes
 docs/documentation/_images/lit-attention.png  | Bin 0 -> 91620 bytes
 .../_images/lit-datapoint-compare.png         | Bin 368329 -> 459728 bytes
 .../documentation/_images/lit-s2s-journey.png | Bin 0 -> 147695 bytes
 .../_images/lit-winogender-metrics.png        | Bin 0 -> 160995 bytes
 docs/documentation/_images/lit-winogender.png | Bin 0 -> 254066 bytes
 docs/documentation/_images/pair-selection.png | Bin 159071 -> 34353 bytes
 docs/documentation/_sources/api.md.txt        |  60 +++--
 docs/documentation/_sources/components.md.txt |  89 +++++--
 docs/documentation/_sources/demos.md.txt      |  99 ++++++-
 docs/documentation/_sources/docker.md.txt     |  34 +--
 docs/documentation/_sources/faq.md.txt        |  22 +-
 .../_sources/frontend_development.md.txt      |  16 +-
 .../_sources/getting_started.md.txt           |   5 +-
 docs/documentation/_sources/ui_guide.md.txt   | 114 +++++---
 docs/documentation/_static/basic.css          |   2 +-
 docs/documentation/_static/doctools.js        |   2 +-
 docs/documentation/_static/language_data.js   |   4 +-
 docs/documentation/_static/pygments.css       |  12 +-
 docs/documentation/_static/scripts/furo.js    |   2 +-
 .../documentation/_static/scripts/furo.js.map |   2 +-
 docs/documentation/_static/searchtools.js     | 165 +++++-------
 docs/documentation/_static/styles/furo.css    |   2 +-
 .../documentation/_static/styles/furo.css.map |   2 +-
 docs/documentation/api.html                   | 193 +++++---------
 docs/documentation/components.html            | 250 ++++++++----------
 docs/documentation/demos.html                 | 219 ++++++++-------
 docs/documentation/docker.html                | 127 +++------
 docs/documentation/faq.html                   | 136 +++-------
 docs/documentation/frontend_development.html  | 129 +++------
 docs/documentation/genindex.html              |  78 +-----
 docs/documentation/getting_started.html       | 106 ++------
 docs/documentation/glossary.html              |  87 +-----
 .../includes/highlight_demos.html             |  85 +-----
 docs/documentation/index.html                 |  94 ++-----
 docs/documentation/search.html                |  83 +-----
 docs/documentation/searchindex.js             |   2 +-
 docs/documentation/ui_guide.html              | 248 ++++++++---------
 docs/index.html                               |   3 +-
 docs/tutorials/coref/index.html               | 187 +++++++++++++
 docs/tutorials/generation/index.html          | 149 +++++++++++
 docs/tutorials/index.html                     |   8 +
 docs/tutorials/sentiment/index.html           |   2 +-
 docs/tutorials/sequence-salience/index.html   |  17 +-
 docs/tutorials/tab-feat-attr/index.html       |   2 +-
 docs/tutorials/text-salience/index.html       |   2 +-
 pyproject.toml                                |   2 +-
 49 files changed, 1475 insertions(+), 1484 deletions(-)
 create mode 100644 docs/documentation/_images/attention.png
 create mode 100644 docs/documentation/_images/lit-attention.png
 create mode 100644 docs/documentation/_images/lit-s2s-journey.png
 create mode 100644 docs/documentation/_images/lit-winogender-metrics.png
 create mode 100644 docs/documentation/_images/lit-winogender.png
 create mode 100644 docs/tutorials/coref/index.html
 create mode 100644 docs/tutorials/generation/index.html

diff --git a/RELEASE.md b/RELEASE.md
index de02df59..d6949d7c 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,5 +1,68 @@
 # Learning Interpretability Tool Release Notes
 
+## Release 1.2
+
+This release covers clean-ups on various obsolete demos, as well as improved
+packaging and isolated dependencies on the GLUE, Penguin, Prompt Debugging with
+Sequence Salience and TyDi demos for easier launch.
+
+### New Stuff
+* Improved packaging and instructions for launching Prompt Debugging with
+Sequence Salience demo, as well as minor bug fixes -
+[08289df](https://github.com/PAIR-code/lit/commit/08289df0dd9927dee7147e5aad6e8b51bbe74f9e),
+[675ca2d](https://github.com/PAIR-code/lit/commit/675ca2de21b68dc62e4909c80a2cd57d8ee8b601),
+[15eccb1](https://github.com/PAIR-code/lit/commit/15eccb1197366c925a5beff310fb5d7d369bde0c),
+[e0e35c3](https://github.com/PAIR-code/lit/commit/e0e35c3ffcfd9ad5331d4154e7d33d0b1d0daf89),
+[c7970fb](https://github.com/PAIR-code/lit/commit/c7970fb8c51d2a8bd3647cc7eedd15cca285ac08),
+[cee3b58](https://github.com/PAIR-code/lit/commit/cee3b58baea2de27633109e6dd5b3e4211fa46ea)
+
+* Clean up of obsolete demos -
+[b16059f](https://github.com/PAIR-code/lit/commit/b16059fbd0320d411298009c0226489e1f548a69),
+[f4c0990](https://github.com/PAIR-code/lit/commit/f4c099082f0e89986aad162cc3cd0ac9bc2214c7),
+[6aa2eb6](https://github.com/PAIR-code/lit/commit/6aa2eb64eddb8ca154401bfd6a039762bc374d6d),
+[c2fb41b](https://github.com/PAIR-code/lit/commit/c2fb41b4945edb91fac973cf0ddbca48c6257511),
+[dd196e9](https://github.com/PAIR-code/lit/commit/dd196e941058a1d4246b3df3a3c37595f9791b18),
+[72fd772](https://github.com/PAIR-code/lit/commit/72fd772fa02c7445f27fb517e667987ea8ab34d7),
+[71d88fb](https://github.com/PAIR-code/lit/commit/71d88fb86eb88ffb80d665cf7571b21d7ae06bd2),
+[aa49340](https://github.com/PAIR-code/lit/commit/aa493409c454a2ed269fdedd15353404c14b4936),
+[fc7b0d0](https://github.com/PAIR-code/lit/commit/fc7b0d0624f6cc8e456ac0a1d75a4149927bef2f),
+[2475b3b](https://github.com/PAIR-code/lit/commit/2475b3bb677c8685ab9a291c490783ae2ccce5b8),
+[a59641c](https://github.com/PAIR-code/lit/commit/a59641c014b17409e8e5cfdac1cc1e6916d6da15),
+[1ed82d4](https://github.com/PAIR-code/lit/commit/1ed82d4e81ff6a6ff5146b6198e35444960d326b),
+[7d5ef58](https://github.com/PAIR-code/lit/commit/7d5ef5831427de71416c096a6dbcd46ea064457e),
+[992823b](https://github.com/PAIR-code/lit/commit/992823b027fca8c60edabe837248a508ac04da22),
+[3dad2b0](https://github.com/PAIR-code/lit/commit/3dad2b061b45cb44b1c3f9b9364660e907662069),
+[0656386](https://github.com/PAIR-code/lit/commit/0656386188d6e4b6c83dab58fb4e6569ebea217e),
+[27d7a84](https://github.com/PAIR-code/lit/commit/27d7a841cf6d514e67ebfb2af9f603398499f6e3),
+[8863019](https://github.com/PAIR-code/lit/commit/886301972ec1e7ed274040b46ec0e0c3f34c8ace),
+[71cbdba](https://github.com/PAIR-code/lit/commit/71cbdbaee0fee8e96f52cd4df7a269a0873b9259),
+[416d573](https://github.com/PAIR-code/lit/commit/416d573d79f84b9a6964d36e498b850a249ef452)
+
+* Python requirements update and isolated setup for individual demos -
+[bcc481e](https://github.com/PAIR-code/lit/commit/bcc481e44185d04268f5f8bb4ba762ec2cd35907),
+[bb29f43](https://github.com/PAIR-code/lit/commit/bb29f430ff7be55d74a82aec5dee1e54fa27bed0),
+[fbd8874](https://github.com/PAIR-code/lit/commit/fbd88746263fec0f72f2f01bcc382e88e902ab50),
+[b3c120b](https://github.com/PAIR-code/lit/commit/b3c120b22138fb03a712f11778197cf4966d0c3a),
+[5188c8c](https://github.com/PAIR-code/lit/commit/5188c8c835328efcc9dff5a0a4cf4cd79fabe099),
+[5639e3b](https://github.com/PAIR-code/lit/commit/5639e3b1b71b1c0ddf4a3c9e1bd25517fba18375)
+
+* Documentation cleanup and updates -
+[afd51fe](https://github.com/PAIR-code/lit/commit/afd51fe299c0070a19946a789984957f14a9b5bb),
+[7dda659](https://github.com/PAIR-code/lit/commit/7dda659bec4e933d187b0d7afc04d954ae262cc2),
+[79ada6e](https://github.com/PAIR-code/lit/commit/79ada6edf8b2e485ec6a6425d4c60720b4dab8d1),
+[1c8d6a0](https://github.com/PAIR-code/lit/commit/1c8d6a0269ce5637e05e79ae435f770e2a0da147),
+[2e9d267](https://github.com/PAIR-code/lit/commit/2e9d26738d9344cde0eebd66d49dfc14cd800e74)
+
+### Non-breaking Changes, Bug Fixes, and Enhancements
+* Refactor DataService reactions - [483082d](https://github.com/PAIR-code/lit/commit/483082dcb0beb39795c0fc093fe93036bb6a274c)
+* Add warm_start option to LitWidget - [a5265a4](https://github.com/PAIR-code/lit/commit/a5265a4feeb701b878986f79665d5fdf9ddc244c)
+* Pretty-printing of Model objects - [4fb3bde](https://github.com/PAIR-code/lit/commit/4fb3bde897c68fdeb3bd829f6e5a88223bc131a4)
+* Avoid equivalent shuffles in Scrambler - [0d8c0d9](https://github.com/PAIR-code/lit/commit/0d8c0d948480e0835fd3f451b95b7ec306b6409d)
+* Updated gunicorn config for demos running in Docker - [b14e3b1](https://github.com/PAIR-code/lit/commit/b14e3b1a81d7b6305063f778f46666a4d1326045)
+* Disable embeddings for TyDi - [7ff377f](https://github.com/PAIR-code/lit/commit/7ff377f92820748476e796994fd207e1b5dba1d9)
+* Cast embeddings to float32 before computing distances - [5456011](https://github.com/PAIR-code/lit/commit/5456011db8ead5d53db6f39bcdca3fc388802fbe)
+* Update colab examples to include installation of the lit-nlp package - [48b029c](https://github.com/PAIR-code/lit/commit/48b029c3a1a3f25d4d2611a9b0e94355d41078ef)
+
 ## Release 1.1.1
 
 This release covers various improvements for sequence salience, including new
diff --git a/docs/demos/index.html b/docs/demos/index.html
index 3a33dc86..546221f4 100644
--- a/docs/demos/index.html
+++ b/docs/demos/index.html
@@ -98,6 +98,17 @@
   <div class="demo-card-copy">Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/penguins.html"></a></div>
 </div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="/lit/demos/images.html" target="_blank">Image classification</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> images </span>  <span class="demo-tag"> multiclass classification </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Imagenette
+  </div>
+  <div class="demo-card-copy">Analyze an image classification model with LIT, including multiple image salience techniques.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/images.html"></a></div>
+</div>
 <div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
   <div class="demo-card-title"><a href="/lit/demos/glue.html" target="_blank">Classification and regression models</a></div>
   <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> binary classification </span>  <span class="demo-tag"> multi-class classification </span>  <span class="demo-tag"> regression </span> 
@@ -119,6 +130,50 @@
   </div>
   <div class="demo-card-copy">Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.</div>
   <div class="demo-card-cta-button"><a href="https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb"></a></div>
+</div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="/lit/demos/coref.html" target="_blank">Gender bias in coreference systems</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> coreference </span>  <span class="demo-tag"> fairness </span>  <span class="demo-tag"> Winogender </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Winogender schemas
+  </div>
+  <div class="demo-card-copy">Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/coref.html"></a></div>
+</div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="/lit/demos/lm.html" target="_blank">Fill in the blanks</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> masked language model </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Stanford Sentiment Treebank, Movie Reviews
+  </div>
+  <div class="demo-card-copy">Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/lm.html"></a></div>
+</div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="/lit/demos/t5.html" target="_blank">Text generation</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> T5 </span>  <span class="demo-tag"> generation </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    CNN / Daily Mail
+  </div>
+  <div class="demo-card-copy">Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/t5.html"></a></div>
+</div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="/lit/demos/is_eval.html" target="_blank">Evaluating input salience methods</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> salience </span>  <span class="demo-tag"> evaluation </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Stanford Sentiment Treebank, Toxicity
+  </div>
+  <div class="demo-card-copy">Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/is_eval.html"></a></div>
 </div>
   </div>
 </div>
diff --git a/docs/documentation/_images/attention.png b/docs/documentation/_images/attention.png
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diff --git a/docs/documentation/_sources/api.md.txt b/docs/documentation/_sources/api.md.txt
index 97ea5b64..a174bf39 100644
--- a/docs/documentation/_sources/api.md.txt
+++ b/docs/documentation/_sources/api.md.txt
@@ -1,6 +1,6 @@
 # LIT Python API
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -349,7 +349,7 @@ list of scores for each token. The Integrated Gradients saliency method
 additionally requires a `TokenEmbeddings` input and corresponding output, as
 well as a label field `Target` to pin the gradient target to the same class as
 an input and corresponding output. See the
-[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
+[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
 for an example of these spec requirements.
 
 The core API involves implementing the `run()` method:
@@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see
 *   A field that appears in _both_ the model's input and output specs is assumed
     to represent the same value. This pattern is used for model-based input
     manipulation. For example, a
-    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
+    [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
     might output `'tokens': lit_types.Tokens(...)`, and accept as (optional)
     input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability
     component could take output from the former, swap one or more tokens (e.g.
@@ -712,9 +712,11 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing,
 etc.) when switching between models or datasets with heterogeneous `Spec`s.
 
 When implementing your own LIT components and modules, you can use
-[`utils.find_spec_keys()`][utils-lib-py] (Python) and
-[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a
-`Spec`. These methods recognize and respect subclasses. For example,
+[`utils.find_spec_keys()`][utils-lib]
+(Python) and
+[`findSpecKeys()`][utils-lib]
+(TypeScript) to identify fields of interest in a `Spec`. These methods recognize
+and respect subclasses. For example,
 `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore`
 fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all
 `Scalar` fields in the `Spec`.
@@ -805,13 +807,8 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples)
 
 ### Available types
 
-The full set of `LitType`s is defined in
-[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types
-such as `Integer` and `Scalar` have predefined ranges that can be overridden
-using corresponding `min_val` and `max_val` attributes as seen in
-[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py)
-`INPUT_SPEC`. The different types available in LIT are summarized in the table
-below.
+The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized
+in the table below.
 
 Note: Bracket syntax, such as `<float>[num_tokens]`, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
@@ -862,7 +859,7 @@ naming collisions with protected TypeScript keywords.*
 Some properties of the LIT frontend can be configured from Python as
 **arguments to `dev_server.Server()`**. These include:
 
-*   `page_title`: set a custom page title.
+*   `page_title`: set a custom page title, such as "Coreference Demo".
 *   `canonical_url`: set a "canonical" URL (such as a shortlink) that will be
     used as the base when copying links from the LIT UI.
 *   `default_layout`: set the default UI layout, by name. See `layout.ts` and
@@ -889,16 +886,22 @@ You can specify custom web app layouts from Python via the `layouts=` attribute.
 The value should be a `Mapping[str, LitCanonicalLayout]`, such as:
 
 ```python
-PENGUIN_LAYOUT = layout.LitCanonicalLayout(
+LM_LAYOUT = layout.LitCanonicalLayout(
     upper={
-        'Main': [
-            modules.DiveModule,
+        "Main": [
+            modules.EmbeddingsModule,
             modules.DataTableModule,
             modules.DatapointEditorModule,
         ]
     },
-    lower=layout.STANDARD_LAYOUT.lower,
-    description='Custom layout for the Palmer Penguins demo.',
+    lower={
+        "Predictions": [
+            modules.LanguageModelPredictionModule,
+            modules.ConfusionMatrixModule,
+        ],
+        "Counterfactuals": [modules.GeneratorModule],
+    },
+    description="Custom layout for language models.",
 )
 ```
 
@@ -909,12 +912,14 @@ lit_demo = dev_server.Server(
     models,
     datasets,
     # other args...
-    layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT},
-    default_layout='penguins',
+    layouts={"lm": LM_LAYOUT},
     **server_flags.get_flags())
 return lit_demo.serve()
 ```
 
+For a full example, see
+[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
+
 You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A
@@ -984,15 +989,15 @@ needing to reload the server or click the UI.
 For example, to view examples in a dataset:
 
 ```python
-from lit_nlp.examples.glue import data as glue_data
-dataset = glue_data.SST2Data('validation')
+from lit_nlp.examples.datasets import glue
+dataset = glue.SST2Data('validation')
 print(dataset.examples)  # list of records {"sentence": ..., "label": ...}
 ```
 
 And to run inference on a few of them:
 
 ```python
-from lit_nlp.examples.glue import models as glue_models
+from lit_nlp.examples.models import glue_models
 
 model = glue_models.SST2Model("/path/to/model/files")
 preds = list(model.predict(dataset.examples[:5]))
@@ -1016,19 +1021,16 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https://
 <!-- Links -->
 
 [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py
-[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py
+[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
 [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py
 [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
 [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py
 [json]: https://www.json.org
 [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/
-
 [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html
-
-[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py
+[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py
 [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts
 [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py
 [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts
 [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts
-[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py
 [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py
diff --git a/docs/documentation/_sources/components.md.txt b/docs/documentation/_sources/components.md.txt
index 90b99dbd..f2366d7c 100644
--- a/docs/documentation/_sources/components.md.txt
+++ b/docs/documentation/_sources/components.md.txt
@@ -1,6 +1,6 @@
 # Components and Features
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -44,9 +44,10 @@ every invocation.) Generally, you'll need to:
     directly.
 
 Alternatively, you can export to a `SavedModel` and load this in an eager mode
-runtime. This leads to much simpler code, but may require changes to your
-`SavedModel` exporter in order to access model internals like embeddings,
-gradients, or attention.
+runtime. This leads to much simpler code (see
+[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)),
+but may require changes to your `SavedModel` exporter in order to access model
+internals like embeddings, gradients, or attention.
 
 ### Remote or hosted models
 
@@ -104,6 +105,8 @@ LIT supports many features for classification tasks, including common metrics,
 confusion matrices, and custom thresholding via the UI. Classification is
 implemented with the `MulticlassPreds` and `CategoryLabel` types.
 
+*   For a basic example on a binary sentiment task, see
+    [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py).
 *   Models should define a `MulticlassPreds` field in their output spec with the
     `vocab=` attribute as the set of class labels, and for each example should
     return a vector of probabilities for each class.
@@ -113,7 +116,9 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types.
 *   A negative class can be designated using the `null_idx` attribute of
     `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as
     precision, recall, F1 will be computed for the remaining classes. AUC and
-    AUCPR will be computed for binary classification tasks.
+    AUCPR will be computed for binary classification tasks. For an example, see
+    the
+    [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180).
 *   If `null_idx` is set and there is only one other class, the other class
     (often, class `1`) is treated as a positive class, and the LIT UI can be
     used to change the classification threshold. If `null_idx` is set and there
@@ -134,7 +139,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the
     which contains numerical targets, and the model's `RegressionScore` field
     should set `parent=` to the name of this field.
 *   For an example, see the STS-B textual similarity task in
-    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
+    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
 
 ### Multi-label classification
 
@@ -142,6 +147,8 @@ LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 `SparseMultilabelPreds` and `SparseMultilabel` types.
 
+*   For a basic example on an image labeling task, see
+    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
 *   Models should define a `SparseMultilabelPreds` field in their output spec
     with the`vocab=` attribute as the set of class labels, and for each example
     should return a list of class score tuples. Each tuple contains two
@@ -172,6 +179,8 @@ or decoder.
     the model can also output a `ReferenceScores` field (with values as
     `list[float]`) with `parent=` set to reference a `TextSegment` or
     `ReferenceTexts` field from the input.
+*   For modeling examples, see
+    [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py)
 
 ![Generated Text Module](images/components/generation-results.png){w=600px align=center}
 
@@ -206,7 +215,7 @@ and otherwise to different parts of the input.
     [Dataset class documentation](./api.md#datasets) and the corresponding
     [Model](./api.md#models).
 *   For a more involved code example including per-token gradients, see
-    [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
+    [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py).
 
 ### Tabular data
 
@@ -224,7 +233,7 @@ these cases, LIT validates model compatibility by checking that:
     (`SparseMultilabel`) field.
 
 For a demo using a penguin stats dataset/binary classification task, see
-[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py).
+[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py).
 
 ### Images
 
@@ -237,6 +246,12 @@ NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don't break existing checked-in code with
 such a change.
 
+*   See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a
+    dataset containing images, including converting images to base64 encoded
+    strings.
+*   For a demo of an image classifier, see
+    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
+
 ## Token-based Salience
 
 LIT supports several methods for token-based input salience, including
@@ -286,8 +301,8 @@ $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$
 
 Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-$\hat{y}$, while a negative score suggests that the prediction would be
-stronger if that token was removed.
+$\hat{y}$, while a negative score suggests that the prediction would be stronger
+if that token was removed.
 
 To enable this method, your model should, as part of the
 [output spec and `predict()` implementation](./api.md#models):
@@ -351,7 +366,7 @@ An example spec would look like:
 
 For a more concrete example that also supports multiple segments with separate
 gradients, see our
-[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py),
+[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py),
 or contact the LIT team for assistance.
 
 ### LIME
@@ -433,7 +448,7 @@ left-to-right ("causal") language models:
 
 The UI supports multiple options for analysis, including:
 
-*   Select from predefined target sequences, or explain generations from the
+*   Select from pre-defined target sequences, or explain generations from the
     model.
 *   Different salience methods, including [Gradient Norm](#gradient-norm) and
     [Gradient-dot-Input](#gradient-dot-input).
@@ -441,7 +456,7 @@ The UI supports multiple options for analysis, including:
     to words, sentences, lines, or paragraphs. Quickly switch between different
     views to refine your analysis to different parts of a prompt.
 *   Display density options to enable working with longer sequences, such as
-    document text, few-shot examples, or chain-of-thought prompts.
+    document text, few-shot eaxmples, or chain-of-thought prompts.
 
 For a walkthrough of how to use sequence salience to debug LLMs, check out the
 Responsible Generative AI Toolkit at
@@ -467,11 +482,13 @@ using either KerasNLP or Transformers.
 *   LIT-for-Gemma Colab:
     [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb)
 *   Demo binary:
-    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py
+    [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
 *   KerasNLP model wrappers:
-    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py
+    [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py)
 *   Transformers model wrappers:
-    https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py
+    [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
+
+
 
 ## Salience Clustering
 
@@ -523,7 +540,9 @@ your model should, as part of the
     input image.
 
     The model should be able to accept input images as numpy arrays in addition
-    to accepting base64 URL encoded format.
+    to accepting base64 URL encoded format. See
+    [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py)
+    for an example.
 
 A variety of image saliency techniques are implemented for models that return
 image gradients, through use of the
@@ -534,6 +553,36 @@ and XRAI.
 Each of these techniques returns a saliency map image as a base64-encoded string
 through the `ImageSalience` type.
 
+## Attention
+
+LIT can display a visualization of attention heads from transformers and other
+models:
+
+![Attention Visualization](./images/components/attention.png){w=400px align=center}
+
+To enable this, your model should return one or more fields of the type
+`AttentionHeads`, with values as arrays of shape `<float>[num_heads, num_tokens,
+num_tokens]`. Each field represents a set of heads at a single layer of the
+model, so models will often have more than one:
+
+```python
+   def output_spec(self) -> types.Spec:
+     return {
+         # ...
+         "tokens": lit_types.Tokens(parent="input_text"),
+         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         # ...
+     }
+```
+
+The `align_in` and `align_out` attributes are the names of `Tokens` fields for
+the source and target tokens for that layer. For self-attention (such as in
+BERT) these would name the same field, but for encoder-decoder attention they
+could reference different token sets (such as "input_tokens" and
+"target_tokens").
+
 ## Embedding Projector
 
 LIT includes a version of the
@@ -565,7 +614,7 @@ example, we could facet by class label:
 
 ![Metrics Table](./images/components/metrics-table.png)
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
 
 To enable metrics, your model should set the `parent` attribute on one or more
 output fields, pointing to the name of the input field that it should be
@@ -604,7 +653,7 @@ analysis.
 
 ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center}
 
-To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab.
+To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab.
 
 The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (`CategoryLabel`).
@@ -690,8 +739,6 @@ datapoints, giving a global view of feature effects.
 
 ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center}
 
-To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab.
-
 ### Dive
 
 Dive is a visualization module, inspired by our prior work on
diff --git a/docs/documentation/_sources/demos.md.txt b/docs/documentation/_sources/demos.md.txt
index 448c0d23..306d3139 100644
--- a/docs/documentation/_sources/demos.md.txt
+++ b/docs/documentation/_sources/demos.md.txt
@@ -1,6 +1,6 @@
 # Demos
 
-<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
+<!-- freshness: { owner: 'lit-dev' reviewed: '2023-08-29' } -->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/.
 ### Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \
-**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
+**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
 
 *   Multi-task demo:
     *   Sentiment analysis as a binary classification task
@@ -36,12 +36,26 @@ https://pair-code.github.io/lit/demos/.
         the margin.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
+    *   Attention visualization.
     *   Counterfactual generators, including HotFlip for targeted adversarial
         perturbations.
 
 Tip: check out a case study for this demo on the public LIT website:
 https://pair-code.github.io/lit/tutorials/sentiment
 
+### Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} -->
+
+**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py)
+
+*   [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of
+    MultiNLI into 14 different languages.
+*   Specify `--languages=en,jp,hi,...` flag to select which languages to load.
+*   NLI as a three-way classification task with two-segment input (premise,
+    hypothesis).
+*   Fine-tuned multilingual BERT model.
+*   Salience methods work with non-whitespace-delimited text, by using the
+    model's wordpiece tokenization.
+
 --------------------------------------------------------------------------------
 
 ## Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} -->
@@ -49,7 +63,7 @@ https://pair-code.github.io/lit/tutorials/sentiment
 ### Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \
-**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)
+**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py)
 
 *   STS-B textual similarity task, predicting scores on a range from 0
     (unrelated) to 5 (very similar).
@@ -60,6 +74,7 @@ https://pair-code.github.io/lit/tutorials/sentiment
         quick filtering.
     *   Embedding projector to find clusters in representation space.
     *   Integrated Gradients, LIME, and other salience methods.
+    *   Attention visualization.
 
 --------------------------------------------------------------------------------
 
@@ -67,20 +82,76 @@ https://pair-code.github.io/lit/tutorials/sentiment
 
 ### Gemma <!-- DO NOT REMOVE {#gemma .demo-header} -->
 
-**Code:**
-[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)
+**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
 
-*   Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
-    and Transformers (with PyTorch).
+*   Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
 *   Interactively debug LLM prompts using
     [sequence salience](./components.md#sequence-salience).
 *   Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-    granularities: token-, word-, line-, sentence-, and paragraph-level.
+    granularities: token-, word-, sentence-, and paragraph-level.
 
 Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
 Generative AI Toolkit.
 
+### T5 <!-- DO NOT REMOVE {#t5 .demo-header} -->
+
+**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \
+**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py)
+
+*   Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
+    formats.
+*   Visualize beam candidates and highlight diffs against references.
+*   Visualize per-token decoder hypotheses to see where the model veers away
+    from desired output.
+*   Filter examples by ROUGE score against reference.
+*   Embeddings from last layer of model, visualized with UMAP or PCA.
+*   Task wrappers to handle pre- and post-processing for summarization and
+    machine translation tasks.
+*   Pre-loaded eval sets for CNNDM and WMT.
+
+Tip: check out a case study for this demo on the public LIT website:
+https://pair-code.github.io/lit/tutorials/generation
+
+--------------------------------------------------------------------------------
+
+## Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} -->
+
+### BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} -->
+
+**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \
+**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py)
+
+*   Compare multiple BERT and GPT-2 models side-by-side on a variety of
+    plain-text corpora.
+*   LM visualization supports different modes:
+    *   BERT masked language model: click-to-mask, and query model at that
+        position.
+    *   GPT-2 shows left-to-right hypotheses for each target token.
+*   Embedding projector to show latent space of the model.
+
+--------------------------------------------------------------------------------
+
+## Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} -->
+
+### Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} -->
+
+**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \
+**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py)
+
+*   Gold-mention coreference model, trained on
+    [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19).
+*   Evaluate on the Winogender schemas
+    ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for
+    gendered associations with profession names.
+*   Visualizations of coreference edges, as well as binary classification
+    between two candidate referents.
+*   Stratified metrics for quantifying model bias as a function of pronoun
+    gender or Bureau of Labor Statistics profession data.
+
+Tip: check out a case study for this demo on the public LIT website:
+https://pair-code.github.io/lit/tutorials/coref
+
 --------------------------------------------------------------------------------
 
 ## Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} -->
@@ -88,7 +159,7 @@ Generative AI Toolkit.
 ### Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \
-**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py)
+**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py)
 
 *   Binary classification on
     [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins).
@@ -98,3 +169,13 @@ Generative AI Toolkit.
 *   Use binary classifier threshold setters to find best thresholds for slices
     of examples to achieve specific fairness constraints, such as demographic
     parity.
+
+### Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} -->
+
+**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \
+**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py)
+
+*   Classification on ImageNet labels using a MobileNet model.
+*   Showing using of LIT on image data.
+*   Explore results of multiple gradient-based image saliency techniques in the
+    Salience Maps module.
diff --git a/docs/documentation/_sources/docker.md.txt b/docs/documentation/_sources/docker.md.txt
index ebf7eb7c..c9cad085 100644
--- a/docs/documentation/_sources/docker.md.txt
+++ b/docs/documentation/_sources/docker.md.txt
@@ -1,6 +1,6 @@
 # Running LIT in a Docker container
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-04' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-07-10' } *-->
 
 Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
@@ -13,21 +13,22 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or
 your preferred engine. This is how we run our
 [hosted demos](https://pair-code.github.io/lit/demos/).
 
-We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory.
-The `Dockerfile` installs all necessary dependencies for LIT and builds the
+We provide a basic
+[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can
+use to build and run any of the demos in the `lit_nlp/examples` directory. The
+`Dockerfile` installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as
 the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py).
 You can find a reference implementation in
-[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py).
+[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or
+[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
 
-Use the following shell
-https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the
-default Docker image for LIT from the provided `Dockerfile`, and then run a
-container from that image. Comments are provided in-line to help explain what
-each step does.
+Use the following shell commands to build the default Docker image for LIT from
+the provided `Dockerfile`, and then run a container from that image. Comments
+are provided in-line to help explain what each step does.
 
 ```shell
 # Build the docker image using the -t argument to name the image. Remember to
@@ -47,11 +48,14 @@ below.
 ```shell
 # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
+#     "lit_nlp.examples.$DEMO_NAME"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+# lit_nlp/examples directory, such as
+#
+#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
+#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -62,15 +66,15 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp
 # containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
 ```
 
 ## Integrating Custom LIT Instances with the Default Docker Image
 
 Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a `main()` method, as
-described in the [Python API docs](api.md#adding-models-and-data).
+described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data).
 
 These custom server scripts can be easily integrated with LIT's default image as
 long as your server meets two requirements:
diff --git a/docs/documentation/_sources/faq.md.txt b/docs/documentation/_sources/faq.md.txt
index aa8bd03d..de6a5b57 100644
--- a/docs/documentation/_sources/faq.md.txt
+++ b/docs/documentation/_sources/faq.md.txt
@@ -1,6 +1,6 @@
 # Frequently Asked Questions
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-19' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -17,7 +17,9 @@ works with any modern ML framework. For more information, see
 In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
 
-*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) -
+*   [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) -
+    image classification, using a Mobilenet model.
+*   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the
     [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins)
@@ -30,7 +32,13 @@ For more details, see
 
 All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications.
+scripts should work without any modifications. For examples, see:
+
+*   [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) -
+    cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
+    model.
+*   [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) -
+    includes WMT data for machine translation
 
 ## Scale
 
@@ -91,8 +99,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir`
 is specified, may be cached to disk.
 
 LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to disk,
-then pass the `--demo_mode` runtime flag to the LIT server.
+disk. If you do not want the tool to be able to write edited datapoints to
+disk, then pass the `--demo_mode` runtime flag to the LIT server.
 
 ### I have proprietary data. Is LIT secure for my team to use?
 
@@ -169,5 +177,7 @@ official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT's `Dataset` objects to manage training data along with standard
 training APIs (such as Keras' `model.fit()`). See
-[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py)
+[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py)
+and/or
+[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py)
 for examples.
diff --git a/docs/documentation/_sources/frontend_development.md.txt b/docs/documentation/_sources/frontend_development.md.txt
index fe8a82bb..a51aed81 100644
--- a/docs/documentation/_sources/frontend_development.md.txt
+++ b/docs/documentation/_sources/frontend_development.md.txt
@@ -1,6 +1,6 @@
 # Frontend Developer Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go
 into greater detail later.
 
 The JS bundle entry point is
-[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first
-imports the loaded, the `<lit-app>` web component is declared, and attaches
-itself to the DOM, waiting for the app to be initialized.
+[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which
+first imports the loaded, the `<lit-app>` web component is declared, and
+attaches itself to the DOM, waiting for the app to be initialized.
 
 The second step is kicking off app initialization. The
 [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton
@@ -67,7 +67,7 @@ pre-configured layouts in
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `three_panel`: A three-panel layout that puts exploratory data
+*   `experimental`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -76,15 +76,15 @@ pre-configured layouts in
 
 You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT
 instance by defining one or more `LitCanonicalLayout` instances and passing them
-to the server. For an example, see
-[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py).
+to the server. For an example, see `CUSTOM_LAYOUTS` in
+[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py).
 
 Note: The pre-configured layouts are added to every `LitApp` instance using
 [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where
 the Mapping passed to the `LitApp` constructor overrides the pre-configured
 layouts `Mapping`. Thus, you can remove or change these pre-configured layouts
 as you like by passing a `Mapping` where the values of `simple`, `default`,
-and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance
+and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance
 (to override) as you desire.
 
 The actual layout of components in the LIT UI, see
diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt
index 0ca605bf..80e4eae9 100644
--- a/docs/documentation/_sources/getting_started.md.txt
+++ b/docs/documentation/_sources/getting_started.md.txt
@@ -1,6 +1,6 @@
 # Getting Started with LIT
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2022-08-17' } *-->
 
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 
@@ -72,7 +72,8 @@ variety of model types. The code for these is under [examples](https://github.co
 ;
 each is a small script that loads one or more models and starts a LIT server.
 
-Most demos can be run with a single command. To run the default one, you can do:
+Most demos can be run with a single blaze command. To run the default one, you
+can do:
 
 ```sh
 python -m lit_nlp.examples.glue.demo \
diff --git a/docs/documentation/_sources/ui_guide.md.txt b/docs/documentation/_sources/ui_guide.md.txt
index bb5028ae..4a38a88c 100644
--- a/docs/documentation/_sources/ui_guide.md.txt
+++ b/docs/documentation/_sources/ui_guide.md.txt
@@ -1,6 +1,6 @@
 # UI Guide
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 
 This is a user guide for the Learning Interpretability Tool (LIT) UI.
 
@@ -42,7 +42,7 @@ LIT provides three pre-configured layouts:
 *   `default`: The original LIT layout with a single group of modules on top for
     exploring and selecting data, and a collection of tabs supporting different
     analytical tasks on the bottom; and
-*   `three_panel`: A three-panel layout that puts exploratory data
+*   `experimental`: A three-panel layout that puts exploratory data
     visualizations at full-page height on the left, tools for inspecting and
     manipulating examples and their associated predictions in the upper right,
     and a collection of tabs supporting different analytical tasks in the lower
@@ -130,26 +130,19 @@ controls and information. The left side of the toolbar contains a set of menus
 for quickly controlling datapoint selection and coloring. This includes the
 following controls:
 
-*   The **"Select datapoint"** menu provides a drop-down of several options:
-    *   the **"Random"** option selects a random datapoint,
-    *   the **"All related"** option adds any datapoints "related" to the
-        current selection. In LIT, "related" is defined as datapoints created
-        from some source datapoint (through manual editing or a datapoint
-        generator), or a source datapoint that a selected datapoint was created
-        from,
-    *   the **"Parents"** option adds the source datapoints that the selected
-        datapoints were created from,
-    *   the **"Children"** option adds the datapoints created from the selected
-        datapoints (through manual editing or a datapoint generator),
-    *   the **Slices** option allows quick selection of an already-created slice
-        of datapoints,
-    *   the **"Clear selection"** button deselects all selected datapoints.
-*   The **"Color by"** menu enables setting of the color of each datapoint in
-    the modules that visualize all datapoints (such as the *Embeddings* and
-    *Scalars* modules) by any number of datapoint features or model outputs on
-    those datapoints (such as coloring by some categorical input feature, or by
-    prediction error for a regression task).
-*   The **Slices** menu allows adding/selecting/removing slices of datapoints.
+*   The **"Select related"** option adds any datapoints "related" to the current
+    selection. In LIT, "related" is defined as datapoints created from some
+    source datapoint (through manual editing or a datapoint generator), or a
+    source datapoint that a selected datapoint was created from.
+*   The **"Clear selection"** button deselects all selected datapoints. The
+    dropdown also contains a color legend for the current color setting.
+*   The **Slices** option allows quick selection of an already-created slice of
+    datapoints.
+*   The **"Datapoint color"** menu enables setting of the color of each
+    datapoint in the modules that visualize all datapoints (such as the
+    *Embeddings* and *Scalars* modules) by any number of datapoint features or
+    model outputs on those datapoints (such as coloring by some categorical
+    input feature, or by prediction error for a regression task).
 
 Next to the menus is a button for pinning/unpinning a datapoint. Pinning a
 datapoint puts LIT into datapoint comparison mode, where two datapoints can be
@@ -329,9 +322,9 @@ such as model predictions, enable them from the "Columns" dropdown.
 
 The datapoint editor shows the details of the primary selected datapoint, if one
 is selected. Any field can be edited, and a new datapoint created with those
-edits through the **"Add"** button. Any edit to an existing datapoint must be
-saved as a new datapoint to be explored, to keep datapoints immutable for
-simplicity of use.
+edits through the **"Make new datapoint"** button. Any edit to an existing
+datapoint must be saved as a new datapoint to be explored, to keep datapoints
+immutable for simplicity of use.
 
 When no datapoint is selected, the editor shows a blank datapoint that can be
 filled out by hand to create a completely new datapoint.
@@ -480,15 +473,24 @@ background of each text piece is colored by the salience of that piece on the
 prediction, and hovering on any piece will display the exact value calculated
 for that piece.
 
-There is an **"autorun"** button by each methodology on the right side of the
-bar (the methodoloy name is on the left side). If it is checked, then that
-calculation is made when a new primary datapoint is selected. If it is
+There is an **"autorun"** button by each methodology. If it is checked, then
+that calculation is made when a new primary datapoint is selected. If it is
 unchecked, the calculation isn't made until it is checked. This can be valuable
 so that expensive, long-running saliency calculations (such as LIME) aren't
 performed on every datapoint selection, but only when explicitly asked for.
 
 ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps")
 
+### Attention
+
+For models that return attention head information along with predictions, the
+attention module shows which tokens are attended to between layers of a model.
+Dropdowns allow you to select which layer and attention head is being
+visualized. Line opacity is controlled by the magnitude of the attention between
+those tokens.
+
+![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center}
+
 ## User Journeys
 
 In this section, we explore some example user journeys and how LIT enables them.
@@ -513,11 +515,51 @@ model.
 
 ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis")
 
-### Sequence salience
-
-Sequence salience generalizes token-based salience to text-to-text models,
-allowing you to explain the impact of the prompt tokens on parts of the model
-output.
-
-Check out [here](components.md#sequence-salience) for more details on how to
-navigate the Sequence Salience UI module.
+### Gender Bias in Coreference
+
+Does a system encode gendered associations, which might lead to incorrect
+predictions? We load a coreference model trained on OntoNotes, and load the
+Winogender dataset into LIT for evaluation. Each Winogender example has a
+pronoun and two candidate referents, one a occupation term like (“technician”)
+and one is an “other participant” (like “customer”). Our model predicts
+coreference probabilities for each candidate. We can explore the model’s
+sensitivity to pronouns by comparing two examples side-by-side (see structured
+predictions in screenshot). We can see how commonly the model makes similar
+errors by paging through the dataset, or by selecting specific slices of
+interest.
+
+![Winogender analysis](./images/lit-winogender.png "Winogender analysis")
+
+In the *Metrics* table, we can slice this selection by pronoun type and by the
+true referent. On the set of male-dominated occupations (< 25% female by BLS),
+we see the model performs well when the ground-truth agrees with the
+stereotype - e.g. when the answer is the occupation term, male pronouns are
+correctly resolved 83% of the time, compared to female pronouns only 37.5% of
+the time (screenshot section (c)).
+
+![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis")
+
+### Debugging Text Generation
+
+<!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a
+     example that fits better with the text generation debugging story -->
+
+Does the training data explain a particular error in text generation? We analyze
+an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module
+allows us to look at per-example ROUGE scores, and quickly select an example
+with middling performance (screenshot section (a)). We find the generated text
+(screenshot section (b)) contains an erroneous constituent: “alastair cook was
+replaced as captain by former captain ...”. We can dig deeper, using LIT’s
+language modeling module (screenshot section (c)) to see that the token “by” is
+predicted with high probability (28.7%).
+
+To find out how T5 arrived at this prediction, we utilize the “similarity
+searcher” component through the datapoint generator (screenshot section (d)).
+This performs a fast approximate nearest-neighbor lookup from a pre-built index
+over the training corpus, using embeddings from the T5 decoder. With one click,
+we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection.
+We see that the words “captain” and “former” appear 34 and 16 times in these
+examples–along with 3 occurrences of “replaced by” (screenshot section (e)),
+suggesting a strong prior toward our erroneous phrase.
+
+![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center}
diff --git a/docs/documentation/_static/basic.css b/docs/documentation/_static/basic.css
index f316efcb..30fee9d0 100644
--- a/docs/documentation/_static/basic.css
+++ b/docs/documentation/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/documentation/_static/doctools.js b/docs/documentation/_static/doctools.js
index 4d67807d..d06a71d7 100644
--- a/docs/documentation/_static/doctools.js
+++ b/docs/documentation/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/documentation/_static/language_data.js b/docs/documentation/_static/language_data.js
index 367b8ed8..250f5665 100644
--- a/docs/documentation/_static/language_data.js
+++ b/docs/documentation/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,7 @@
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, if available */
+/* Non-minified version is copied as a separate JS file, is available */
 
 /**
  * Porter Stemmer
diff --git a/docs/documentation/_static/pygments.css b/docs/documentation/_static/pygments.css
index 02b4b128..c2e07c71 100644
--- a/docs/documentation/_static/pygments.css
+++ b/docs/documentation/_static/pygments.css
@@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /*
 body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
 body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
 body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
-body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */
+body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */
 body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
 body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */
+body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */
 body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
 body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */
 body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */
 body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
 body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
 body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
-body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */
+body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */
 body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */
 body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */
 body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
@@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol
 body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
 body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
 body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
-body:not([data-theme="light"]) .highlight .gd { color: #ff3a3a } /* Generic.Deleted */
+body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */
 body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
 body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-body:not([data-theme="light"]) .highlight .gr { color: #ff3a3a } /* Generic.Error */
+body:not([data-theme="light"]) .highlight .gr { color: #d22323 } /* Generic.Error */
 body:not([data-theme="light"]) .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
 body:not([data-theme="light"]) .highlight .gi { color: #589819 } /* Generic.Inserted */
 body:not([data-theme="light"]) .highlight .go { color: #cccccc } /* Generic.Output */
 body:not([data-theme="light"]) .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
 body:not([data-theme="light"]) .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
 body:not([data-theme="light"]) .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
-body:not([data-theme="light"]) .highlight .gt { color: #ff3a3a } /* Generic.Traceback */
+body:not([data-theme="light"]) .highlight .gt { color: #d22323 } /* Generic.Traceback */
 body:not([data-theme="light"]) .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */
 body:not([data-theme="light"]) .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */
 body:not([data-theme="light"]) .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
diff --git a/docs/documentation/_static/scripts/furo.js b/docs/documentation/_static/scripts/furo.js
index 0267c7e1..32e7c05b 100644
--- a/docs/documentation/_static/scripts/furo.js
+++ b/docs/documentation/_static/scripts/furo.js
@@ -1,3 +1,3 @@
 /*! For license information please see furo.js.LICENSE.txt */
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diff --git a/docs/documentation/_static/scripts/furo.js.map b/docs/documentation/_static/scripts/furo.js.map
index c3b37aaa..47053022 100644
--- a/docs/documentation/_static/scripts/furo.js.map
+++ b/docs/documentation/_static/scripts/furo.js.map
@@ -1 +1 @@
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get default export","webpack:///webpack/runtime/define property getters","webpack:///webpack/runtime/global","webpack:///webpack/runtime/hasOwnProperty shorthand","webpack:///./src/furo/assets/scripts/furo.js"],"sourcesContent":["/*!\n * gumshoejs v5.1.2 (patched by @pradyunsg)\n * A simple, framework-agnostic scrollspy script.\n * (c) 2019 Chris Ferdinandi\n * MIT License\n * http://github.com/cferdinandi/gumshoe\n */\n\n(function (root, factory) {\n  if (typeof define === \"function\" && define.amd) {\n    define([], function () {\n      return factory(root);\n    });\n  } else if (typeof exports === \"object\") {\n    module.exports = factory(root);\n  } else {\n    root.Gumshoe = factory(root);\n  }\n})(\n  typeof global !== \"undefined\"\n    ? global\n    : typeof window !== \"undefined\"\n      ? window\n      : this,\n  function (window) {\n    \"use strict\";\n\n    //\n    // Defaults\n    //\n\n    var defaults = {\n      // Active classes\n      navClass: \"active\",\n      contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) 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contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) {\n          if (current) {\n            deactivate(current, settings);\n            current = null;\n          }\n          return;\n        }\n\n        // If the active content is the one currently active, do nothing\n        if (current && active.content === current.content) return;\n\n        // Deactivate the current content and activate the new content\n        deactivate(current, settings);\n        activate(active, settings);\n\n        // Update the currently active content\n        current = active;\n      };\n\n      /**\n       * Detect the active content on scroll\n       * Debounced for performance\n       */\n      var scrollHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(publicAPIs.detect);\n      };\n\n      /**\n       * Update content sorting on resize\n       * Debounced for performance\n       */\n      var resizeHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(function () {\n          sortContents(contents);\n          publicAPIs.detect();\n        });\n      };\n\n      /**\n       * Destroy the current instantiation\n       */\n      publicAPIs.destroy = function () {\n        // Undo DOM changes\n        if (current) {\n          deactivate(current, settings);\n        }\n\n        // Remove event listeners\n        window.removeEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.removeEventListener(\"resize\", resizeHandler, false);\n        }\n\n        // Reset variables\n        contents = null;\n        navItems = null;\n        current = null;\n        timeout = null;\n        settings = null;\n      };\n\n      /**\n       * Initialize the current instantiation\n       */\n      var init = function () {\n        // Merge user options into defaults\n        settings = extend(defaults, options || {});\n\n        // Setup variables based on the current DOM\n        publicAPIs.setup();\n\n        // Find the currently active content\n        publicAPIs.detect();\n\n        // Setup event listeners\n        window.addEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.addEventListener(\"resize\", resizeHandler, false);\n        }\n      };\n\n      //\n      // Initialize and return the public APIs\n      //\n\n      init();\n      return publicAPIs;\n    };\n\n    //\n    // Return the Constructor\n    //\n\n    return Constructor;\n  },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n  if (Math.floor(header.getBoundingClientRect().top) == 0) {\n    header.classList.add(\"scrolled\");\n  } else {\n    header.classList.remove(\"scrolled\");\n  }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n  if (positionY < GO_TO_TOP_OFFSET) {\n    document.documentElement.classList.remove(\"show-back-to-top\");\n  } else {\n    if (positionY < lastScrollTop) {\n      document.documentElement.classList.add(\"show-back-to-top\");\n    } else if (positionY > lastScrollTop) {\n      document.documentElement.classList.remove(\"show-back-to-top\");\n    }\n  }\n  lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // top of page.\n  if (positionY == 0) {\n    tocScroll.scrollTo(0, 0);\n  } else if (\n    // bottom of page.\n    Math.ceil(positionY) >=\n    Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n  ) {\n    tocScroll.scrollTo(0, tocScroll.scrollHeight);\n  } else {\n    // somewhere in the middle.\n    const current = document.querySelector(\".scroll-current\");\n    if (current == null) {\n      return;\n    }\n\n    // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n    // // scroll the currently \"active\" heading in toc, into view.\n    // const rect = current.getBoundingClientRect();\n    // if (0 > rect.top) {\n    //   current.scrollIntoView(true); // the argument is \"alignTop\"\n    // } else if (rect.bottom > window.innerHeight) {\n    //   current.scrollIntoView(false);\n    // }\n  }\n}\n\nfunction scrollHandler(positionY) {\n  scrollHandlerForHeader();\n  scrollHandlerForBackToTop(positionY);\n  scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  document.body.dataset.theme = mode;\n  localStorage.setItem(\"theme\", mode);\n  console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n  const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n  const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n  if (prefersDark) {\n    // Auto (dark) -> Light -> Dark\n    if (currentTheme === \"auto\") {\n      setTheme(\"light\");\n    } else if (currentTheme == \"light\") {\n      setTheme(\"dark\");\n    } else {\n      setTheme(\"auto\");\n    }\n  } else {\n    // Auto (light) -> Dark -> Light\n    if (currentTheme === \"auto\") {\n      setTheme(\"dark\");\n    } else if (currentTheme == \"dark\") {\n      setTheme(\"light\");\n    } else {\n      setTheme(\"auto\");\n    }\n  }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n  // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n  let last_known_scroll_position = 0;\n  let ticking = false;\n\n  window.addEventListener(\"scroll\", function (e) {\n    last_known_scroll_position = window.scrollY;\n\n    if (!ticking) {\n      window.requestAnimationFrame(function () {\n        scrollHandler(last_known_scroll_position);\n        ticking = false;\n      });\n\n      ticking = true;\n    }\n  });\n  window.scroll();\n}\n\nfunction setupScrollSpy() {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // Scrollspy -- highlight table on contents, based on scroll\n  new Gumshoe(\".toc-tree a\", {\n    reflow: true,\n    recursive: true,\n    navClass: \"scroll-current\",\n    offset: () => {\n      let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n      return header.getBoundingClientRect().height + 0.5 * rem + 1;\n    },\n  });\n}\n\nfunction setupTheme() {\n  // Attach event handlers for toggling themes\n  const buttons = document.getElementsByClassName(\"theme-toggle\");\n  Array.from(buttons).forEach((btn) => {\n    btn.addEventListener(\"click\", cycleThemeOnce);\n  });\n}\n\nfunction setup() {\n  setupTheme();\n  setupScrollHandler();\n  setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n  document.body.parentNode.classList.remove(\"no-js\");\n\n  header = document.querySelector(\"header\");\n  tocScroll = document.querySelector(\".toc-scroll\");\n\n  setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/docs/documentation/_static/searchtools.js b/docs/documentation/_static/searchtools.js
index 92da3f8b..7918c3fa 100644
--- a/docs/documentation/_static/searchtools.js
+++ b/docs/documentation/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -99,7 +99,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, anchor)
+            Search.makeSearchSummary(data, searchTerms)
           );
         // highlight search terms in the summary
         if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
@@ -116,8 +116,8 @@ const _finishSearch = (resultCount) => {
     );
   else
     Search.status.innerText = _(
-      "Search finished, found ${resultCount} page(s) matching the search query."
-    ).replace('${resultCount}', resultCount);
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
 };
 const _displayNextItem = (
   results,
@@ -137,22 +137,6 @@ const _displayNextItem = (
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
-  const leftScore = a[4];
-  const rightScore = b[4];
-  if (leftScore === rightScore) {
-    // same score: sort alphabetically
-    const leftTitle = a[1].toLowerCase();
-    const rightTitle = b[1].toLowerCase();
-    if (leftTitle === rightTitle) return 0;
-    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-  }
-  return leftScore > rightScore ? 1 : -1;
-};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -176,26 +160,13 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString, anchor) => {
+  htmlToText: (htmlString) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlinks", "script", "style"]) {
-      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
-    }
-    if (anchor) {
-      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
-      if (anchorContent) return anchorContent.textContent;
-
-      console.warn(
-        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
-      );
-    }
-
-    // if anchor not specified or not found, fall back to main content
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent) return docContent.textContent;
-
+    if (docContent !== undefined) return docContent.textContent;
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
     );
     return "";
   },
@@ -268,7 +239,16 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  _parseQuery: (query) => {
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -304,32 +284,16 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
-    // Collect multiple result groups to be sorted separately and then ordered.
-    // Each is an array of [docname, title, anchor, descr, score, filename].
-    const normalResults = [];
-    const nonMainIndexResults = [];
-
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase().trim();
+    const queryLower = query.toLowerCase();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
           let score = Math.round(100 * queryLower.length / title.length)
-          normalResults.push([
+          results.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
@@ -344,47 +308,46 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id, isMain] of foundEntries) {
-          const score = Math.round(100 * queryLower.length / entry.length);
-          const result = [
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-          ];
-          if (isMain) {
-            normalResults.push(result);
-          } else {
-            nonMainIndexResults.push(result);
-          }
+          ]);
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+      results.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
-      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
-    }
-
-    // Sort each group of results by score and then alphabetically by name.
-    normalResults.sort(_orderResultsByScoreThenName);
-    nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
-    // Combine the result groups in (reverse) order.
-    // Non-main index entries are typically arbitrary cross-references,
-    // so display them after other results.
-    let results = [...nonMainIndexResults, ...normalResults];
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -398,12 +361,7 @@ const Search = {
       return acc;
     }, []);
 
-    return results.reverse();
-  },
-
-  query: (query) => {
-    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
-    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+    results = results.reverse();
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
@@ -508,18 +466,14 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        if (!terms.hasOwnProperty(word)) {
-          Object.keys(terms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: terms[term], score: Scorer.partialTerm });
-          });
-        }
-        if (!titleTerms.hasOwnProperty(word)) {
-          Object.keys(titleTerms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
-          });
-        }
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
       }
 
       // no match but word was a required one
@@ -542,8 +496,9 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (!fileMap.has(file)) fileMap.set(file, [word]);
-        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
       });
     });
 
@@ -594,8 +549,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords, anchor) => {
-    const text = Search.htmlToText(htmlText, anchor);
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();
diff --git a/docs/documentation/_static/styles/furo.css b/docs/documentation/_static/styles/furo.css
index e3d4e57b..3d29a218 100644
--- a/docs/documentation/_static/styles/furo.css
+++ b/docs/documentation/_static/styles/furo.css
@@ -1,2 +1,2 @@
-/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{line-height:1.15;-webkit-text-size-adjust:100%}body{margin:0}main{display:block}h1{font-size:2em;margin:.67em 0}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-family:monospace,monospace;font-size:1em}a{background-color:transparent}abbr[title]{border-bottom:none;text-decoration:underline;text-decoration:underline dotted}b,strong{font-weight:bolder}code,kbd,samp{font-family:monospace,monospace;font-size:1em}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}img{border-style:none}button,input,optgroup,select,textarea{font-family:inherit;font-size:100%;line-height:1.15;margin:0}button,input{overflow:visible}button,select{text-transform:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{border-style:none;padding:0}[type=button]:-moz-focusring,[type=reset]:-moz-focusring,[type=submit]:-moz-focusring,button:-moz-focusring{outline:1px dotted ButtonText}fieldset{padding:.35em .75em .625em}legend{box-sizing:border-box;color:inherit;display:table;max-width:100%;padding:0;white-space:normal}progress{vertical-align:baseline}textarea{overflow:auto}[type=checkbox],[type=radio]{box-sizing:border-box;padding:0}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}details{display:block}summary{display:list-item}[hidden],template{display:none}@media print{.content-icon-container,.headerlink,.mobile-header,.related-pages{display:none!important}.highlight{border:.1pt solid 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Console,monospace;--font-stack--headings:var(--font-stack);--font-size--normal:100%;--font-size--small:87.5%;--font-size--small--2:81.25%;--font-size--small--3:75%;--font-size--small--4:62.5%;--sidebar-caption-font-size:var(--font-size--small--2);--sidebar-item-font-size:var(--font-size--small);--sidebar-search-input-font-size:var(--font-size--small);--toc-font-size:var(--font-size--small--3);--toc-font-size--mobile:var(--font-size--normal);--toc-title-font-size:var(--font-size--small--4);--admonition-font-size:0.8125rem;--admonition-title-font-size:0.8125rem;--code-font-size:var(--font-size--small--2);--api-font-size:var(--font-size--small);--header-height:calc(var(--sidebar-item-line-height) + var(--sidebar-item-spacing-vertical)*4);--header-padding:0.5rem;--sidebar-tree-space-above:1.5rem;--sidebar-caption-space-above:1rem;--sidebar-item-line-height:1rem;--sidebar-item-spacing-vertical:0.5rem;--sidebar-item-spacing-horizontal:1rem;--sidebar-item-height:calc(var(--sidebar-item-line-height) + var(--sidebar-item-spacing-vertical)*2);--sidebar-expander-width:var(--sidebar-item-height);--sidebar-search-space-above:0.5rem;--sidebar-search-input-spacing-vertical:0.5rem;--sidebar-search-input-spacing-horizontal:0.5rem;--sidebar-search-input-height:1rem;--sidebar-search-icon-size:var(--sidebar-search-input-height);--toc-title-padding:0.25rem 0;--toc-spacing-vertical:1.5rem;--toc-spacing-horizontal:1.5rem;--toc-item-spacing-vertical:0.4rem;--toc-item-spacing-horizontal:1rem;--icon-search:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" viewBox="0 0 24 24"><path stroke="none" d="M0 0h24v24H0z"/><circle cx="10" cy="10" r="7"/><path d="m21 21-6-6"/></svg>');--icon-pencil:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75z"/></svg>');--icon-abstract:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 5h16v2H4zm0 4h16v2H4zm0 4h16v2H4zm0 4h10v2H4z"/></svg>');--icon-info:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2"/></svg>');--icon-flame:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.8.8 0 0 1-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25zm-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27z"/></svg>');--icon-question:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m15.07 11.25-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 0 0-2-2 2 2 0 0 0-2 2H8a4 4 0 0 1 4-4 4 4 0 0 1 4 4 3.2 3.2 0 0 1-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 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body .only-dark{display:none!important}@media not 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body[data-theme=dark] .only-light{display:none!important}body[data-theme=dark] 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto-light\n    display: block\n\n  @media (prefers-color-scheme: dark)\n    .theme-toggle svg.theme-icon-when-auto-dark\n      display: block\n    .theme-toggle svg.theme-icon-when-auto-light\n      display: none\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n  --font-stack--headings: var(--font-stack);\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #6b6f76; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #0a4bff;\n  --color-brand-content: #2757dd;\n  --color-brand-visited: #872ee0;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n\n  --color-api-added: #21632c;\n  --color-api-added-border: #38a84d;\n  --color-api-changed: #046172;\n  --color-api-changed-border: #06a1bc;\n  --color-api-deprecated: #605706;\n  --color-api-deprecated-border: #f0d90f;\n  --color-api-removed: #b30000;\n  --color-api-removed-border: #ff5c5c;\n\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline--hover: var(--color-foreground-border);\n\n  --color-link--visited: var(--color-brand-visited);\n  --color-link-underline--visited: var(--color-background-border);\n  --color-link--visited--hover: var(--color-brand-visited);\n  --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #cfd0d0; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #3d94ff;\n  --color-brand-content: #5ca5ff;\n  --color-brand-visited: #b27aeb;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  --color-api-added: #3db854;\n  --color-api-added-border: #267334;\n  --color-api-changed: #09b0ce;\n  --color-api-changed-border: #056d80;\n  --color-api-deprecated: #b1a10b;\n  --color-api-deprecated-border: #6e6407;\n  --color-api-removed: #ff7575;\n  --color-api-removed-border: #b03b3b;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-family: var(--font-stack--headings)\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:visited\n    color: var(--color-link--visited)\n    text-decoration-color: var(--color-link-underline--visited)\n    &:hover\n      color: var(--color-link--visited--hover)\n      text-decoration-color: var(--color-link-underline--visited--hover)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link--hover)\n      text-decoration-color: var(--color-link-underline--hover)\n      &:visited\n        color: var(--color-link--visited--hover)\n        text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n.skip-to-content\n  position: fixed\n  padding: 1rem\n  border-radius: 1rem\n  left: 0.25rem\n  top: 0.25rem\n  z-index: 40\n  background: var(--color-background-primary)\n  color: var(--color-foreground-primary)\n\n  transform: translateY(-200%)\n  transition: transform 300ms ease-in-out\n\n  &:focus-within\n    transform: translateY(0%)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  vertical-align: middle\n\n.theme-toggle\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  vertical-align: middle\n  height: $icon-size\n  width: $icon-size\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  float: left\n  padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: $icon-size\n    width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page, .view-this-page\n    svg\n      color: inherit\n      height: $icon-size\n      width: $icon-size\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n  .theme-toggle-header\n    display: block\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: $icon-size\n    width: $icon-size\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Center the page, and accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n  .content\n    margin-left: auto\n    margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  .content\n    padding: 0 $content-padding--small\n    // Don't float sidebars to the right.\n  article aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 4.25rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n  border-left: 0.1875rem solid\n  border-radius: 0.125rem\n\n  padding-left: 0.75rem\n\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\ndiv.versionadded\n  border-color: var(--color-api-added-border)\n  .versionmodified\n    color: var(--color-api-added)\n\ndiv.versionchanged\n  border-color: var(--color-api-changed-border)\n  .versionmodified\n    color: var(--color-api-changed)\n\ndiv.deprecated\n  border-color: var(--color-api-deprecated-border)\n  .versionmodified\n    color: var(--color-api-deprecated)\n\ndiv.versionremoved\n  border-color: var(--color-api-removed-border)\n  .versionmodified\n    color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(2.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      color: var(--color-sidebar-link-text)\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current > .reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  a.reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling. Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto\n    display: block\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #646776; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2962ff;\n  --color-brand-content: #2a5adf;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #ffffffcc; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2b8cee;\n  --color-brand-content: #368ce2;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  vertical-align: middle\n\n.theme-toggle\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  vertical-align: middle\n  height: 1rem\n  width: 1rem\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  float: left\n  padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: 1rem\n    width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page svg\n    color: inherit\n    height: 1rem\n    width: 1rem\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n  .theme-toggle-header\n    display: block\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: 1.25rem\n    width: 1.25rem\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: var(--header-height)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Center the page, and accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n  .content\n    margin-left: auto\n    margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  .content\n    padding: 0 $content-padding--small\n    // Don't float sidebars to the right.\n  article aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 3.5rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\n.versionmodified\n  font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(0.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current >.reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  .reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling.Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/docs/documentation/api.html b/docs/documentation/api.html
index dcfbbd60..41efe94a 100644
--- a/docs/documentation/api.html
+++ b/docs/documentation/api.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Frontend Developer Guide" href="frontend_development.html" /><link rel="prev" title="Components and Features" href="components.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>LIT Python API - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
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@@ -69,7 +69,7 @@
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     <title>Light mode</title>
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
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     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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@@ -92,7 +92,7 @@
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     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
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-  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
-    <title>Auto light/dark, in dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/api.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
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+          
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             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
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-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,13 +200,13 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="lit-python-api">
-<h1>LIT Python API<a class="headerlink" href="#lit-python-api" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<h1>LIT Python API<a class="headerlink" href="#lit-python-api" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="design-overview">
-<h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to this heading">¶</a></h2>
+<h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to this heading">#</a></h2>
 <p>LIT is a modular system, comprising a collection of backend components (written
 in Python) and frontend modules (written in TypeScript). Most users will develop
 against the Python API, which is documented below and allows LIT to be extended
@@ -294,7 +231,7 @@ <h2>Design Overview<a class="headerlink" href="#design-overview" title="Link to
 <a class="reference internal" href="frontend_development.html"><span class="std std-doc">frontend developer guide</span></a>.</p>
 </section>
 <section id="adding-models-and-data">
-<h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" title="Link to this heading">¶</a></h2>
+<h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" title="Link to this heading">#</a></h2>
 <p>To run LIT with your own models and data, you can create a custom <code class="docutils literal notranslate"><span class="pre">demo.py</span></code>
 script that passes these to the LIT server. For example:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">_</span><span class="p">):</span>
@@ -324,7 +261,7 @@ <h2>Adding Models and Data<a class="headerlink" href="#adding-models-and-data" t
 <p>For pre-built <code class="docutils literal notranslate"><span class="pre">demo.py</span></code> examples, check out
 <a class="reference external" href="https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples">https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples</a></p>
 <section id="validating-models-and-data">
-<h3>Validating Models and Data<a class="headerlink" href="#validating-models-and-data" title="Link to this heading">¶</a></h3>
+<h3>Validating Models and Data<a class="headerlink" href="#validating-models-and-data" title="Link to this heading">#</a></h3>
 <p>Datasets and models can optionally be validated by LIT to ensure that dataset
 examples match their spec and that model output values match their spec.
 This can be very helpful during development of new model and dataset wrappers
@@ -342,7 +279,7 @@ <h3>Validating Models and Data<a class="headerlink" href="#validating-models-and
 </section>
 </section>
 <section id="datasets">
-<h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">¶</a></h2>
+<h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">#</a></h2>
 <p>Datasets (<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a>) are
 just a list of examples, with associated type information following LIT’s
 <a class="reference internal" href="#type-system">type system</a>.</p>
@@ -404,7 +341,7 @@ <h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">
 these features for slicing, binning, etc., and they will also be available to
 interpretation components such as custom metrics.</p>
 <section id="transformations">
-<h3>Transformations<a class="headerlink" href="#transformations" title="Link to this heading">¶</a></h3>
+<h3>Transformations<a class="headerlink" href="#transformations" title="Link to this heading">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class also supports a limited set of transformations, similar to
 TensorFlow’s
 <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset">tf.data.Dataset</a>
@@ -423,7 +360,7 @@ <h3>Transformations<a class="headerlink" href="#transformations" title="Link to
 </section>
 </section>
 <section id="models">
-<h2>Models<a class="headerlink" href="#models" title="Link to this heading">¶</a></h2>
+<h2>Models<a class="headerlink" href="#models" title="Link to this heading">#</a></h2>
 <p>Models (<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code></a>) are
 functions which take inputs and produce outputs, with associated type
 information following LIT’s <a class="reference internal" href="#type-system">type system</a>. The core API consists
@@ -485,7 +422,7 @@ <h2>Models<a class="headerlink" href="#models" title="Link to this heading">¶</
 <p>If your model is on a remote server, consider using the <code class="docutils literal notranslate"><span class="pre">BatchedRemoteModel</span></code>
 base class, which implements parallel batched requests using a thread pool.</p>
 <section id="adding-more-outputs">
-<h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="Link to this heading">¶</a></h3>
+<h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="Link to this heading">#</a></h3>
 <p>The above example defined a black-box model, with predictions but no access to
 internals. If we want a richer view into the model’s behavior, we can add
 additional return fields corresponding to hidden-state activations, gradients,
@@ -534,7 +471,7 @@ <h3>Adding more outputs<a class="headerlink" href="#adding-more-outputs" title="
 types and their semantics.</p>
 </section>
 <section id="optional-inputs">
-<h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to this heading">¶</a></h3>
+<h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to this heading">#</a></h3>
 <p>By default, LIT treats <code class="docutils literal notranslate"><span class="pre">input_spec</span></code> fields as required. However, this can be set
 to false if you wish to define optional model inputs. For example, a model that
 can accept pre-tokenized inputs might have the following spec:</p>
@@ -558,7 +495,7 @@ <h3>Optional inputs<a class="headerlink" href="#optional-inputs" title="Link to
 </section>
 </section>
 <section id="interpretation-components">
-<h2>Interpretation Components<a class="headerlink" href="#interpretation-components" title="Link to this heading">¶</a></h2>
+<h2>Interpretation Components<a class="headerlink" href="#interpretation-components" title="Link to this heading">#</a></h2>
 <p>Backend interpretation components include metrics, salience maps, visualization
 aids like <a class="reference external" href="https://umap-learn.readthedocs.io/en/latest/">UMAP</a>, and
 counterfactual generator plug-ins.</p>
@@ -572,7 +509,7 @@ <h2>Interpretation Components<a class="headerlink" href="#interpretation-compone
 additionally requires a <code class="docutils literal notranslate"><span class="pre">TokenEmbeddings</span></code> input and corresponding output, as
 well as a label field <code class="docutils literal notranslate"><span class="pre">Target</span></code> to pin the gradient target to the same class as
 an input and corresponding output. See the
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">GLUE models class</a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py">GLUE models class</a>
 for an example of these spec requirements.</p>
 <p>The core API involves implementing the <code class="docutils literal notranslate"><span class="pre">run()</span></code> method:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
@@ -631,7 +568,7 @@ <h2>Interpretation Components<a class="headerlink" href="#interpretation-compone
 benefit is flexibility - Python can be used to specify complex dependencies
 between fields, and multiple outputs can be easily supported in a loop.</p>
 <section id="metrics">
-<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶</a></h3>
+<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
 <p>For metrics, the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/metrics.py"><code class="docutils literal notranslate"><span class="pre">SimpleMetrics</span></code></a>
 class implements the spec-matching and input-unpacking logic to satisfy the
@@ -665,7 +602,7 @@ <h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶
 on the unpacked values.</p>
 </section>
 <section id="generators">
-<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">¶</a></h3>
+<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">#</a></h3>
 <p>Conceptually, a generator is just an interpreter that returns new input
 examples. These may depend on the input only, as for techniques such as back-
 translation, or can involve feedback from the model, such as for adversarial
@@ -701,7 +638,7 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
 through <code class="docutils literal notranslate"><span class="pre">config</span></code>, such as the list of substitutions for the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py">word replacer</a>.</p>
 <section id="backtranslator-generator">
-<h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generator" title="Link to this heading">¶</a></h4>
+<h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generator" title="Link to this heading">#</a></h4>
 <p>The <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/backtranslator.py">backtranslator</a>
 generator translates text segment inputs into foreign languages and back to the
 source language in order to create paraphrases.
@@ -715,7 +652,7 @@ <h4>Backtranslator Generator<a class="headerlink" href="#backtranslator-generato
 </section>
 </section>
 <section id="configuration-ui">
-<h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link to this heading">¶</a></h3>
+<h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link to this heading">#</a></h3>
 <p>Interpreter components support an optional <code class="docutils literal notranslate"><span class="pre">config</span></code> option to specify run-time
 options, such as the number of samples for LIME or the pivot languages for
 back-translation. LIT provides a simple DSL to define these options, which will
@@ -771,7 +708,7 @@ <h3>Configuration UI<a class="headerlink" href="#configuration-ui" title="Link t
 </section>
 </section>
 <section id="type-system">
-<h2>Type System<a class="headerlink" href="#type-system" title="Link to this heading">¶</a></h2>
+<h2>Type System<a class="headerlink" href="#type-system" title="Link to this heading">#</a></h2>
 <p>LIT passes data around (e.g., between the server and the web app) as flat
 records with <code class="docutils literal notranslate"><span class="pre">string</span></code> keys. In Python types these are <code class="docutils literal notranslate"><span class="pre">Mapping[str,</span> <span class="pre">...]</span></code> and in
 TypeScript types these are <code class="docutils literal notranslate"><span class="pre">{[key:</span> <span class="pre">string]:</span> <span class="pre">unknown}</span></code>. LIT serializes these
@@ -794,7 +731,7 @@ <h2>Type System<a class="headerlink" href="#type-system" title="Link to this hea
 inheritance; the canonical types can be found in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>, with
 parallel implementations in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts">lit_types.ts</a>.</p>
 <section id="conventions">
-<h3>Conventions<a class="headerlink" href="#conventions" title="Link to this heading">¶</a></h3>
+<h3>Conventions<a class="headerlink" href="#conventions" title="Link to this heading">#</a></h3>
 <p>LIT supports several different “kinds” of <code class="docutils literal notranslate"><span class="pre">Spec</span></code>s (input vs output vs meta,
 etc.), and their use in context has specific implications, described
 per base class below.</p>
@@ -810,7 +747,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 in the dataset loading section of the web app’s Global Settings.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.model.Model</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.model.Model</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.input_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the shape required of all records
 passed into the <code class="docutils literal notranslate"><span class="pre">Model.predict()</span></code> function via the <code class="docutils literal notranslate"><span class="pre">inputs</span></code> argument. LIT
@@ -825,7 +762,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 the model loading section of the web app’s Global Settings.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.[Interpreter</span> <span class="pre">|</span> <span class="pre">Generator]</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.[Interpreter</span> <span class="pre">|</span> <span class="pre">Generator]</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.config_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the user-configurable parameters
 for running this component. Returning an empty <code class="docutils literal notranslate"><span class="pre">Spec</span></code> means that this
@@ -841,7 +778,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 because each input may enable the generation of one or more new examples.</p></li>
 </ul>
 </li>
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.Metrics</span></code></a></p>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components.Metrics</span></code></a></p>
 <ul>
 <li><p><strong><code class="docutils literal notranslate"><span class="pre">.config_spec()</span> <span class="pre">-&gt;</span> <span class="pre">Spec</span></code></strong> describes the user-configurable parameters
 for running this component. Returning an empty <code class="docutils literal notranslate"><span class="pre">Spec</span></code> means that this
@@ -881,7 +818,7 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 <li><p>A field that appears in <em>both</em> the model’s input and output specs is assumed
 to represent the same value. This pattern is used for model-based input
 manipulation. For example, a
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">language model</a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py">language model</a>
 might output <code class="docutils literal notranslate"><span class="pre">'tokens':</span> <span class="pre">lit_types.Tokens(...)</span></code>, and accept as (optional)
 input <code class="docutils literal notranslate"><span class="pre">'tokens':</span> <span class="pre">lit_types.Tokens(required=False,</span> <span class="pre">...)</span></code>. An interpretability
 component could take output from the former, swap one or more tokens (e.g.
@@ -890,17 +827,17 @@ <h3>Conventions<a class="headerlink" href="#conventions" title="Link to this hea
 </ul>
 </section>
 <section id="compatibility-checks">
-<h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title="Link to this heading">¶</a></h3>
+<h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title="Link to this heading">#</a></h3>
 <p>LIT’s type system plays a critical role in ensuring reliability of and
 interoperability between the <code class="docutils literal notranslate"><span class="pre">Model</span></code>, <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code>, <code class="docutils literal notranslate"><span class="pre">Generator</span></code>, and
 <code class="docutils literal notranslate"><span class="pre">Metrics</span></code> classes:</p>
 <ul class="simple">
 <li><p>The <strong>Model-Dataset compatibility check</strong> ensures that the
 <code class="docutils literal notranslate"><span class="pre">Model.input_spec()</span></code> is a subset of the <code class="docutils literal notranslate"><span class="pre">Dataset.spec()</span></code>. The base
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code> class</a> provides a robust and universal implementation of
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">Model</span></code> class</a> provides a robust and universal implementation of
 this check in the <code class="docutils literal notranslate"><span class="pre">is_compatible_with_dataset()</span></code> API, but you can override
 this method in your <code class="docutils literal notranslate"><span class="pre">Model</span></code> subclass if you so choose.</p></li>
-<li><p>All <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components</span></code> classes</a> provide an
+<li><p>All <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py"><code class="docutils literal notranslate"><span class="pre">lit_nlp.api.components</span></code> classes</a> provide an
 <code class="docutils literal notranslate"><span class="pre">is_compatible</span></code> API to check their compatibility against <code class="docutils literal notranslate"><span class="pre">Model</span></code>s and
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s, as appropriate. For example, the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py"><code class="docutils literal notranslate"><span class="pre">WordReplacer</span></code> generator</a> only checks against the <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>
@@ -918,9 +855,11 @@ <h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title
 this can cause jitter (UI modules appearing, disappearing, reordering, resizing,
 etc.) when switching between models or datasets with heterogeneous <code class="docutils literal notranslate"><span class="pre">Spec</span></code>s.</p>
 <p>When implementing your own LIT components and modules, you can use
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py"><code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys()</span></code></a> (Python) and
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">findSpecKeys()</span></code></a> (TypeScript) to identify fields of interest in a
-<code class="docutils literal notranslate"><span class="pre">Spec</span></code>. These methods recognize and respect subclasses. For example,
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys()</span></code></a>
+(Python) and
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts"><code class="docutils literal notranslate"><span class="pre">findSpecKeys()</span></code></a>
+(TypeScript) to identify fields of interest in a <code class="docutils literal notranslate"><span class="pre">Spec</span></code>. These methods recognize
+and respect subclasses. For example,
 <code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys(spec,</span> <span class="pre">Scalar)</span></code> will also match any <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>
 fields, but <code class="docutils literal notranslate"><span class="pre">utils.find_spec_keys(spec,</span> <span class="pre">RegressionScore)</span></code> will not return all
 <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> fields in the <code class="docutils literal notranslate"><span class="pre">Spec</span></code>.</p>
@@ -933,7 +872,7 @@ <h3>Compatibility Checks<a class="headerlink" href="#compatibility-checks" title
 herrings.</p>
 </section>
 <section id="an-in-depth-example">
-<h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="Link to this heading">¶</a></h3>
+<h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="Link to this heading">#</a></h3>
 <p>Consider the following example from the <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">MNLI demo</a>. The
 <a class="reference external" href="https://cims.nyu.edu/~sbowman/multinli/">MultiNLI</a> dataset might define the following <code class="docutils literal notranslate"><span class="pre">Spec</span></code>.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Dataset.spec()</span>
@@ -999,14 +938,9 @@ <h3>An In-Depth Example<a class="headerlink" href="#an-in-depth-example" title="
 <p><em>See the <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples">examples</a> for more.</em></p>
 </section>
 <section id="available-types">
-<h3>Available types<a class="headerlink" href="#available-types" title="Link to this heading">¶</a></h3>
-<p>The full set of <code class="docutils literal notranslate"><span class="pre">LitType</span></code>s is defined in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>. Numeric types
-such as <code class="docutils literal notranslate"><span class="pre">Integer</span></code> and <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> have predefined ranges that can be overridden
-using corresponding <code class="docutils literal notranslate"><span class="pre">min_val</span></code> and <code class="docutils literal notranslate"><span class="pre">max_val</span></code> attributes as seen in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py">penguin data</a>
-<code class="docutils literal notranslate"><span class="pre">INPUT_SPEC</span></code>. The different types available in LIT are summarized in the table
-below.</p>
+<h3>Available types<a class="headerlink" href="#available-types" title="Link to this heading">#</a></h3>
+<p>The full set of <code class="docutils literal notranslate"><span class="pre">LitType</span></code>s is defined in <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py">types.py</a>. Numeric types such as <code class="docutils literal notranslate"><span class="pre">Integer</span></code> and <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> have predefined ranges that can be overridden using corresponding <code class="docutils literal notranslate"><span class="pre">min_val</span></code> and <code class="docutils literal notranslate"><span class="pre">max_val</span></code> attributes as seen <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438">here</a>. The different types available in LIT are summarized
+in the table below.</p>
 <p>Note: Bracket syntax, such as <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[num_tokens]</span></code>, refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.</p>
 <div class="table-wrapper colwidths-auto docutils container">
@@ -1150,11 +1084,11 @@ <h3>Available types<a class="headerlink" href="#available-types" title="Link to
 </section>
 </section>
 <section id="server-configuration">
-<h2>Server Configuration<a class="headerlink" href="#server-configuration" title="Link to this heading">¶</a></h2>
+<h2>Server Configuration<a class="headerlink" href="#server-configuration" title="Link to this heading">#</a></h2>
 <p>Some properties of the LIT frontend can be configured from Python as
 <strong>arguments to <code class="docutils literal notranslate"><span class="pre">dev_server.Server()</span></code></strong>. These include:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">page_title</span></code>: set a custom page title.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">page_title</span></code>: set a custom page title, such as “Coreference Demo”.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">canonical_url</span></code>: set a “canonical” URL (such as a shortlink) that will be
 used as the base when copying links from the LIT UI.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">default_layout</span></code>: set the default UI layout, by name. See <code class="docutils literal notranslate"><span class="pre">layout.ts</span></code> and
@@ -1174,19 +1108,25 @@ <h2>Server Configuration<a class="headerlink" href="#server-configuration" title
 a <code class="docutils literal notranslate"><span class="pre">description()</span></code> method which can be used to specify a human-readable
 description or help text that will appear in the UI.</p>
 <section id="customizing-the-layout">
-<h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" title="Link to this heading">¶</a></h3>
+<h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" title="Link to this heading">#</a></h3>
 <p>You can specify custom web app layouts from Python via the <code class="docutils literal notranslate"><span class="pre">layouts=</span></code> attribute.
 The value should be a <code class="docutils literal notranslate"><span class="pre">Mapping[str,</span> <span class="pre">LitCanonicalLayout]</span></code>, such as:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">PENGUIN_LAYOUT</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">LitCanonicalLayout</span><span class="p">(</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LM_LAYOUT</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">LitCanonicalLayout</span><span class="p">(</span>
     <span class="n">upper</span><span class="o">=</span><span class="p">{</span>
-        <span class="s1">&#39;Main&#39;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="n">modules</span><span class="o">.</span><span class="n">DiveModule</span><span class="p">,</span>
+        <span class="s2">&quot;Main&quot;</span><span class="p">:</span> <span class="p">[</span>
+            <span class="n">modules</span><span class="o">.</span><span class="n">EmbeddingsModule</span><span class="p">,</span>
             <span class="n">modules</span><span class="o">.</span><span class="n">DataTableModule</span><span class="p">,</span>
             <span class="n">modules</span><span class="o">.</span><span class="n">DatapointEditorModule</span><span class="p">,</span>
         <span class="p">]</span>
     <span class="p">},</span>
-    <span class="n">lower</span><span class="o">=</span><span class="n">layout</span><span class="o">.</span><span class="n">STANDARD_LAYOUT</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
-    <span class="n">description</span><span class="o">=</span><span class="s1">&#39;Custom layout for the Palmer Penguins demo.&#39;</span><span class="p">,</span>
+    <span class="n">lower</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">&quot;Predictions&quot;</span><span class="p">:</span> <span class="p">[</span>
+            <span class="n">modules</span><span class="o">.</span><span class="n">LanguageModelPredictionModule</span><span class="p">,</span>
+            <span class="n">modules</span><span class="o">.</span><span class="n">ConfusionMatrixModule</span><span class="p">,</span>
+        <span class="p">],</span>
+        <span class="s2">&quot;Counterfactuals&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">modules</span><span class="o">.</span><span class="n">GeneratorModule</span><span class="p">],</span>
+    <span class="p">},</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Custom layout for language models.&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -1195,12 +1135,13 @@ <h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" t
     <span class="n">models</span><span class="p">,</span>
     <span class="n">datasets</span><span class="p">,</span>
     <span class="c1"># other args...</span>
-    <span class="n">layouts</span><span class="o">=</span><span class="n">layout</span><span class="o">.</span><span class="n">DEFAULT_LAYOUTS</span> <span class="o">|</span> <span class="p">{</span><span class="s1">&#39;penguins&#39;</span><span class="p">:</span> <span class="n">PENGUIN_LAYOUT</span><span class="p">},</span>
-    <span class="n">default_layout</span><span class="o">=</span><span class="s1">&#39;penguins&#39;</span><span class="p">,</span>
+    <span class="n">layouts</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lm&quot;</span><span class="p">:</span> <span class="n">LM_LAYOUT</span><span class="p">},</span>
     <span class="o">**</span><span class="n">server_flags</span><span class="o">.</span><span class="n">get_flags</span><span class="p">())</span>
 <span class="k">return</span> <span class="n">lit_demo</span><span class="o">.</span><span class="n">serve</span><span class="p">()</span>
 </pre></div>
 </div>
+<p>For a full example, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
 <p>You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py"><code class="docutils literal notranslate"><span class="pre">layout.py</span></code></a>. A
@@ -1230,7 +1171,7 @@ <h3>Customizing the Layout<a class="headerlink" href="#customizing-the-layout" t
 </section>
 </section>
 <section id="accessing-the-lit-ui-in-notebooks">
-<h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-lit-ui-in-notebooks" title="Link to this heading">¶</a></h2>
+<h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-lit-ui-in-notebooks" title="Link to this heading">#</a></h2>
 <p>As an alternative to running a LIT server and connecting to it through a web
 browser, LIT can be used directly inside of python notebook environments, such
 as <a class="reference external" href="https://colab.research.google.com/">Colab</a> and
@@ -1256,20 +1197,20 @@ <h2>Accessing the LIT UI in Notebooks<a class="headerlink" href="#accessing-the-
 <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb">example notebook</a>.</p>
 </section>
 <section id="using-lit-components-outside-of-lit">
-<h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-components-outside-of-lit" title="Link to this heading">¶</a></h2>
+<h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-components-outside-of-lit" title="Link to this heading">#</a></h2>
 <p>All LIT Python components (models, datasets, interpreters, metrics, generators,
 etc.) are standalone classes that do not depend on the serving framework. You
 can easily use them from Colab, in scripts, or in your libraries. This can
 also be handy for development, as you can test new models or components without
 needing to reload the server or click the UI.</p>
 <p>For example, to view examples in a dataset:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.glue</span> <span class="kn">import</span> <span class="n">data</span> <span class="k">as</span> <span class="n">glue_data</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">glue_data</span><span class="o">.</span><span class="n">SST2Data</span><span class="p">(</span><span class="s1">&#39;validation&#39;</span><span class="p">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.datasets</span> <span class="kn">import</span> <span class="n">glue</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">glue</span><span class="o">.</span><span class="n">SST2Data</span><span class="p">(</span><span class="s1">&#39;validation&#39;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">examples</span><span class="p">)</span>  <span class="c1"># list of records {&quot;sentence&quot;: ..., &quot;label&quot;: ...}</span>
 </pre></div>
 </div>
 <p>And to run inference on a few of them:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.glue</span> <span class="kn">import</span> <span class="n">models</span> <span class="k">as</span> <span class="n">glue_models</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lit_nlp.examples.models</span> <span class="kn">import</span> <span class="n">glue_models</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">glue_models</span><span class="o">.</span><span class="n">SST2Model</span><span class="p">(</span><span class="s2">&quot;/path/to/model/files&quot;</span><span class="p">)</span>
 <span class="n">preds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">examples</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
@@ -1416,8 +1357,8 @@ <h2>Using LIT components outside of LIT<a class="headerlink" href="#using-lit-co
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/components.html b/docs/documentation/components.html
index 27f7401b..bfbf62a6 100644
--- a/docs/documentation/components.html
+++ b/docs/documentation/components.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="LIT Python API" href="api.html" /><link rel="prev" title="UI Guide" href="ui_guide.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Components and Features - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
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     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
-      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
-      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
-      <line x1="14.5" y1="3.25" x2="14.5" y2="1.25"/>
-      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
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-      <line x1="10.044" y1="14.006" x2="8.63" y2="15.42"/>
-      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
-      <circle cx="14.5" cy="9.55" r="3.6"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
-    <title>Auto light/dark, in dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
-      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
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 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
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 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
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         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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@@ -243,17 +186,11 @@
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-          <div class="view-this-page">
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@@ -263,13 +200,13 @@
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         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="components-and-features">
-<h1>Components and Features<a class="headerlink" href="#components-and-features" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<h1>Components and Features<a class="headerlink" href="#components-and-features" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-20' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="framework-and-model-support">
-<h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-support" title="Link to this heading">¶</a></h2>
+<h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-support" title="Link to this heading">#</a></h2>
 <p>LIT is framework-agnostic and is compatible with any model that can be wrapped
 in a Python class for inference. In particular, we’ve tested with TF1.x, TF2,
 JAX, and PyTorch, as well as models that use custom C++ inference code (wrapped
@@ -282,7 +219,7 @@ <h2>Framework and Model Support<a class="headerlink" href="#framework-and-model-
 <a class="reference internal" href="api.html#adding-models-and-data"><span class="std std-ref">model and dataset classes</span></a>, but we also have
 out-of-the-box support for a few modeling frameworks, described below.</p>
 <section id="huggingface-transformers">
-<h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformers" title="Link to this heading">¶</a></h3>
+<h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformers" title="Link to this heading">#</a></h3>
 <p>Many of the
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/">open-source LIT examples</a> use
 HuggingFace Transformers via their TF2/Keras model classes. These give easy
@@ -293,7 +230,7 @@ <h3>HuggingFace Transformers<a class="headerlink" href="#huggingface-transformer
 working on academic projects.</p>
 </section>
 <section id="tf1-x-estimator">
-<h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to this heading">¶</a></h3>
+<h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to this heading">#</a></h3>
 <p>LIT supports Estimator and other TF1.x models, but the model wrappers can be
 more involved due to the need to explicitly manage the graph and sessions. (In
 particular: <code class="docutils literal notranslate"><span class="pre">Estimator.predict()</span></code> cannot be used because it reloads the model on
@@ -305,12 +242,13 @@ <h3>TF1.x Estimator<a class="headerlink" href="#tf1-x-estimator" title="Link to
 directly.</p></li>
 </ul>
 <p>Alternatively, you can export to a <code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> and load this in an eager mode
-runtime. This leads to much simpler code, but may require changes to your
-<code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> exporter in order to access model internals like embeddings,
-gradients, or attention.</p>
+runtime. This leads to much simpler code (see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&amp;amp;rcl=378713625">this example</a>),
+but may require changes to your <code class="docutils literal notranslate"><span class="pre">SavedModel</span></code> exporter in order to access model
+internals like embeddings, gradients, or attention.</p>
 </section>
 <section id="remote-or-hosted-models">
-<h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models" title="Link to this heading">¶</a></h3>
+<h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models" title="Link to this heading">#</a></h3>
 <p>LIT can easily interact with models hosted via an RPC or HTTP endpoint,
 including Servomatic. In this usage, the model weights and computation remain on
 the server, while the LIT <code class="docutils literal notranslate"><span class="pre">Model</span></code> implementation simply manages the RPC stub and
@@ -321,7 +259,7 @@ <h3>Remote or hosted models<a class="headerlink" href="#remote-or-hosted-models"
 </ul>
 </section>
 <section id="static-predictions">
-<h3>Static predictions<a class="headerlink" href="#static-predictions" title="Link to this heading">¶</a></h3>
+<h3>Static predictions<a class="headerlink" href="#static-predictions" title="Link to this heading">#</a></h3>
 <p>LIT works best when the model can be queried interactively, but this isn’t
 always possible for all situations. The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/static_preds.py"><code class="docutils literal notranslate"><span class="pre">StaticPredictions</span></code></a>
@@ -332,7 +270,7 @@ <h3>Static predictions<a class="headerlink" href="#static-predictions" title="Li
 as LIME which involve querying the model on new examples.</p>
 </section>
 <section id="data-loading">
-<h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this heading">¶</a></h3>
+<h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this heading">#</a></h3>
 <p>LIT can load data from almost any format, including TFRecord, Capacitor,
 SSTable, or even SQL queries, via a custom Python class that implements the
 <a class="reference internal" href="api.html#datasets"><span class="std std-ref">Dataset API</span></a>. Many of our demos use TFDS, and the LIT loader
@@ -345,7 +283,7 @@ <h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this h
 </section>
 </section>
 <section id="input-and-output-types">
-<h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" title="Link to this heading">¶</a></h2>
+<h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" title="Link to this heading">#</a></h2>
 <p>LIT uses an extensible system of semantic types to describe data and models.
 This allows for flexible support of a number of different input and output
 modalities common to NLP and other domains. For a full reference, see the
@@ -359,11 +297,13 @@ <h2>Input and Output Types<a class="headerlink" href="#input-and-output-types" t
 output type, many LIT features - such as datapoint exploration, side-by-side
 functionality, and counterfactual generation - are available for any model.</p>
 <section id="classification">
-<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading">¶</a></h3>
+<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading">#</a></h3>
 <p>LIT supports many features for classification tasks, including common metrics,
 confusion matrices, and custom thresholding via the UI. Classification is
 implemented with the <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> and <code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code> types.</p>
 <ul class="simple">
+<li><p>For a basic example on a binary sentiment task, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py">examples/simple_tf2_demo.py</a>.</p></li>
 <li><p>Models should define a <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> field in their output spec with the
 <code class="docutils literal notranslate"><span class="pre">vocab=</span></code> attribute as the set of class labels, and for each example should
 return a vector of probabilities for each class.</p></li>
@@ -373,7 +313,9 @@ <h3>Classification<a class="headerlink" href="#classification" title="Link to th
 <li><p>A negative class can be designated using the <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> attribute of
 <code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code> (most commonly, <code class="docutils literal notranslate"><span class="pre">null_idx=0</span></code>), and metrics such as
 precision, recall, F1 will be computed for the remaining classes. AUC and
-AUCPR will be computed for binary classification tasks.</p></li>
+AUCPR will be computed for binary classification tasks. For an example, see
+the
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&amp;amp;rcl=386779180">comment toxicity model</a>.</p></li>
 <li><p>If <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> is set and there is only one other class, the other class
 (often, class <code class="docutils literal notranslate"><span class="pre">1</span></code>) is treated as a positive class, and the LIT UI can be
 used to change the classification threshold. If <code class="docutils literal notranslate"><span class="pre">null_idx</span></code> is set and there
@@ -383,7 +325,7 @@ <h3>Classification<a class="headerlink" href="#classification" title="Link to th
 <p><a class="reference internal" href="_images/classification-results.png"><img alt="Classification Results Module" class="align-center" src="_images/classification-results.png" style="width: 600px;" /></a></p>
 </section>
 <section id="regression-scoring">
-<h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="Link to this heading">¶</a></h3>
+<h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="Link to this heading">#</a></h3>
 <p>Regression or scoring models also are well-supported with metrics, bucketed
 faceting, and scatterplots of scalar output. Regression is implemented with the
 <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> (input) and <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code> (output) types.</p>
@@ -394,15 +336,17 @@ <h3>Regression / Scoring<a class="headerlink" href="#regression-scoring" title="
 which contains numerical targets, and the model’s <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code> field
 should set <code class="docutils literal notranslate"><span class="pre">parent=</span></code> to the name of this field.</p></li>
 <li><p>For an example, see the STS-B textual similarity task in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a>.</p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a>.</p></li>
 </ul>
 </section>
 <section id="multi-label-classification">
-<h3>Multi-label classification<a class="headerlink" href="#multi-label-classification" title="Link to this heading">¶</a></h3>
+<h3>Multi-label classification<a class="headerlink" href="#multi-label-classification" title="Link to this heading">#</a></h3>
 <p>LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 <code class="docutils literal notranslate"><span class="pre">SparseMultilabelPreds</span></code> and <code class="docutils literal notranslate"><span class="pre">SparseMultilabel</span></code> types.</p>
 <ul class="simple">
+<li><p>For a basic example on an image labeling task, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a>.</p></li>
 <li><p>Models should define a <code class="docutils literal notranslate"><span class="pre">SparseMultilabelPreds</span></code> field in their output spec
 with the<code class="docutils literal notranslate"><span class="pre">vocab=</span></code> attribute as the set of class labels, and for each example
 should return a list of class score tuples. Each tuple contains two
@@ -415,7 +359,7 @@ <h3>Multi-label classification<a class="headerlink" href="#multi-label-classific
 </ul>
 </section>
 <section id="seq2seq-generation">
-<h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="Link to this heading">¶</a></h3>
+<h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="Link to this heading">#</a></h3>
 <p>LIT has a number of features for sequence generation models, though support is
 not quite as mature as for classification or regression. In particular, LIT can
 display single generations as well as scored candidates from beam search, and
@@ -434,11 +378,13 @@ <h3>Seq2Seq / Generation<a class="headerlink" href="#seq2seq-generation" title="
 the model can also output a <code class="docutils literal notranslate"><span class="pre">ReferenceScores</span></code> field (with values as
 <code class="docutils literal notranslate"><span class="pre">list[float]</span></code>) with <code class="docutils literal notranslate"><span class="pre">parent=</span></code> set to reference a <code class="docutils literal notranslate"><span class="pre">TextSegment</span></code> or
 <code class="docutils literal notranslate"><span class="pre">ReferenceTexts</span></code> field from the input.</p></li>
+<li><p>For modeling examples, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py">examples/models/t5.py</a></p></li>
 </ul>
 <p><a class="reference internal" href="_images/generation-results.png"><img alt="Generated Text Module" class="align-center" src="_images/generation-results.png" style="width: 600px;" /></a></p>
 </section>
 <section id="span-labeling-and-structured-prediction">
-<h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-labeling-and-structured-prediction" title="Link to this heading">¶</a></h3>
+<h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-labeling-and-structured-prediction" title="Link to this heading">#</a></h3>
 <p>LIT can support a variety of structured prediction types, and provides rich,
 interactive visualizations.</p>
 <ul class="simple">
@@ -458,7 +404,7 @@ <h3>Span Labeling and Structured Prediction<a class="headerlink" href="#span-lab
 <p><a class="reference internal" href="_images/structured-preds.png"><img alt="Structured Predictions Module" class="align-center" src="_images/structured-preds.png" style="width: 400px;" /></a></p>
 </section>
 <section id="multiple-input-segments">
-<h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments" title="Link to this heading">¶</a></h3>
+<h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments" title="Link to this heading">#</a></h3>
 <p>LIT can easily handle multiple text fields, or a mix of text, categorical,
 scalar, and other input features. LIT does not explicitly “privilege” one input
 field, and metadata in the model spec can be used to align gradients, attention,
@@ -468,11 +414,11 @@ <h3>Multiple input segments<a class="headerlink" href="#multiple-input-segments"
 <a class="reference internal" href="api.html#datasets"><span class="std std-ref">Dataset class documentation</span></a> and the corresponding
 <a class="reference internal" href="api.html#models"><span class="std std-ref">Model</span></a>.</p></li>
 <li><p>For a more involved code example including per-token gradients, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a>.</p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a>.</p></li>
 </ul>
 </section>
 <section id="tabular-data">
-<h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this heading">¶</a></h3>
+<h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this heading">#</a></h3>
 <p>LIT can be used as a replacement for the <a class="reference external" href="https://whatif-tool.dev">What-If Tool</a>
 but with more extensibility, when working with predictions over tabular data.</p>
 <p>Some interpreters, such as Kernel SHAP, require models that use tabular data. In
@@ -486,10 +432,10 @@ <h3>Tabular data<a class="headerlink" href="#tabular-data" title="Link to this h
 (<code class="docutils literal notranslate"><span class="pre">SparseMultilabel</span></code>) field.</p></li>
 </ul>
 <p>For a demo using a penguin stats dataset/binary classification task, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">examples/penguin/demo.py</a>.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">examples/penguin_demo.py</a>.</p>
 </section>
 <section id="images">
-<h3>Images<a class="headerlink" href="#images" title="Link to this heading">¶</a></h3>
+<h3>Images<a class="headerlink" href="#images" title="Link to this heading">#</a></h3>
 <p>LIT also contains support for models with images as input features or generated
 images as model output. The LIT type <code class="docutils literal notranslate"><span class="pre">ImageBytes</span></code> can be used as a feature in
 datasets and as part of an input spec or output spec for a model. That feature’s
@@ -497,10 +443,17 @@ <h3>Images<a class="headerlink" href="#images" title="Link to this heading">¶</
 <p>NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don’t break existing checked-in code with
 such a change.</p>
+<ul class="simple">
+<li><p>See <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py">examples/datasets/open_images.py</a> for a
+dataset containing images, including converting images to base64 encoded
+strings.</p></li>
+<li><p>For a demo of an image classifier, see
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a>.</p></li>
+</ul>
 </section>
 </section>
 <section id="token-based-salience">
-<h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title="Link to this heading">¶</a></h2>
+<h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title="Link to this heading">#</a></h2>
 <p>LIT supports several methods for token-based input salience, including
 gradient-based methods as well as black-box techniques like LIME that don’t
 require any access to model internals. Output is rendered in the Salience Maps
@@ -512,7 +465,7 @@ <h2>Token-based Salience<a class="headerlink" href="#token-based-salience" title
 regression (<code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) and generation (<code class="docutils literal notranslate"><span class="pre">GeneratedText</span></code> or
 <code class="docutils literal notranslate"><span class="pre">GeneratedTextCandidates</span></code>) outputs.</p>
 <section id="gradient-norm">
-<h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this heading">¶</a></h3>
+<h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this heading">#</a></h3>
 <p>This is a simple method, in which salience scores are proportional to the L2
 norm of the gradient, i.e. the score for token <span class="math notranslate nohighlight">\(i\)</span> is:</p>
 <div class="math-wrapper docutils container">
@@ -537,7 +490,7 @@ <h3>Gradient Norm<a class="headerlink" href="#gradient-norm" title="Link to this
 class.</p>
 </section>
 <section id="gradient-dot-input">
-<h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Link to this heading">¶</a></h3>
+<h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Link to this heading">#</a></h3>
 <p>In this method, salience scores are proportional to the dot product of the input
 embeddings and their gradients, i.e. for token <span class="math notranslate nohighlight">\(i\)</span> we compute:</p>
 <div class="math-wrapper docutils container">
@@ -546,8 +499,8 @@ <h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Li
 </div>
 <p>Compared to grad-norm, this gives directional scores: a positive score is can be
 interpreted as that token having a positive influence on the prediction
-<span class="math notranslate nohighlight">\(\hat{y}\)</span>, while a negative score suggests that the prediction would be
-stronger if that token was removed.</p>
+<span class="math notranslate nohighlight">\(\hat{y}\)</span>, while a negative score suggests that the prediction would be stronger
+if that token was removed.</p>
 <p>To enable this method, your model should, as part of the
 <a class="reference internal" href="api.html#models"><span class="std std-ref">output spec and <code class="docutils literal notranslate"><span class="pre">predict()</span></code> implementation</span></a>:</p>
 <ul class="simple">
@@ -567,7 +520,7 @@ <h3>Gradient-dot-Input<a class="headerlink" href="#gradient-dot-input" title="Li
 appropriate normalization.</p>
 </section>
 <section id="integrated-gradients">
-<h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title="Link to this heading">¶</a></h3>
+<h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title="Link to this heading">#</a></h3>
 <p>Integrated gradients is a more robust method for estimating feature
 contribution, based on integrating a gradients along a path in embedding space.
 See <a class="reference external" href="https://arxiv.org/abs/1703.01365">Sundararajan et al. 2017</a> for additional
@@ -606,11 +559,11 @@ <h3>Integrated Gradients<a class="headerlink" href="#integrated-gradients" title
 </div>
 <p>For a more concrete example that also supports multiple segments with separate
 gradients, see our
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py">BERT classifier demo model</a>,
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py">BERT classifier demo model</a>,
 or contact the LIT team for assistance.</p>
 </section>
 <section id="lime">
-<h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">¶</a></h3>
+<h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">#</a></h3>
 <p><a class="reference external" href="https://arxiv.org/abs/1602.04938">LIME</a> is a black-box salience method that
 does not require access to any model internals. It works by generating a set of
 perturbed inputs - generally, by dropping out or masking tokens - and training a
@@ -625,7 +578,7 @@ <h3>LIME<a class="headerlink" href="#lime" title="Link to this heading">¶</a></
 regression/scoring (<code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) model.</p>
 </section>
 <section id="target-selection-on-classification-output">
-<h3>Target Selection on Classification Output<a class="headerlink" href="#target-selection-on-classification-output" title="Link to this heading">¶</a></h3>
+<h3>Target Selection on Classification Output<a class="headerlink" href="#target-selection-on-classification-output" title="Link to this heading">#</a></h3>
 <p>For all salience methods, we require that the class to explain is given as a
 label field in the input. For example, if the input example is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="s2">&quot;this movie was terrible!&quot;</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;0&quot;</span><span class="p">}</span>
@@ -662,7 +615,7 @@ <h3>Target Selection on Classification Output<a class="headerlink" href="#target
 </section>
 </section>
 <section id="sequence-salience">
-<h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">¶</a></h2>
+<h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">#</a></h2>
 <p>Sequence salience generalizes token-based salience to text-to-text models,
 allowing you to explain the impact of the prompt tokens on parts of the model
 output.</p>
@@ -672,7 +625,7 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 <p><a class="reference internal" href="_images/sequence-salience-2.png"><img alt="Sequence salience - visualization" class="align-center" src="_images/sequence-salience-2.png" style="width: 650px;" /></a></p>
 <p>The UI supports multiple options for analysis, including:</p>
 <ul class="simple">
-<li><p>Select from predefined target sequences, or explain generations from the
+<li><p>Select from pre-defined target sequences, or explain generations from the
 model.</p></li>
 <li><p>Different salience methods, including <a class="reference internal" href="#gradient-norm">Gradient Norm</a> and
 <a class="reference internal" href="#gradient-dot-input">Gradient-dot-Input</a>.</p></li>
@@ -680,7 +633,7 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 to words, sentences, lines, or paragraphs. Quickly switch between different
 views to refine your analysis to different parts of a prompt.</p></li>
 <li><p>Display density options to enable working with longer sequences, such as
-document text, few-shot examples, or chain-of-thought prompts.</p></li>
+document text, few-shot eaxmples, or chain-of-thought prompts.</p></li>
 </ul>
 <p>For a walkthrough of how to use sequence salience to debug LLMs, check out the
 Responsible Generative AI Toolkit at
@@ -702,15 +655,15 @@ <h2>Sequence Salience<a class="headerlink" href="#sequence-salience" title="Link
 <li><p>LIT-for-Gemma Colab:
 <a class="reference external" href="https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb"><code class="docutils literal notranslate"><span class="pre">lit_gemma.ipynb</span></code></a></p></li>
 <li><p>Demo binary:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py</a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_salience_demo.py</span></code></a></p></li>
 <li><p>KerasNLP model wrappers:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py</a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py"><code class="docutils literal notranslate"><span class="pre">instrumented_keras_lms.py</span></code></a></p></li>
 <li><p>Transformers model wrappers:
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py">https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py</a></p></li>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py"><code class="docutils literal notranslate"><span class="pre">pretrained_lms.py</span></code></a></p></li>
 </ul>
 </section>
 <section id="salience-clustering">
-<h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="Link to this heading">¶</a></h2>
+<h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="Link to this heading">#</a></h2>
 <p>LIT includes a basic implementation of the salience clustering method from
 <a class="reference external" href="https://arxiv.org/abs/2211.05485">Ebert et al. 2022</a>, which uses k-means on a
 salience-weighted bag-of-words representation to find patterns in model
@@ -723,7 +676,7 @@ <h2>Salience Clustering<a class="headerlink" href="#salience-clustering" title="
 <a class="reference external" href="https://arxiv.org/pdf/2211.05485.pdf">the paper</a>.</p>
 </section>
 <section id="tabular-feature-attribution">
-<h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attribution" title="Link to this heading">¶</a></h2>
+<h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attribution" title="Link to this heading">#</a></h2>
 <p>Tabular feature attribution seeks to understand the importance of a column of
 data on a model’s predictions. LIT’s tabular feature attribution module supports
 this analysis using the <a class="reference external" href="https://github.com/slundberg/shap">SHAP interpreter</a>.
@@ -734,7 +687,7 @@ <h2>Tabular Feature Attribution<a class="headerlink" href="#tabular-feature-attr
 <p><a class="reference internal" href="_images/tabular-feature-attribution.png"><img alt="Tabular feature attribution module module" class="align-center" src="_images/tabular-feature-attribution.png" style="width: 500px;" /></a></p>
 </section>
 <section id="pixel-based-salience">
-<h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title="Link to this heading">¶</a></h2>
+<h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title="Link to this heading">#</a></h2>
 <p>LIT also supports pixel-based salience methods, for models that take images as
 inputs. Output is rendered in the Salience Maps module in the LIT UI, which
 allows for comparison of multiple methods at once.</p>
@@ -754,7 +707,9 @@ <h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title
 the gradients with respect to each pixel in each color channel in the 2D
 input image.</p>
 <p>The model should be able to accept input images as numpy arrays in addition
-to accepting base64 URL encoded format.</p>
+to accepting base64 URL encoded format. See
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py">mobilenet.py</a>
+for an example.</p>
 </li>
 </ul>
 <p>A variety of image saliency techniques are implemented for models that return
@@ -765,8 +720,33 @@ <h2>Pixel-based Salience<a class="headerlink" href="#pixel-based-salience" title
 <p>Each of these techniques returns a saliency map image as a base64-encoded string
 through the <code class="docutils literal notranslate"><span class="pre">ImageSalience</span></code> type.</p>
 </section>
+<section id="attention">
+<h2>Attention<a class="headerlink" href="#attention" title="Link to this heading">#</a></h2>
+<p>LIT can display a visualization of attention heads from transformers and other
+models:</p>
+<p><a class="reference internal" href="_images/attention.png"><img alt="Attention Visualization" class="align-center" src="_images/attention.png" style="width: 400px;" /></a></p>
+<p>To enable this, your model should return one or more fields of the type
+<code class="docutils literal notranslate"><span class="pre">AttentionHeads</span></code>, with values as arrays of shape <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[num_heads,</span> <span class="pre">num_tokens,</span> <span class="pre">num_tokens]</span></code>. Each field represents a set of heads at a single layer of the
+model, so models will often have more than one:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>   <span class="k">def</span> <span class="nf">output_spec</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">types</span><span class="o">.</span><span class="n">Spec</span><span class="p">:</span>
+     <span class="k">return</span> <span class="p">{</span>
+         <span class="c1"># ...</span>
+         <span class="s2">&quot;tokens&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">Tokens</span><span class="p">(</span><span class="n">parent</span><span class="o">=</span><span class="s2">&quot;input_text&quot;</span><span class="p">),</span>
+         <span class="s2">&quot;layer_0/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
+         <span class="s2">&quot;layer_1/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
+         <span class="s2">&quot;layer_2/attention&quot;</span><span class="p">:</span> <span class="n">lit_types</span><span class="o">.</span><span class="n">AttentionHeads</span><span class="p">(</span><span class="n">align_in</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">,</span> <span class="n">align_out</span><span class="o">=</span><span class="s2">&quot;tokens&quot;</span><span class="p">),</span>
+         <span class="c1"># ...</span>
+     <span class="p">}</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">align_in</span></code> and <code class="docutils literal notranslate"><span class="pre">align_out</span></code> attributes are the names of <code class="docutils literal notranslate"><span class="pre">Tokens</span></code> fields for
+the source and target tokens for that layer. For self-attention (such as in
+BERT) these would name the same field, but for encoder-decoder attention they
+could reference different token sets (such as “input_tokens” and
+“target_tokens”).</p>
+</section>
 <section id="embedding-projector">
-<h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">¶</a></h2>
+<h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">#</a></h2>
 <p>LIT includes a version of the
 <a class="reference external" href="https://projector.tensorflow.org/">embedding projector</a> which can be used to
 visualize the latent space of your model, in order to find clusters or patterns
@@ -782,16 +762,16 @@ <h2>Embedding Projector<a class="headerlink" href="#embedding-projector" title="
 <code class="docutils literal notranslate"><span class="pre">&lt;float&gt;[emb_dim]</span></code> for each example.</p>
 </section>
 <section id="aggregate-analysis">
-<h2>Aggregate Analysis<a class="headerlink" href="#aggregate-analysis" title="Link to this heading">¶</a></h2>
+<h2>Aggregate Analysis<a class="headerlink" href="#aggregate-analysis" title="Link to this heading">#</a></h2>
 <section id="metrics">
-<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶</a></h3>
+<h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
 <p>LIT includes common metrics for classification, regression, and seq2seq (BLEU)
 by default, which will appear in the table when the appropriate types are
 present in the model output and input data. Metrics can be computed on the whole
 dataset, a selected subset, or on facets defined by particular features. For
 example, we could facet by class label:</p>
 <p><img alt="Metrics Table" src="_images/metrics-table.png" /></p>
-<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Metrics” tab.</p>
+<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Performance” tab.</p>
 <p>To enable metrics, your model should set the <code class="docutils literal notranslate"><span class="pre">parent</span></code> attribute on one or more
 output fields, pointing to the name of the input field that it should be
 evaluated against. For example, for classification, the data spec might have:</p>
@@ -816,19 +796,19 @@ <h3>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">¶
 <a class="reference internal" href="api.html#metrics"><span class="std std-ref">API documentation</span></a> for more.</p>
 </section>
 <section id="confusion-matrix">
-<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">¶</a></h3>
+<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">#</a></h3>
 <p>LIT includes a powerful and flexible confusion matrix, which can be used to
 compare predictions to gold labels as well as to compare between two models or
 between different categorical features. You can click cells or row/column
 headers to select a subset of examples, which is useful for intersectional
 analysis.</p>
 <p><a class="reference internal" href="_images/confusion-matrix.png"><img alt="Confusion Matrix" class="align-center" src="_images/confusion-matrix.png" style="width: 600px;" /></a></p>
-<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Metrics” tab.</p>
+<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Performance” tab.</p>
 <p>The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (<code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code>).</p>
 </section>
 <section id="scalar-plots">
-<h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this heading">¶</a></h3>
+<h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this heading">#</a></h3>
 <p>LIT includes scatterplots for scalar features, including plain scalars (<code class="docutils literal notranslate"><span class="pre">Scalar</span></code>
 or <code class="docutils literal notranslate"><span class="pre">RegressionScore</span></code>) as well as per-class probabilities from classification
 output (<code class="docutils literal notranslate"><span class="pre">MulticlassPreds</span></code>).</p>
@@ -840,7 +820,7 @@ <h3>Scalar Plots<a class="headerlink" href="#scalar-plots" title="Link to this h
 <p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> and navigate to the “Predictions” tab.</p>
 </section>
 <section id="binary-classification-thresholds">
-<h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classification-thresholds" title="Link to this heading">¶</a></h3>
+<h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classification-thresholds" title="Link to this heading">#</a></h3>
 <p>For binary classification models, LIT contains a module for setting
 classification thresholds, which determine at what score for the positive class
 the model determines that an example should be classified as belonging to the
@@ -876,7 +856,7 @@ <h3>Binary Classification Thresholds<a class="headerlink" href="#binary-classifi
 <p><img alt="Binary Classifier Thresholds Module" src="_images/lit-thresholder.png" /></p>
 </section>
 <section id="partial-dependence-plots">
-<h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">¶</a></h3>
+<h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">#</a></h3>
 <p>For classification or regression models with <code class="docutils literal notranslate"><span class="pre">CategoryLabel</span></code> or <code class="docutils literal notranslate"><span class="pre">Scalar</span></code> input
 features, the Partial Dependence Plots module shows plots indicating the effect
 that changing those individual features has on model output.</p>
@@ -894,10 +874,9 @@ <h3>Partial Dependence Plots<a class="headerlink" href="#partial-dependence-plot
 <p>If no datapoints are selected, then the calculations are done across all
 datapoints, giving a global view of feature effects.</p>
 <p><a class="reference internal" href="_images/lit-pdps.png"><img alt="Partial Dependence Plots Module" class="align-center" src="_images/lit-pdps.png" style="width: 400px;" /></a></p>
-<p>To try this out, see <a class="reference external" href="https://pair-code.github.io/lit/demos/penguins.html">https://pair-code.github.io/lit/demos/penguins.html</a> and navigate to the “Predictions” tab.</p>
 </section>
 <section id="dive">
-<h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">¶</a></h3>
+<h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">#</a></h3>
 <p>Dive is a visualization module, inspired by our prior work on
 <a class="reference external" href="https://pair-code.github.io/facets/">Facets Dive</a> and its use in the
 <a class="reference external" href="https://pair-code.github.io/what-if-tool/">What-If Tool</a>, that enables
@@ -921,7 +900,7 @@ <h3>Dive<a class="headerlink" href="#dive" title="Link to this heading">¶</a></
 </section>
 </section>
 <section id="tcav">
-<h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">¶</a></h2>
+<h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">#</a></h2>
 <p>Many interpretability methods provide importance values per input feature (e.g,
 token). By contrast, <a class="reference external" href="https://arxiv.org/abs/1711.11279">TCAV</a> shows the
 importance of high-level concepts (e.g., color, gender, race) for a prediction
@@ -939,7 +918,7 @@ <h2>TCAV<a class="headerlink" href="#tcav" title="Link to this heading">¶</a></
 layer, the predicted class, and the corresponding gradient values for that layer
 and class.</p>
 <section id="example">
-<h3>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h3>
+<h3>Example<a class="headerlink" href="#example" title="Link to this heading">#</a></h3>
 <p>1.) To use TCAV, begin by creating one or more ‘concept’ slices.</p>
 <p>Every dataset/model is different, but for images, as low as 15 data points are
 shown to be sufficient. Start by adding at least 3 data points and add more as
@@ -965,7 +944,7 @@ <h3>Example<a class="headerlink" href="#example" title="Link to this heading">¶
 <p><a class="reference internal" href="_images/tcav-results-table.png"><img alt="TCAV2" class="align-center" src="_images/tcav-results-table.png" style="width: 800px;" /></a></p>
 </section>
 <section id="statistical-significance">
-<h3>Statistical Significance<a class="headerlink" href="#statistical-significance" title="Link to this heading">¶</a></h3>
+<h3>Statistical Significance<a class="headerlink" href="#statistical-significance" title="Link to this heading">#</a></h3>
 <p>One of the pitfalls with the TCAV method is the potential generating meaningless
 CAVs, since any randomly chosen set of images will still produce a CAV (even if
 it is not meaningful).</p>
@@ -989,13 +968,13 @@ <h3>Statistical Significance<a class="headerlink" href="#statistical-significanc
 statistical testing (which is indicated in the UI).</p>
 </section>
 <section id="sorting-by-cosine-similarity">
-<h3>Sorting by Cosine Similarity<a class="headerlink" href="#sorting-by-cosine-similarity" title="Link to this heading">¶</a></h3>
+<h3>Sorting by Cosine Similarity<a class="headerlink" href="#sorting-by-cosine-similarity" title="Link to this heading">#</a></h3>
 <p>The option to sort examples by cosine similarity to a CAV will be available in
 an upcoming release.</p>
 </section>
 </section>
 <section id="counterfactual-analysis">
-<h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis" title="Link to this heading">¶</a></h2>
+<h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis" title="Link to this heading">#</a></h2>
 <p>While aggregate metrics can give a picture of overall behavior, and salience
 maps can give quick insight into a model’s local behavior, many questions about
 model behavior are best answered in a counterfactual setting: “How does my model
@@ -1006,7 +985,7 @@ <h2>Counterfactual Analysis<a class="headerlink" href="#counterfactual-analysis"
 explore this, both through manual edits and through automatic “generator”
 components.</p>
 <section id="manual-editing">
-<h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to this heading">¶</a></h3>
+<h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to this heading">#</a></h3>
 <p>Examples can be edited manually in the Datapoint Editor module:</p>
 <p><a class="reference internal" href="_images/manual-edit.png"><img alt="Manual Edit in the Datapoint Editor" class="align-center" src="_images/manual-edit.png" style="width: 400px;" /></a></p>
 <p>The “Add and Compare” button can be used to enter comparison mode, which will
@@ -1020,7 +999,7 @@ <h3>Manual Editing<a class="headerlink" href="#manual-editing" title="Link to th
 <p><a class="reference internal" href="_images/pair-selection.png"><img alt="Pair Selection Controls" class="align-center" src="_images/pair-selection.png" style="width: 700px;" /></a></p>
 </section>
 <section id="generators">
-<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">¶</a></h3>
+<h3>Generators<a class="headerlink" href="#generators" title="Link to this heading">#</a></h3>
 <p>The <strong>Generator Module</strong> supports automatic generation of counterfactuals
 through a variety of plug-in components:</p>
 <p><img alt="Generator Module" src="_images/generator-module.png" /></p>
@@ -1143,6 +1122,7 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
 <li><a class="reference internal" href="#salience-clustering">Salience Clustering</a></li>
 <li><a class="reference internal" href="#tabular-feature-attribution">Tabular Feature Attribution</a></li>
 <li><a class="reference internal" href="#pixel-based-salience">Pixel-based Salience</a></li>
+<li><a class="reference internal" href="#attention">Attention</a></li>
 <li><a class="reference internal" href="#embedding-projector">Embedding Projector</a></li>
 <li><a class="reference internal" href="#aggregate-analysis">Aggregate Analysis</a><ul>
 <li><a class="reference internal" href="#metrics">Metrics</a></li>
@@ -1176,9 +1156,9 @@ <h3>Generators<a class="headerlink" href="#generators" title="Link to this headi
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
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-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
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diff --git a/docs/documentation/demos.html b/docs/documentation/demos.html
index 3a4e4f48..b1a613d1 100644
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+++ b/docs/documentation/demos.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="UI Guide" href="ui_guide.html" /><link rel="prev" title="Getting Started with LIT" href="getting_started.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Demos - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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     <title>Dark mode</title>
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/demos.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,10 +200,10 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="demos">
-<h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">¶</a></h1>
-<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
+<h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">#</a></h1>
+<!-- freshness: { owner: 'lit-dev' reviewed: '2023-08-29' } -->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>The LIT team maintains a number of hosted demos, as well as pre-built launchers
 for some common tasks and model types.</p>
@@ -274,11 +211,11 @@ <h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">¶</a>
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>.</p>
 <hr class="docutils" />
 <section id="classification">
-<h2>Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --><a class="headerlink" href="#classification" title="Link to this heading">¶</a></h2>
+<h2>Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --><a class="headerlink" href="#classification" title="Link to this heading">#</a></h2>
 <section id="sentiment-and-nli">
-<h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="headerlink" href="#sentiment-and-nli" title="Link to this heading">¶</a></h3>
+<h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="headerlink" href="#sentiment-and-nli" title="Link to this heading">#</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a></p>
 <ul class="simple">
 <li><p>Multi-task demo:</p>
 <ul>
@@ -301,6 +238,7 @@ <h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="heade
 the margin.</p></li>
 <li><p>Embedding projector to find clusters in representation space.</p></li>
 <li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
+<li><p>Attention visualization.</p></li>
 <li><p>Counterfactual generators, including HotFlip for targeted adversarial
 perturbations.</p></li>
 </ul>
@@ -309,14 +247,28 @@ <h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="heade
 <p>Tip: check out a case study for this demo on the public LIT website:
 <a class="reference external" href="https://pair-code.github.io/lit/tutorials/sentiment">https://pair-code.github.io/lit/tutorials/sentiment</a></p>
 </section>
+<section id="multilingual-xnli">
+<h3>Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} --><a class="headerlink" href="#multilingual-xnli" title="Link to this heading">#</a></h3>
+<p><strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py">examples/xnli_demo.py</a></p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://cims.nyu.edu/~sbowman/xnli/">XNLI</a> dataset translates a subset of
+MultiNLI into 14 different languages.</p></li>
+<li><p>Specify <code class="docutils literal notranslate"><span class="pre">--languages=en,jp,hi,...</span></code> flag to select which languages to load.</p></li>
+<li><p>NLI as a three-way classification task with two-segment input (premise,
+hypothesis).</p></li>
+<li><p>Fine-tuned multilingual BERT model.</p></li>
+<li><p>Salience methods work with non-whitespace-delimited text, by using the
+model’s wordpiece tokenization.</p></li>
+</ul>
+</section>
 </section>
 <hr class="docutils" />
 <section id="regression-scoring">
-<h2>Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --><a class="headerlink" href="#regression-scoring" title="Link to this heading">¶</a></h2>
+<h2>Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --><a class="headerlink" href="#regression-scoring" title="Link to this heading">#</a></h2>
 <section id="textual-similarity-sts-b">
-<h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a class="headerlink" href="#textual-similarity-sts-b" title="Link to this heading">¶</a></h3>
+<h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a class="headerlink" href="#textual-similarity-sts-b" title="Link to this heading">#</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;amp;dataset=stsb_dev">https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;dataset=stsb_dev</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py">examples/glue_demo.py</a></p>
 <ul class="simple">
 <li><p>STS-B textual similarity task, predicting scores on a range from 0
 (unrelated) to 5 (very similar).</p></li>
@@ -328,6 +280,7 @@ <h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a cla
 quick filtering.</p></li>
 <li><p>Embedding projector to find clusters in representation space.</p></li>
 <li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
+<li><p>Attention visualization.</p></li>
 </ul>
 </li>
 </ul>
@@ -335,31 +288,91 @@ <h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a cla
 </section>
 <hr class="docutils" />
 <section id="sequence-to-sequence">
-<h2>Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --><a class="headerlink" href="#sequence-to-sequence" title="Link to this heading">¶</a></h2>
+<h2>Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --><a class="headerlink" href="#sequence-to-sequence" title="Link to this heading">#</a></h2>
 <section id="gemma">
-<h3>Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --><a class="headerlink" href="#gemma" title="Link to this heading">¶</a></h3>
-<p><strong>Code:</strong>
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py">examples/prompt_debugging/server.py</a></p>
+<h3>Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --><a class="headerlink" href="#gemma" title="Link to this heading">#</a></h3>
+<p><strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py">examples/lm_salience_demo.py</a></p>
 <ul class="simple">
-<li><p>Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
-and Transformers (with PyTorch).</p></li>
+<li><p>Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.</p></li>
 <li><p>Interactively debug LLM prompts using
 <a class="reference internal" href="components.html#sequence-salience"><span class="std std-ref">sequence salience</span></a>.</p></li>
 <li><p>Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-granularities: token-, word-, line-, sentence-, and paragraph-level.</p></li>
+granularities: token-, word-, sentence-, and paragraph-level.</p></li>
 </ul>
 <p>Tip: check out the in-depth walkthrough at
 <a class="reference external" href="https://ai.google.dev/responsible/model_behavior">https://ai.google.dev/responsible/model_behavior</a>, part of the Responsible
 Generative AI Toolkit.</p>
 </section>
+<section id="t5">
+<h3>T5 <!-- DO NOT REMOVE {#t5 .demo-header} --><a class="headerlink" href="#t5" title="Link to this heading">#</a></h3>
+<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/t5.html">https://pair-code.github.io/lit/demos/t5.html</a> <br />
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py">examples/t5_demo.py</a></p>
+<ul class="simple">
+<li><p>Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
+formats.</p></li>
+<li><p>Visualize beam candidates and highlight diffs against references.</p></li>
+<li><p>Visualize per-token decoder hypotheses to see where the model veers away
+from desired output.</p></li>
+<li><p>Filter examples by ROUGE score against reference.</p></li>
+<li><p>Embeddings from last layer of model, visualized with UMAP or PCA.</p></li>
+<li><p>Task wrappers to handle pre- and post-processing for summarization and
+machine translation tasks.</p></li>
+<li><p>Pre-loaded eval sets for CNNDM and WMT.</p></li>
+</ul>
+<p>Tip: check out a case study for this demo on the public LIT website:
+<a class="reference external" href="https://pair-code.github.io/lit/tutorials/generation">https://pair-code.github.io/lit/tutorials/generation</a></p>
+</section>
+</section>
+<hr class="docutils" />
+<section id="language-modeling">
+<h2>Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --><a class="headerlink" href="#language-modeling" title="Link to this heading">#</a></h2>
+<section id="bert-and-gpt-2">
+<h3>BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} --><a class="headerlink" href="#bert-and-gpt-2" title="Link to this heading">#</a></h3>
+<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/lm.html">https://pair-code.github.io/lit/demos/lm.html</a> <br />
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py">examples/lm_demo.py</a></p>
+<ul class="simple">
+<li><p>Compare multiple BERT and GPT-2 models side-by-side on a variety of
+plain-text corpora.</p></li>
+<li><p>LM visualization supports different modes:</p>
+<ul>
+<li><p>BERT masked language model: click-to-mask, and query model at that
+position.</p></li>
+<li><p>GPT-2 shows left-to-right hypotheses for each target token.</p></li>
+</ul>
+</li>
+<li><p>Embedding projector to show latent space of the model.</p></li>
+</ul>
+</section>
+</section>
+<hr class="docutils" />
+<section id="structured-prediction">
+<h2>Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --><a class="headerlink" href="#structured-prediction" title="Link to this heading">#</a></h2>
+<section id="gender-bias-in-coreference">
+<h3>Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} --><a class="headerlink" href="#gender-bias-in-coreference" title="Link to this heading">#</a></h3>
+<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/coref.html">https://pair-code.github.io/lit/demos/coref.html</a> <br />
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py">examples/coref/coref_demo.py</a></p>
+<ul class="simple">
+<li><p>Gold-mention coreference model, trained on
+<a class="reference external" href="https://catalog.ldc.upenn.edu/LDC2013T19">OntoNotes</a>.</p></li>
+<li><p>Evaluate on the Winogender schemas
+(<a class="reference external" href="https://arxiv.org/abs/1804.09301">Rudinger et al. 2018</a>) which test for
+gendered associations with profession names.</p></li>
+<li><p>Visualizations of coreference edges, as well as binary classification
+between two candidate referents.</p></li>
+<li><p>Stratified metrics for quantifying model bias as a function of pronoun
+gender or Bureau of Labor Statistics profession data.</p></li>
+</ul>
+<p>Tip: check out a case study for this demo on the public LIT website:
+<a class="reference external" href="https://pair-code.github.io/lit/tutorials/coref">https://pair-code.github.io/lit/tutorials/coref</a></p>
+</section>
 </section>
 <hr class="docutils" />
 <section id="multimodal">
-<h2>Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --><a class="headerlink" href="#multimodal" title="Link to this heading">¶</a></h2>
+<h2>Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --><a class="headerlink" href="#multimodal" title="Link to this heading">#</a></h2>
 <section id="tabular-data-penguin-classification">
-<h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --><a class="headerlink" href="#tabular-data-penguin-classification" title="Link to this heading">¶</a></h3>
+<h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --><a class="headerlink" href="#tabular-data-penguin-classification" title="Link to this heading">#</a></h3>
 <p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/penguins.html">https://pair-code.github.io/lit/demos/penguins.html</a> <br />
-<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">examples/penguin/demo.py</a></p>
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">examples/penguin_demo.py</a></p>
 <ul class="simple">
 <li><p>Binary classification on
 <a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">penguin dataset</a>.</p></li>
@@ -371,6 +384,17 @@ <h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-head
 parity.</p></li>
 </ul>
 </section>
+<section id="image-classification-with-mobilenet">
+<h3>Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} --><a class="headerlink" href="#image-classification-with-mobilenet" title="Link to this heading">#</a></h3>
+<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/images.html">https://pair-code.github.io/lit/demos/images.html</a> <br />
+<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">examples/image_demo.py</a></p>
+<ul class="simple">
+<li><p>Classification on ImageNet labels using a MobileNet model.</p></li>
+<li><p>Showing using of LIT on image data.</p></li>
+<li><p>Explore results of multiple gradient-based image saliency techniques in the
+Salience Maps module.</p></li>
+</ul>
+</section>
 </section>
 </section>
 
@@ -432,6 +456,7 @@ <h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-head
 <li><a class="reference internal" href="#">Demos</a><ul>
 <li><a class="reference internal" href="#classification">Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#sentiment-and-nli">Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --></a></li>
+<li><a class="reference internal" href="#multilingual-xnli">Multilingual (XNLI) <!-- DO NOT REMOVE {#xnli .demo-header} --></a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#regression-scoring">Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --></a><ul>
@@ -440,10 +465,20 @@ <h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-head
 </li>
 <li><a class="reference internal" href="#sequence-to-sequence">Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#gemma">Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --></a></li>
+<li><a class="reference internal" href="#t5">T5 <!-- DO NOT REMOVE {#t5 .demo-header} --></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#language-modeling">Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --></a><ul>
+<li><a class="reference internal" href="#bert-and-gpt-2">BERT and GPT-2 <!-- DO NOT REMOVE {#bert .demo-header} --></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#structured-prediction">Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --></a><ul>
+<li><a class="reference internal" href="#gender-bias-in-coreference">Gender Bias in Coreference <!-- DO NOT REMOVE {#coref .demo-header} --></a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#multimodal">Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --></a><ul>
 <li><a class="reference internal" href="#tabular-data-penguin-classification">Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --></a></li>
+<li><a class="reference internal" href="#image-classification-with-mobilenet">Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} --></a></li>
 </ul>
 </li>
 </ul>
@@ -458,8 +493,8 @@ <h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-head
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
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     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
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diff --git a/docs/documentation/docker.html b/docs/documentation/docker.html
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@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Glossary" href="glossary.html" /><link rel="prev" title="Frontend Developer Guide" href="frontend_development.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Running LIT in a Docker container - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/docker.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
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             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="running-lit-in-a-docker-container">
-<h1>Running LIT in a Docker container<a class="headerlink" href="#running-lit-in-a-docker-container" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-04' } *-->
+<h1>Running LIT in a Docker container<a class="headerlink" href="#running-lit-in-a-docker-container" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-07-10' } *-->
 <p>Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>. This doc describes the basic usage of
 LIT’s built-in demos, how to integrate your custom demo into this</p>
 <section id="basic-usage">
-<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this heading">¶</a></h2>
+<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this heading">#</a></h2>
 <p>LIT can be run as a containerized app using <a class="reference external" href="https://www.docker.com/">Docker</a> or
 your preferred engine. This is how we run our
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">hosted demos</a>.</p>
-<p>We provide a basic Dockerfile <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/Dockerfile">https://github.com/PAIR-code/lit/blob/main/Dockerfile</a> that you can use to build and run any of the demos in the <code class="docutils literal notranslate"><span class="pre">lit_nlp/examples</span></code> directory.
-The <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code> installs all necessary dependencies for LIT and builds the
+<p>We provide a basic
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile"><code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code></a> that you can
+use to build and run any of the demos in the <code class="docutils literal notranslate"><span class="pre">lit_nlp/examples</span></code> directory. The
+<code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code> installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs <a class="reference external" href="https://gunicorn.org/">gunicorn</a> as
 the HTTP server, invoking the <code class="docutils literal notranslate"><span class="pre">get_wsgi_app()</span></code> method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py"><code class="docutils literal notranslate"><span class="pre">gunicorn_config.py</span></code></a>.
 You can find a reference implementation in
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code class="docutils literal notranslate"><span class="pre">glue/demo.py</span></code></a>.</p>
-<p>Use the following shell
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml">https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml</a> commands to build the
-default Docker image for LIT from the provided <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code>, and then run a
-container from that image. Comments are provided in-line to help explain what
-each step does.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code class="docutils literal notranslate"><span class="pre">glue_demo.py</span></code></a> or
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
+<p>Use the following shell commands to build the default Docker image for LIT from
+the provided <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code>, and then run a container from that image. Comments
+are provided in-line to help explain what each step does.</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build the docker image using the -t argument to name the image. Remember to</span>
 <span class="c1"># include the trailing . so Docker knows where to look for the Dockerfile.</span>
 docker<span class="w"> </span>build<span class="w"> </span>--file<span class="w"> </span>Dockerfile<span class="w"> </span>--tag<span class="w"> </span>lit-nlp<span class="w"> </span>.
@@ -305,11 +243,14 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this hea
 below.</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># DEMO_NAME is used to complete the Python module path</span>
 <span class="c1">#</span>
-<span class="c1">#     &quot;lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()&quot;</span>
+<span class="c1">#     &quot;lit_nlp.examples.$DEMO_NAME&quot;</span>
 <span class="c1">#</span>
 <span class="c1"># Therefore, valid values for DEMO_NAME are Python module paths in the</span>
-<span class="c1"># lit_nlp/examples directory, such as glue, penguin, tydi, etc.</span>
-docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>penguin<span class="w"> </span>lit-nlp
+<span class="c1"># lit_nlp/examples directory, such as</span>
+<span class="c1">#</span>
+<span class="c1">#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.</span>
+<span class="c1">#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.</span>
+docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>lm_demo<span class="w"> </span>lit-nlp
 
 <span class="c1"># Use the DEMO_PORT environment variable as to change the port that LIT uses in</span>
 <span class="c1"># the container. Be sure to also change the -p option to map the container&#39;s</span>
@@ -320,16 +261,16 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this hea
 <span class="c1"># containers on your machine using the combination of the DEMO_NAME and</span>
 <span class="c1"># DEMO_PORT arguments, and docker run with the -d flag to run the container in</span>
 <span class="c1"># the background.</span>
-docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>penguin<span class="w"> </span>lit-nlp
-docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">2345</span>:2345<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>tydi<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_PORT</span><span class="o">=</span><span class="m">2345</span><span class="w"> </span>lit-nlp
+docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">5432</span>:5432<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>t5_demo<span class="w"> </span>lit-nlp
+docker<span class="w"> </span>run<span class="w"> </span>-d<span class="w"> </span>-p<span class="w"> </span><span class="m">2345</span>:2345<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_NAME</span><span class="o">=</span>lm_demo<span class="w"> </span>-e<span class="w"> </span><span class="nv">DEMO_PORT</span><span class="o">=</span><span class="m">2345</span><span class="w"> </span>lit-nlp
 </pre></div>
 </div>
 </section>
 <section id="integrating-custom-lit-instances-with-the-default-docker-image">
-<h2>Integrating Custom LIT Instances with the Default Docker Image<a class="headerlink" href="#integrating-custom-lit-instances-with-the-default-docker-image" title="Link to this heading">¶</a></h2>
+<h2>Integrating Custom LIT Instances with the Default Docker Image<a class="headerlink" href="#integrating-custom-lit-instances-with-the-default-docker-image" title="Link to this heading">#</a></h2>
 <p>Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a <code class="docutils literal notranslate"><span class="pre">main()</span></code> method, as
-described in the <a class="reference internal" href="api.html#adding-models-and-data"><span class="std std-ref">Python API docs</span></a>.</p>
+described in the <a class="reference external" href="https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data">Python API docs</a>.</p>
 <p>These custom server scripts can be easily integrated with LIT’s default image as
 long as your server meets two requirements:</p>
 <ol class="arabic simple">
@@ -392,7 +333,7 @@ <h2>Integrating Custom LIT Instances with the Default Docker Image<a class="head
 </div>
 </section>
 <section id="building-your-own-image">
-<h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image" title="Link to this heading">¶</a></h2>
+<h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image" title="Link to this heading">#</a></h2>
 <p>Coming soon.</p>
 </section>
 </section>
@@ -468,8 +409,8 @@ <h2>Building Your Own Image<a class="headerlink" href="#building-your-own-image"
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/faq.html b/docs/documentation/faq.html
index 32bd2624..18038110 100644
--- a/docs/documentation/faq.html
+++ b/docs/documentation/faq.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="prev" title="Glossary" href="glossary.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Frequently Asked Questions - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
-      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
-      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
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-      <line x1="14.5" y1="15.85" x2="14.5" y2="17.85"/>
-      <line x1="10.044" y1="5.094" x2="8.63" y2="3.68"/>
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-      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
-      <circle cx="14.5" cy="9.55" r="3.6"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
-    <title>Auto light/dark, in dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
-      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
-      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
-      <line style="opacity: 50%" x1="18" y1="3.705" x2="18" y2="2.5"/>
-      <line style="opacity: 50%" x1="18" y1="11.295" x2="18" y2="12.5"/>
-      <line style="opacity: 50%" x1="15.316" y1="4.816" x2="14.464" y2="3.964"/>
-      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
-      <line style="opacity: 50%" x1="14.205" y1="7.5" x2="13.001" y2="7.5"/>
-      <line style="opacity: 50%" x1="21.795" y1="7.5" x2="23" y2="7.5"/>
-      <line style="opacity: 50%" x1="15.316" y1="10.184" x2="14.464" y2="11.036"/>
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-      <circle style="opacity: 50%" cx="18" cy="7.5" r="2.169"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-pencil" viewBox="0 0 24 24">
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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-      <path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4" />
-      <path d="M13.5 6.5l4 4" />
-      <path d="M20 21l2 -2l-2 -2" />
-      <path d="M17 17l-2 2l2 2" /
-    </svg>
-  </symbol>
-  <symbol id="svg-eye" viewBox="0 0 24 24">
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
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-        d="M11.11 17.958c-3.209 -.307 -5.91 -2.293 -8.11 -5.958c2.4 -4 5.4 -6 9 -6c3.6 0 6.6 2 9 6c-.21 .352 -.427 .688 -.647 1.008" />
-      <path d="M20 21l2 -2l-2 -2" />
-      <path d="M17 17l-2 2l2 2" />
-    </svg>
-  </symbol>
 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/faq.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,23 +200,25 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="frequently-asked-questions">
-<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
+<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-19' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>Looking for help? Submit bugs, ask questions, suggest content, and request
 features on our
 <a class="reference external" href="https://github.com/pair-code/lit/issues/">Github issues list</a>.</p>
 <section id="model-and-data-types">
-<h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title="Link to this heading">¶</a></h2>
+<h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title="Link to this heading">#</a></h2>
 <p>LIT can handle a variety of models with different input and output types, and
 works with any modern ML framework. For more information, see
 <a class="reference internal" href="components.html#framework-and-model-support"><span class="std std-ref">Framework &amp; Model Support</span></a>.</p>
 <p>In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:</p>
 <ul class="simple">
-<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">Tabular demo</a> -
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py">Image demo</a> -
+image classification, using a Mobilenet model.</p></li>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py">Tabular demo</a> -
 multi-class classification on tabular (numeric and categorical string) data,
 using the
 <a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">Palmer Penguins</a>
@@ -289,15 +228,22 @@ <h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title
 <a class="reference internal" href="api.html#type-system"><span class="std std-ref">the features guide to input and output types</span></a>.</p>
 </section>
 <section id="languages">
-<h2>Languages<a class="headerlink" href="#languages" title="Link to this heading">¶</a></h2>
+<h2>Languages<a class="headerlink" href="#languages" title="Link to this heading">#</a></h2>
 <p>All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications.</p>
+scripts should work without any modifications. For examples, see:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py">XNLI demo</a> -
+cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
+model.</p></li>
+<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py">T5 demo</a> -
+includes WMT data for machine translation</p></li>
+</ul>
 </section>
 <section id="scale">
-<h2>Scale<a class="headerlink" href="#scale" title="Link to this heading">¶</a></h2>
+<h2>Scale<a class="headerlink" href="#scale" title="Link to this heading">#</a></h2>
 <section id="dataset-size">
-<h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this heading">¶</a></h3>
+<h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this heading">#</a></h3>
 <p>LIT can comfortably handle 10k-100k datapoints, depending on the speed of the
 server (for hosting the model) and your local machine (for viewing the UI). When
 working with large datasets, there are a couple caveats:</p>
@@ -323,7 +269,7 @@ <h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this h
 data files at runtime using the settings (⚙️) menu.</p>
 </section>
 <section id="large-models">
-<h3>Large Models<a class="headerlink" href="#large-models" title="Link to this heading">¶</a></h3>
+<h3>Large Models<a class="headerlink" href="#large-models" title="Link to this heading">#</a></h3>
 <p>LIT can work with large or slow models, as long as you can wrap them into the
 model API. If you have more than a few preloaded datapoints, however, you’ll
 probably want to use <code class="docutils literal notranslate"><span class="pre">warm_start=1</span></code> (or pass <code class="docutils literal notranslate"><span class="pre">--warm_start=1</span></code> as a flag) to
@@ -342,17 +288,17 @@ <h3>Large Models<a class="headerlink" href="#large-models" title="Link to this h
 </section>
 </section>
 <section id="privacy-and-security">
-<h2>Privacy and Security<a class="headerlink" href="#privacy-and-security" title="Link to this heading">¶</a></h2>
+<h2>Privacy and Security<a class="headerlink" href="#privacy-and-security" title="Link to this heading">#</a></h2>
 <p>LIT allows users to query the model, as well as to view the loaded evaluation
 data. The LIT UI state is ephemeral and exists only in the browser window;
 however, model predictions and any newly-generated examples (including as
 manually entered in the web UI) are stored in server memory, and if <code class="docutils literal notranslate"><span class="pre">--data_dir</span></code>
 is specified, may be cached to disk.</p>
 <p>LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to disk,
-then pass the <code class="docutils literal notranslate"><span class="pre">--demo_mode</span></code> runtime flag to the LIT server.</p>
+disk. If you do not want the tool to be able to write edited datapoints to
+disk, then pass the <code class="docutils literal notranslate"><span class="pre">--demo_mode</span></code> runtime flag to the LIT server.</p>
 <section id="i-have-proprietary-data-is-lit-secure-for-my-team-to-use">
-<h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerlink" href="#i-have-proprietary-data-is-lit-secure-for-my-team-to-use" title="Link to this heading">¶</a></h3>
+<h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerlink" href="#i-have-proprietary-data-is-lit-secure-for-my-team-to-use" title="Link to this heading">#</a></h3>
 <p>We don’t store, collect or share datasets, models or any other information
 loaded into LIT. When you run a LIT server, anyone with access to the web
 address of the server will be able to see data from the loaded datasets and
@@ -366,9 +312,9 @@ <h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerli
 </section>
 </section>
 <section id="workflow-and-integrations">
-<h2>Workflow and Integrations<a class="headerlink" href="#workflow-and-integrations" title="Link to this heading">¶</a></h2>
+<h2>Workflow and Integrations<a class="headerlink" href="#workflow-and-integrations" title="Link to this heading">#</a></h2>
 <section id="sending-examples-from-another-tool">
-<h3>Sending examples from another tool<a class="headerlink" href="#sending-examples-from-another-tool" title="Link to this heading">¶</a></h3>
+<h3>Sending examples from another tool<a class="headerlink" href="#sending-examples-from-another-tool" title="Link to this heading">#</a></h3>
 <p>LIT can read input fields directly from the URL; they should be encoded as
 <code class="docutils literal notranslate"><span class="pre">data_&lt;fieldname&gt;=&lt;value&gt;</span></code>, and field names should match those in the (default)
 dataset.</p>
@@ -377,7 +323,7 @@ <h3>Sending examples from another tool<a class="headerlink" href="#sending-examp
 but using <code class="docutils literal notranslate"><span class="pre">data0</span></code>, <code class="docutils literal notranslate"><span class="pre">data1</span></code>, <code class="docutils literal notranslate"><span class="pre">data2</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">data0_&lt;fieldname&gt;=&lt;value&gt;</span></code>.</p>
 </section>
 <section id="downloading-or-exporting-data">
-<h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exporting-data" title="Link to this heading">¶</a></h3>
+<h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exporting-data" title="Link to this heading">#</a></h3>
 <p>Currently, there are three ways to export data from the LIT UI:</p>
 <ul class="simple">
 <li><p>In the Data Table, you can copy or download the current view in CSV format -
@@ -391,7 +337,7 @@ <h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exp
 </ul>
 </section>
 <section id="loading-data-from-the-ui">
-<h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-ui" title="Link to this heading">¶</a></h3>
+<h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-ui" title="Link to this heading">#</a></h3>
 <p>There is limited support for this via the settings (⚙️) menu. Select a dataset,
 and enter a path to load from:</p>
 <p><img alt="Load data from the UI" src="_images/lit-load-data.png" /></p>
@@ -400,7 +346,7 @@ <h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-u
 appropriate format.</p>
 </section>
 <section id="using-components-outside-the-lit-ui">
-<h3>Using components outside the LIT UI<a class="headerlink" href="#using-components-outside-the-lit-ui" title="Link to this heading">¶</a></h3>
+<h3>Using components outside the LIT UI<a class="headerlink" href="#using-components-outside-the-lit-ui" title="Link to this heading">#</a></h3>
 <p>Python components such as models, datasets, and generators are designed to
 support standalone use. These don’t depend on the LIT serving framework, and you
 can treat them as any other Python class and use from Colab, regular scripts,
@@ -416,13 +362,15 @@ <h3>Using components outside the LIT UI<a class="headerlink" href="#using-compon
 then be re-used in other environments.</p>
 </section>
 <section id="training-models-with-lit">
-<h3>Training models with LIT<a class="headerlink" href="#training-models-with-lit" title="Link to this heading">¶</a></h3>
+<h3>Training models with LIT<a class="headerlink" href="#training-models-with-lit" title="Link to this heading">#</a></h3>
 <p>LIT is primarily an evaluation/inference-time tool, so we don’t provide any
 official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT’s <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> objects to manage training data along with standard
 training APIs (such as Keras’ <code class="docutils literal notranslate"><span class="pre">model.fit()</span></code>). See
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py"><code class="docutils literal notranslate"><span class="pre">glue/models.py</span></code></a>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py"><code class="docutils literal notranslate"><span class="pre">quickstart_sst_demo.py</span></code></a>
+and/or
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py"><code class="docutils literal notranslate"><span class="pre">glue_models.py</span></code></a>
 for examples.</p>
 </section>
 </section>
@@ -507,8 +455,8 @@ <h3>Training models with LIT<a class="headerlink" href="#training-models-with-li
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/frontend_development.html b/docs/documentation/frontend_development.html
index de68d9af..f3b9bbea 100644
--- a/docs/documentation/frontend_development.html
+++ b/docs/documentation/frontend_development.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Running LIT in a Docker container" href="docker.html" /><link rel="prev" title="LIT Python API" href="api.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Frontend Developer Guide - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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-      <line x1="19" y1="5.05" x2="20.414" y2="3.636"/>
-      <circle cx="14.5" cy="9.55" r="3.6"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
-    <title>Auto light/dark, in dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/frontend_development.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,15 +200,15 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="frontend-developer-guide">
-<h1>Frontend Developer Guide<a class="headerlink" href="#frontend-developer-guide" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<h1>Frontend Developer Guide<a class="headerlink" href="#frontend-developer-guide" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>This document aims to describe the current LIT frontend system, including
 conventions, best practices, and gotchas.</p>
 <section id="high-level-overview">
-<h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="Link to this heading">¶</a></h2>
+<h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="Link to this heading">#</a></h2>
 <p>LIT is powered by two central pieces of tech -
 <a class="reference external" href="https://lit-element.polymer-project.org/">lit-element</a> for components and HTML
 rendering, and <a class="reference external" href="https://mobx.js.org/README.html">mobx</a> for observable-oriented
@@ -286,13 +223,13 @@ <h2>High Level Overview<a class="headerlink" href="#high-level-overview" title="
 like Angular.</p>
 </section>
 <section id="application-architecture">
-<h2>Application Architecture<a class="headerlink" href="#application-architecture" title="Link to this heading">¶</a></h2>
+<h2>Application Architecture<a class="headerlink" href="#application-architecture" title="Link to this heading">#</a></h2>
 <p>The LIT client frontend is roughly divided into three conceptual groups -
 <strong>Modules</strong> (which render visualizations), <strong>Services</strong> (which manage data), and
 the <strong>App</strong> itself (which coordinates initialization of services and determines
 which modules to render).</p>
 <section id="bootstrapping">
-<h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this heading">¶</a></h3>
+<h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this heading">#</a></h3>
 <p>The LIT app bootstrapping takes place in two steps: First, the served
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/static/index.html"><code class="docutils literal notranslate"><span class="pre">index.html</span></code></a>
 page contains a single web component for the
@@ -304,16 +241,16 @@ <h3>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Link to this
 and rendering the various <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> components, a process about which we’ll go
 into greater detail later.</p>
 <p>The JS bundle entry point is
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts"><code class="docutils literal notranslate"><span class="pre">main.ts</span></code></a>, which first
-imports the loaded, the <code class="docutils literal notranslate"><span class="pre">&lt;lit-app&gt;</span></code> web component is declared, and attaches
-itself to the DOM, waiting for the app to be initialized.</p>
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts"><code class="docutils literal notranslate"><span class="pre">main.ts</span></code></a>, which
+first imports the loaded, the <code class="docutils literal notranslate"><span class="pre">&lt;lit-app&gt;</span></code> web component is declared, and
+attaches itself to the DOM, waiting for the app to be initialized.</p>
 <p>The second step is kicking off app initialization. The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts"><code class="docutils literal notranslate"><span class="pre">LitApp</span></code></a> singleton
 class is provided with a layout declaring which <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> components to use,
 then builds the app services and kicks off app initialization and loading data.</p>
 </section>
 <section id="layout">
-<h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">¶</a></h3>
+<h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">#</a></h3>
 <p>A layout defines the arraignment of <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> classes in the UI. Layouts are
 specified in Python as <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instances, and LIT includes three
 pre-configured layouts in
@@ -324,7 +261,7 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">¶</
 <li><p><code class="docutils literal notranslate"><span class="pre">default</span></code>: The original LIT layout with a single group of modules on top for
 exploring and selecting data, and a collection of tabs supporting different
 analytical tasks on the bottom; and</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">three_panel</span></code>: A three-panel layout that puts exploratory data
+<li><p><code class="docutils literal notranslate"><span class="pre">experimental</span></code>: A three-panel layout that puts exploratory data
 visualizations at full-page height on the left, tools for inspecting and
 manipulating examples and their associated predictions in the upper right,
 and a collection of tabs supporting different analytical tasks in the lower
@@ -333,14 +270,14 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">¶</
 </ul>
 <p>You can also add <a class="reference internal" href="api.html#customizing-the-layout"><span class="std std-ref">custom layouts</span></a> to your LIT
 instance by defining one or more <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instances and passing them
-to the server. For an example, see
-<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py"><code class="docutils literal notranslate"><span class="pre">prompt_debugging/layouts.py</span></code></a>.</p>
+to the server. For an example, see <code class="docutils literal notranslate"><span class="pre">CUSTOM_LAYOUTS</span></code> in
+<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py"><code class="docutils literal notranslate"><span class="pre">lm_demo.py</span></code></a>.</p>
 <p>Note: The pre-configured layouts are added to every <code class="docutils literal notranslate"><span class="pre">LitApp</span></code> instance using
 <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict">dictionary updates</a> where
 the Mapping passed to the <code class="docutils literal notranslate"><span class="pre">LitApp</span></code> constructor overrides the pre-configured
 layouts <code class="docutils literal notranslate"><span class="pre">Mapping</span></code>. Thus, you can remove or change these pre-configured layouts
 as you like by passing a <code class="docutils literal notranslate"><span class="pre">Mapping</span></code> where the values of <code class="docutils literal notranslate"><span class="pre">simple</span></code>, <code class="docutils literal notranslate"><span class="pre">default</span></code>,
-and/or <code class="docutils literal notranslate"><span class="pre">three_panel</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (to remove) or a <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instance
+and/or <code class="docutils literal notranslate"><span class="pre">experimental</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (to remove) or a <code class="docutils literal notranslate"><span class="pre">LitCanonicalLayout</span></code> instance
 (to override) as you desire.</p>
 <p>The actual layout of components in the LIT UI, see
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/modules.ts"><code class="docutils literal notranslate"><span class="pre">&lt;lit-modules&gt;</span></code></a>,
@@ -352,7 +289,7 @@ <h3>Layout<a class="headerlink" href="#layout" title="Link to this heading">¶</
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/services/modules_service.ts"><code class="docutils literal notranslate"><span class="pre">modules_service</span></code></a>.</p>
 </section>
 <section id="initialization">
-<h3>Initialization<a class="headerlink" href="#initialization" title="Link to this heading">¶</a></h3>
+<h3>Initialization<a class="headerlink" href="#initialization" title="Link to this heading">#</a></h3>
 <p>Finally, the LIT App initializes by building the various service classes and
 starting the initial load of data from the server. This process consists of:</p>
 <ol class="arabic simple">
@@ -364,7 +301,7 @@ <h3>Initialization<a class="headerlink" href="#initialization" title="Link to th
 </section>
 </section>
 <section id="modules-litmodule">
-<h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Link to this heading">¶</a></h2>
+<h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Link to this heading">#</a></h2>
 <p>The
 <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts"><code class="docutils literal notranslate"><span class="pre">LitModule</span></code></a>
 is the base class from which all module components derive. It provides a number
@@ -436,7 +373,7 @@ <h2>Modules (LitModule)<a class="headerlink" href="#modules-litmodule" title="Li
 necessary static properties and many of the most common patterns found in the
 LIT app.</p>
 <section id="setup">
-<h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">¶</a></h3>
+<h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a></h3>
 <p>First, a <code class="docutils literal notranslate"><span class="pre">LitModule</span></code> must declare a static <code class="docutils literal notranslate"><span class="pre">title</span></code> string (1) and <code class="docutils literal notranslate"><span class="pre">template</span></code>
 function (2). The <code class="docutils literal notranslate"><span class="pre">template</span></code> function determines how the modules layout renders
 the component template and passes in module properties, such as the name of the
@@ -469,7 +406,7 @@ <h3>Setup<a class="headerlink" href="#setup" title="Link to this heading">¶</a>
 other TypeScript files in different build units.</p>
 </section>
 <section id="functionality">
-<h3>Functionality<a class="headerlink" href="#functionality" title="Link to this heading">¶</a></h3>
+<h3>Functionality<a class="headerlink" href="#functionality" title="Link to this heading">#</a></h3>
 <p>The above module has a very simple task - When the user selects input data, it
 makes a request to an API service to fetch and display a pig latin translation
 of the data. Since we’re using mobx observables to store and compute our state,
@@ -508,7 +445,7 @@ <h3>Functionality<a class="headerlink" href="#functionality" title="Link to this
 components.</p>
 </section>
 <section id="escape-hatches">
-<h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to this heading">¶</a></h3>
+<h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to this heading">#</a></h3>
 <p>Finally, it’s worth noting that the declarative template-based rendering setup,
 while effective for handling most component render logic, is sometimes
 inadequate for more advanced visualizations. In particular, the template
@@ -538,7 +475,7 @@ <h3>Escape Hatches<a class="headerlink" href="#escape-hatches" title="Link to th
 </div>
 </section>
 <section id="stateful-child-elements">
-<h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements" title="Link to this heading">¶</a></h3>
+<h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements" title="Link to this heading">#</a></h3>
 <p>Some modules may contain stateful child elements, where the element has some
 internal state that can have an effect on the module that contains it. Examples
 of this include any modules that contain the
@@ -577,7 +514,7 @@ <h3>Stateful Child Elements<a class="headerlink" href="#stateful-child-elements"
 </section>
 </section>
 <section id="style-guide">
-<h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this heading">¶</a></h2>
+<h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this heading">#</a></h2>
 <ul>
 <li><p>Please disable clang-format on <code class="docutils literal notranslate"><span class="pre">lit-html</span></code> templates and format these
 manually instead:</p>
@@ -612,7 +549,7 @@ <h2>Style Guide<a class="headerlink" href="#style-guide" title="Link to this hea
 </ul>
 </section>
 <section id="development-tips-open-source">
-<h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-open-source" title="Link to this heading">¶</a></h2>
+<h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-open-source" title="Link to this heading">#</a></h2>
 <p>If you’re modifying any TypeScript code, you’ll need to re-build the frontend.
 You can have yarn do this automatically. In one terminal, run:</p>
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>~/lit/lit_nlp
@@ -642,7 +579,7 @@ <h2>Development Tips (open-source)<a class="headerlink" href="#development-tips-
 be sure to remove any stale cache files.</p>
 </section>
 <section id="custom-client-modules">
-<h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" title="Link to this heading">¶</a></h2>
+<h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" title="Link to this heading">#</a></h2>
 <p>The LIT frontend can be extended with custom visualizations or interactive
 modules, though this is currently provided as “best effort” support and the API
 is not as mature as for Python extensions.</p>
@@ -774,8 +711,8 @@ <h2>Custom Client / Modules<a class="headerlink" href="#custom-client-modules" t
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/genindex.html b/docs/documentation/genindex.html
index 898845ed..71297246 100644
--- a/docs/documentation/genindex.html
+++ b/docs/documentation/genindex.html
@@ -4,9 +4,9 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="#" /><link rel="search" title="Search" href="search.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 --><title>Index - 🔥LIT 1.0 documentation</title>
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 --><title>Index - 🔥LIT 1.0 documentation</title>
 <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -67,7 +67,7 @@
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     <title>Light mode</title>
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -82,7 +82,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -90,7 +90,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -100,60 +100,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
-      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
-      <path style="opacity: 50%" d="M 5.411 14.504 C 5.471 14.504 5.532 14.504 5.591 14.504 C 3.639 16.319 4.383 19.569 6.931 20.352 C 7.693 20.586 8.512 20.551 9.25 20.252 C 8.023 23.207 4.056 23.725 2.11 21.184 C 0.166 18.642 1.702 14.949 4.874 14.536 C 5.051 14.512 5.231 14.5 5.411 14.5 L 5.411 14.504 Z"/>
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-    <title>Auto light/dark, in dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -165,8 +111,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -184,8 +128,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -244,8 +187,7 @@
           <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -255,7 +197,7 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           
 <section class="genindex-section">
   <h1 id="index">Index</h1>
@@ -294,8 +236,8 @@ <h1 id="index">Index</h1>
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/getting_started.html b/docs/documentation/getting_started.html
index 9d1c0f87..400e6844 100644
--- a/docs/documentation/getting_started.html
+++ b/docs/documentation/getting_started.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Demos" href="demos.html" /><link rel="prev" title="Learning Interpretability Tool (LIT)" href="index.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Getting Started with LIT - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
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     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       <circle cx="12" cy="12" r="5"></circle>
       <line x1="12" y1="1" x2="12" y2="3"></line>
       <line x1="12" y1="21" x2="12" y2="23"></line>
@@ -84,7 +84,7 @@
   <symbol id="svg-moon" viewBox="0 0 24 24">
     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
     </svg>
@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
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-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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-  <symbol id="svg-moon-with-sun" viewBox="0 0 24 24">
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 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
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         <button class="theme-toggle">
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-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/getting_started.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,13 +200,13 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="getting-started-with-lit">
-<h1>Getting Started with LIT<a class="headerlink" href="#getting-started-with-lit" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
+<h1>Getting Started with LIT<a class="headerlink" href="#getting-started-with-lit" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2022-08-17' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h2>
 <p>Using pip:</p>
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>lit-nlp
 </pre></div>
@@ -278,7 +215,7 @@ <h2>Installation<a class="headerlink" href="#installation" title="Link to this h
 <a class="reference external" href="https://github.com/pair-code/lit#download-and-installation">GitHub</a>.</p>
 </section>
 <section id="hosted-demos">
-<h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this heading">¶</a></h2>
+<h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this heading">#</a></h2>
 <p>If you want to jump in and start playing with the LIT UI, check out
 <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a> for links to our hosted demos.</p>
 <p>For a guide to the many features available, check out the
@@ -286,7 +223,7 @@ <h2>Hosted demos<a class="headerlink" href="#hosted-demos" title="Link to this h
 <a class="reference external" href="https://www.youtube.com/watch?v=j0OfBWFUqIE">short video</a>.</p>
 </section>
 <section id="lit-with-your-model">
-<h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerlink" href="#lit-with-your-model" title="Link to this heading">¶</a></h2>
+<h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerlink" href="#lit-with-your-model" title="Link to this heading">#</a></h2>
 <p>LIT provides a simple <a class="reference internal" href="api.html"><span class="std std-doc">Python API</span></a> for use with custom models and
 data, as well as components such as metrics and counterfactual generators. Most
 LIT users will take this route, which involves writing a short <code class="docutils literal notranslate"><span class="pre">demo.py</span></code> binary
@@ -308,7 +245,7 @@ <h2>LIT with your model <!-- DO NOT REMOVE {#custom-demos} --><a class="headerli
 for your task.</p>
 </section>
 <section id="using-lit-in-notebooks">
-<h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink" href="#using-lit-in-notebooks" title="Link to this heading">¶</a></h2>
+<h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink" href="#using-lit-in-notebooks" title="Link to this heading">#</a></h2>
 <p>LIT can also be used directly from Colab and Jupyter notebooks, with the LIT UI
 rendered in an output cell. See <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/dev/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb">LIT_sentiment_classifier.ipynb</a> for an example.</p>
 <p>Note: if you see a 403 error in the output cell where LIT should render, you may
@@ -316,7 +253,7 @@ <h2>Using LIT in notebooks <!-- DO NOT REMOVE {#colab} --><a class="headerlink"
 <code class="docutils literal notranslate"><span class="pre">LitWidget</span></code> constructor.</p>
 </section>
 <section id="stand-alone-components">
-<h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerlink" href="#stand-alone-components" title="Link to this heading">¶</a></h2>
+<h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerlink" href="#stand-alone-components" title="Link to this heading">#</a></h2>
 <p>Many LIT components - such as models, datasets, metrics, and salience methods -
 are stand-alone Python classes and can be easily used outside of the LIT UI. For
 additional details, see the
@@ -324,13 +261,14 @@ <h2>Stand-alone components <!-- DO NOT REMOVE {#standalone} --><a class="headerl
 at <a class="reference external" href="https://colab.research.google.com/github/pair-code/lit/blob/dev/lit_nlp/examples/notebooks/LIT_components_example.ipynb">LIT_components_example.ipynb</a>.</p>
 </section>
 <section id="run-an-existing-example">
-<h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="headerlink" href="#run-an-existing-example" title="Link to this heading">¶</a></h2>
+<h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="headerlink" href="#run-an-existing-example" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="demos.html"><span class="std std-doc">demos page</span></a> lists some of the pre-built demos available for a
 variety of model types. The code for these is under <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples">examples</a>
 ;
 each is a small script that loads one or more models and starts a LIT server.</p>
-<p>Most demos can be run with a single command. To run the default one, you can do:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>lit_nlp.examples.glue.demo<span class="w"> </span><span class="se">\</span>
+<p>Most demos can be run with a single blaze command. To run the default one, you
+can do:</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>lit_nlp.examples.glue_demo<span class="w"> </span><span class="se">\</span>
 <span class="w">  </span>--quickstart<span class="w"> </span>--port<span class="o">=</span><span class="m">4321</span><span class="w"> </span>--alsologtostderr
 </pre></div>
 </div>
@@ -418,8 +356,8 @@ <h2>Run an existing example <!-- DO NOT REMOVE {#running-lit} --><a class="heade
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/glossary.html b/docs/documentation/glossary.html
index 43af1927..3af25e68 100644
--- a/docs/documentation/glossary.html
+++ b/docs/documentation/glossary.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Frequently Asked Questions" href="faq.html" /><link rel="prev" title="Running LIT in a Docker container" href="docker.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>Glossary - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=36a5483c" />
     <link rel="stylesheet" type="text/css" href="_static/furo_custom.css?v=6173ce67" />
     
@@ -69,7 +69,7 @@
   <symbol id="svg-sun" viewBox="0 0 24 24">
     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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       <line x1="12" y1="1" x2="12" y2="3"></line>
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@@ -84,7 +84,7 @@
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     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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 </svg>
 
 <input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
           <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/glossary.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
               <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
@@ -263,9 +200,9 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="glossary">
-<h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">¶</a></h1>
+<h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">#</a></h1>
 <p>There are a few commonly-overloaded terms which refer to specific things in the
 LIT APIs and codebase:</p>
 <ul class="simple">
@@ -363,8 +300,8 @@ <h1>Glossary<a class="headerlink" href="#glossary" title="Link to this heading">
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/includes/highlight_demos.html b/docs/documentation/includes/highlight_demos.html
index 56f05c2a..1050e3d9 100644
--- a/docs/documentation/includes/highlight_demos.html
+++ b/docs/documentation/includes/highlight_demos.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>&lt;no title&gt; - 🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
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@@ -69,7 +69,7 @@
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     <title>Light mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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       <line x1="12" y1="1" x2="12" y2="3"></line>
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@@ -84,7 +84,7 @@
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     <title>Dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       <path stroke="none" d="M0 0h24v24H0z" fill="none" />
       <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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@@ -92,7 +92,7 @@
   <symbol id="svg-sun-half" viewBox="0 0 24 24">
     <title>Auto light/dark mode</title>
     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
       <circle cx="12" cy="12" r="9" />
       <path d="M13 12h5" />
@@ -102,60 +102,6 @@
       <path d="M13 6h1" />
     </svg>
   </symbol>
-  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
-    <title>Auto light/dark, in light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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@@ -186,8 +130,7 @@
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-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
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@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="../_sources/includes/highlight_demos.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
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-  </a>
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+          
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-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
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             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
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-        <article role="main" id="furo-main-content">
+        <article role="main">
           
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@@ -297,8 +234,8 @@
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   </div>
 </div><script src="../_static/documentation_options.js?v=f2a433a1"></script>
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-    <script src="../_static/scripts/furo.js?v=4e2eecee"></script>
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\ No newline at end of file
diff --git a/docs/documentation/index.html b/docs/documentation/index.html
index 9f1f5286..dad9c69b 100644
--- a/docs/documentation/index.html
+++ b/docs/documentation/index.html
@@ -5,10 +5,10 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Getting Started with LIT" href="getting_started.html" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
         <title>🔥LIT 1.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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@@ -167,8 +113,6 @@
   <div class="visually-hidden">Hide table of contents sidebar</div>
 </label>
 
-<a class="skip-to-content muted-link" href="#furo-main-content">Skip to content</a>
-
 
 
 <div class="page">
@@ -186,8 +130,7 @@
       <div class="theme-toggle-container theme-toggle-header">
         <button class="theme-toggle">
           <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
@@ -243,17 +186,11 @@
           <span>Back to top</span>
         </a>
         <div class="content-icon-container">
-          <div class="view-this-page">
-  <a class="muted-link" href="_sources/index.md.txt" title="View this page">
-    <svg><use href="#svg-eye"></use></svg>
-    <span class="visually-hidden">View this page</span>
-  </a>
-</div>
+          
 <div class="theme-toggle-container theme-toggle-content">
             <button class="theme-toggle">
               <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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@@ -263,15 +200,15 @@
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="learning-interpretability-tool-lit">
-<h1>Learning Interpretability Tool (LIT)<a class="headerlink" href="#learning-interpretability-tool-lit" title="Link to this heading">¶</a></h1>
+<h1>Learning Interpretability Tool (LIT)<a class="headerlink" href="#learning-interpretability-tool-lit" title="Link to this heading">#</a></h1>
 <!--* freshness: { owner: 'lit-dev' reviewed: '2024-02-21' } *-->
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <p>Welcome to 🔥LIT, the Learning Interpretability Tool!</p>
 <p>If you want to jump in and start playing with the LIT UI, check out the hosted demos at <a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>.</p>
 <section id="research">
-<h2>Research<a class="headerlink" href="#research" title="Link to this heading">¶</a></h2>
+<h2>Research<a class="headerlink" href="#research" title="Link to this heading">#</a></h2>
 <p>Found LIT useful in your research? Please cite our
 <a class="reference external" href="https://aclanthology.org/2020.emnlp-demos.15/">system demonstration paper</a>!</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@misc</span><span class="p">{</span><span class="n">tenney2020language</span><span class="p">,</span>
@@ -301,6 +238,8 @@ <h2>Research<a class="headerlink" href="#research" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="demos.html#classification">Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --></a></li>
 <li class="toctree-l2"><a class="reference internal" href="demos.html#regression-scoring">Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --></a></li>
 <li class="toctree-l2"><a class="reference internal" href="demos.html#sequence-to-sequence">Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --></a></li>
+<li class="toctree-l2"><a class="reference internal" href="demos.html#language-modeling">Language Modeling <!-- DO NOT REMOVE {#lm .demo-section-header} --></a></li>
+<li class="toctree-l2"><a class="reference internal" href="demos.html#structured-prediction">Structured Prediction <!-- DO NOT REMOVE {#structured .demo-section-header} --></a></li>
 <li class="toctree-l2"><a class="reference internal" href="demos.html#multimodal">Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --></a></li>
 </ul>
 </li>
@@ -323,6 +262,7 @@ <h2>Research<a class="headerlink" href="#research" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="components.html#salience-clustering">Salience Clustering</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#tabular-feature-attribution">Tabular Feature Attribution</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#pixel-based-salience">Pixel-based Salience</a></li>
+<li class="toctree-l2"><a class="reference internal" href="components.html#attention">Attention</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#embedding-projector">Embedding Projector</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#aggregate-analysis">Aggregate Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="components.html#tcav">TCAV</a></li>
@@ -430,10 +370,8 @@ <h2>Research<a class="headerlink" href="#research" title="Link to this heading">
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
-    <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/documentation/search.html b/docs/documentation/search.html
index 3c64769b..1e5289dc 100644
--- a/docs/documentation/search.html
+++ b/docs/documentation/search.html
@@ -1,14 +1,11 @@
 <!doctype html>
 <html class="no-js" lang="en" data-content_root="./">
-  <head>
-<meta charset="utf-8"/>
+  <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="#" />
 
-    <!-- Generated with Sphinx 7.3.7 and Furo 2024.05.06 -->
-<meta name="robots" content="noindex" />
-<title>Search - 🔥LIT 1.0 documentation</title><link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=387cc868" />
+    <!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 --><title>Search - 🔥LIT 1.0 documentation</title><link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
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@@ -69,7 +66,7 @@
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     <title>Light mode</title>
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@@ -84,7 +81,7 @@
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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@@ -92,7 +89,7 @@
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     <title>Auto light/dark mode</title>
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-      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
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@@ -186,8 +127,7 @@
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-          <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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@@ -246,8 +186,7 @@
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-              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
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+        <article role="main">
           
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-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
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+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     
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diff --git a/docs/documentation/searchindex.js b/docs/documentation/searchindex.js
index b0ae447b..75ece15f 100644
--- a/docs/documentation/searchindex.js
+++ b/docs/documentation/searchindex.js
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             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
         </div>
-        <article role="main" id="furo-main-content">
+        <article role="main">
           <section class="tex2jax_ignore mathjax_ignore" id="ui-guide">
-<h1>UI Guide<a class="headerlink" href="#ui-guide" title="Link to this heading">¶</a></h1>
-<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } *-->
+<h1>UI Guide<a class="headerlink" href="#ui-guide" title="Link to this heading">#</a></h1>
+<!--* freshness: { owner: 'lit-dev' reviewed: '2023-08-23' } *-->
 <p>This is a user guide for the Learning Interpretability Tool (LIT) UI.</p>
 <p>For a quick video tour of LIT, check out this
 <a class="reference external" href="https://www.youtube.com/watch?v=CuRI_VK83dU">video</a>.</p>
 <!-- [TOC] placeholder - DO NOT REMOVE -->
 <section id="general-layout">
-<h2>General Layout<a class="headerlink" href="#general-layout" title="Link to this heading">¶</a></h2>
+<h2>General Layout<a class="headerlink" href="#general-layout" title="Link to this heading">#</a></h2>
 <p>LIT lives inside a single page web application, comprised of multiple toolbars
 and a main section consisting of individual modules. Modules will automatically
 display if they are applicable to the current model and dataset; for example,
@@ -290,7 +227,7 @@ <h2>General Layout<a class="headerlink" href="#general-layout" title="Link to th
 visualization vs. counterfactual example generation), but you can adopt whatever
 organizational scheme you desire in your custom layouts.</p>
 <section id="layout-options">
-<h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to this heading">¶</a></h3>
+<h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to this heading">#</a></h3>
 <!--
   TODO: Add an image of the 3 LIT layouts side by side.
 -->
@@ -301,7 +238,7 @@ <h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to th
 <li><p><code class="docutils literal notranslate"><span class="pre">default</span></code>: The original LIT layout with a single group of modules on top for
 exploring and selecting data, and a collection of tabs supporting different
 analytical tasks on the bottom; and</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">three_panel</span></code>: A three-panel layout that puts exploratory data
+<li><p><code class="docutils literal notranslate"><span class="pre">experimental</span></code>: A three-panel layout that puts exploratory data
 visualizations at full-page height on the left, tools for inspecting and
 manipulating examples and their associated predictions in the upper right,
 and a collection of tabs supporting different analytical tasks in the lower
@@ -311,7 +248,7 @@ <h3>Layout Options<a class="headerlink" href="#layout-options" title="Link to th
 </section>
 </section>
 <section id="datapoint-selections">
-<h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title="Link to this heading">¶</a></h2>
+<h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title="Link to this heading">#</a></h2>
 <p>LIT displays a loaded dataset and its model results across the set of selected
 models. Users can dive into detailed results by selecting datapoints from the
 dataset.</p>
@@ -341,16 +278,16 @@ <h2>Datapoint Selections<a class="headerlink" href="#datapoint-selections" title
 <em><a class="reference internal" href="#metrics-table">Metrics Module</a></em> section.</p>
 </section>
 <section id="toolbars">
-<h2>Toolbars<a class="headerlink" href="#toolbars" title="Link to this heading">¶</a></h2>
+<h2>Toolbars<a class="headerlink" href="#toolbars" title="Link to this heading">#</a></h2>
 <p>There are three toolbars provided in LIT. The top bar includes the selected
 model(s) and dataset, a settings button, and URL sharing functionality. Below
 that is the main toolbar with the menus and controls for navigation and
 selection. At the bottom of the page is a status bar.</p>
 <p><img alt="LIT toolbars" src="_images/lit-toolbars.png" /></p>
 <section id="top-bar">
-<h3>Top Bar<a class="headerlink" href="#top-bar" title="Link to this heading">¶</a></h3>
+<h3>Top Bar<a class="headerlink" href="#top-bar" title="Link to this heading">#</a></h3>
 <section id="global-settings">
-<h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to this heading">¶</a></h4>
+<h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to this heading">#</a></h4>
 <p>The global settings dialog is accessible through the <strong>“Configure”</strong> button in
 the top bar.</p>
 <p>LIT can be launched with a set of models and datasets. The settings screen
@@ -365,7 +302,7 @@ <h4>Global Settings<a class="headerlink" href="#global-settings" title="Link to
 <p><img alt="LIT global settings" src="_images/lit-settings.png" /></p>
 </section>
 <section id="url-sharing">
-<h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this heading">¶</a></h4>
+<h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this heading">#</a></h4>
 <p>Much of the LIT app’s state — the loaded models and datasets, selected
 datapoints, minimized and/or full-screen modules — is stored in URL
 parameters. The <strong>“Copy Link”</strong> button in the top bar allows a user to share
@@ -376,35 +313,25 @@ <h4>URL Sharing<a class="headerlink" href="#url-sharing" title="Link to this hea
 </section>
 </section>
 <section id="main-toolbar">
-<h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this heading">¶</a></h3>
+<h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this heading">#</a></h3>
 <p>The main toolbar is right below the top bar and contains a number of different
 controls and information. The left side of the toolbar contains a set of menus
 for quickly controlling datapoint selection and coloring. This includes the
 following controls:</p>
 <ul class="simple">
-<li><p>The <strong>“Select datapoint”</strong> menu provides a drop-down of several options:</p>
-<ul>
-<li><p>the <strong>“Random”</strong> option selects a random datapoint,</p></li>
-<li><p>the <strong>“All related”</strong> option adds any datapoints “related” to the
-current selection. In LIT, “related” is defined as datapoints created
-from some source datapoint (through manual editing or a datapoint
-generator), or a source datapoint that a selected datapoint was created
-from,</p></li>
-<li><p>the <strong>“Parents”</strong> option adds the source datapoints that the selected
-datapoints were created from,</p></li>
-<li><p>the <strong>“Children”</strong> option adds the datapoints created from the selected
-datapoints (through manual editing or a datapoint generator),</p></li>
-<li><p>the <strong>Slices</strong> option allows quick selection of an already-created slice
-of datapoints,</p></li>
-<li><p>the <strong>“Clear selection”</strong> button deselects all selected datapoints.</p></li>
-</ul>
-</li>
-<li><p>The <strong>“Color by”</strong> menu enables setting of the color of each datapoint in
-the modules that visualize all datapoints (such as the <em>Embeddings</em> and
-<em>Scalars</em> modules) by any number of datapoint features or model outputs on
-those datapoints (such as coloring by some categorical input feature, or by
-prediction error for a regression task).</p></li>
-<li><p>The <strong>Slices</strong> menu allows adding/selecting/removing slices of datapoints.</p></li>
+<li><p>The <strong>“Select related”</strong> option adds any datapoints “related” to the current
+selection. In LIT, “related” is defined as datapoints created from some
+source datapoint (through manual editing or a datapoint generator), or a
+source datapoint that a selected datapoint was created from.</p></li>
+<li><p>The <strong>“Clear selection”</strong> button deselects all selected datapoints. The
+dropdown also contains a color legend for the current color setting.</p></li>
+<li><p>The <strong>Slices</strong> option allows quick selection of an already-created slice of
+datapoints.</p></li>
+<li><p>The <strong>“Datapoint color”</strong> menu enables setting of the color of each
+datapoint in the modules that visualize all datapoints (such as the
+<em>Embeddings</em> and <em>Scalars</em> modules) by any number of datapoint features or
+model outputs on those datapoints (such as coloring by some categorical
+input feature, or by prediction error for a regression task).</p></li>
 </ul>
 <p>Next to the menus is a button for pinning/unpinning a datapoint. Pinning a
 datapoint puts LIT into datapoint comparison mode, where two datapoints can be
@@ -422,7 +349,7 @@ <h3>Main Toolbar<a class="headerlink" href="#main-toolbar" title="Link to this h
 to easily select all or none of the datapoints, respectively.</p>
 </section>
 <section id="status-bar">
-<h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this heading">¶</a></h3>
+<h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this heading">#</a></h3>
 <p>The status bar at the bottom of the tool contains a text area on the left side.
 If the tool is currently waiting on the results of a call to the backend (such
 as for running predictions or getting embeddings), this information will be
@@ -435,7 +362,7 @@ <h3>Status Bar<a class="headerlink" href="#status-bar" title="Link to this headi
 </section>
 </section>
 <section id="comparing-models">
-<h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link to this heading">¶</a></h2>
+<h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link to this heading">#</a></h2>
 <p>By loading more than one model in the global settings controls, LIT can compare
 multiple models. A subset of modules that show per-model information are then
 duplicated to allow easy comparison across two models. Other modules, such the
@@ -444,7 +371,7 @@ <h2>Comparing Models<a class="headerlink" href="#comparing-models" title="Link t
 <p><img alt="LIT model comparison" src="_images/lit-model-compare.png" /></p>
 </section>
 <section id="comparing-datapoints">
-<h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title="Link to this heading">¶</a></h2>
+<h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title="Link to this heading">#</a></h2>
 <p>Pinning a datapoint, through either the toolbar button or controls in modules
 (e.g., the pin icons in Data Table rows), puts LIT into <strong>datapoint comparison
 mode</strong>. In this mode, the pinned datapoint is used as a reference to compare the
@@ -459,7 +386,7 @@ <h2>Comparing Datapoints<a class="headerlink" href="#comparing-datapoints" title
 <p><img alt="LIT datapoint comparison" src="_images/lit-datapoint-compare.png" /></p>
 </section>
 <section id="slices">
-<h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">¶</a></h2>
+<h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">#</a></h2>
 <p>The <em>Slice Editor</em> allow users to create, edit, select, and delete slices. The
 current selection can be saved as a slice by giving it a name and clicking
 “Create slice”. The slice list allows you to select any of the previously-saved
@@ -478,14 +405,14 @@ <h2>Slices<a class="headerlink" href="#slices" title="Link to this heading">¶</
 <p><img alt="LIT slice controls" src="_images/lit-slices.png" /></p>
 </section>
 <section id="module-details">
-<h2>Module Details<a class="headerlink" href="#module-details" title="Link to this heading">¶</a></h2>
+<h2>Module Details<a class="headerlink" href="#module-details" title="Link to this heading">#</a></h2>
 <p>This section contains details on using and interacting with individual modules
 that are built into LIT. Note that this list may not be complete and additional
 modules can be created and used in LIT by clients.</p>
 <p>All modules can be toggled to be shown full-screen through use of the
 full-screen button in the top-right of each module.</p>
 <section id="embedding-projector">
-<h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">¶</a></h3>
+<h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="Link to this heading">#</a></h3>
 <p>When using LIT with a model that returns embeddings (or activations) in addition
 to predictions, the embedding projector will show all datapoints by their
 embeddings projected down to 3 dimensions. This is useful for exploring and
@@ -503,7 +430,7 @@ <h3>Embedding Projector<a class="headerlink" href="#embedding-projector" title="
 toolbar.</p>
 </section>
 <section id="data-table">
-<h3>Data Table<a class="headerlink" href="#data-table" title="Link to this heading">¶</a></h3>
+<h3>Data Table<a class="headerlink" href="#data-table" title="Link to this heading">#</a></h3>
 <p>The data table shows all datapoints in a simple table. Datapoints can be
 selected or unselected through a click. Shift+click allows selecting a set of
 consecutive datapoints, and control+click allows selecting a set of individual
@@ -556,12 +483,12 @@ <h3>Data Table<a class="headerlink" href="#data-table" title="Link to this headi
 such as model predictions, enable them from the “Columns” dropdown.</p>
 </section>
 <section id="datapoint-editor">
-<h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link to this heading">¶</a></h3>
+<h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link to this heading">#</a></h3>
 <p>The datapoint editor shows the details of the primary selected datapoint, if one
 is selected. Any field can be edited, and a new datapoint created with those
-edits through the <strong>“Add”</strong> button. Any edit to an existing datapoint must be
-saved as a new datapoint to be explored, to keep datapoints immutable for
-simplicity of use.</p>
+edits through the <strong>“Make new datapoint”</strong> button. Any edit to an existing
+datapoint must be saved as a new datapoint to be explored, to keep datapoints
+immutable for simplicity of use.</p>
 <p>When no datapoint is selected, the editor shows a blank datapoint that can be
 filled out by hand to create a completely new datapoint.</p>
 <p>Features shown with a “(*)” next to their name are required as model input and
@@ -569,7 +496,7 @@ <h3>Datapoint Editor<a class="headerlink" href="#datapoint-editor" title="Link t
 <p><a class="reference internal" href="_images/lit-datapoint-editor.png"><img alt="LIT datapoint editor" class="align-center" src="_images/lit-datapoint-editor.png" style="width: 500px;" /></a></p>
 </section>
 <section id="datapoint-generator">
-<h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="Link to this heading">¶</a></h3>
+<h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="Link to this heading">#</a></h3>
 <p>The datapoint generator module allows creation of new datapoints from all
 currently-selected datapoints (or the entire dataset if no datapoints are
 selected) through a set of counterfactual datapoint generators. These generators
@@ -604,7 +531,7 @@ <h3>Datapoint Generator<a class="headerlink" href="#datapoint-generator" title="
 <p><img alt="LIT datapoint generator" src="_images/lit-datapoint-generator.png" /></p>
 </section>
 <section id="metrics-table">
-<h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this heading">¶</a></h3>
+<h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this heading">#</a></h3>
 <p>The metrics table shows model metrics for each model in a table format. The
 exact metric types are determined by the python metrics component that
 calculates metrics given the model types being evaluated. These can include
@@ -626,7 +553,7 @@ <h3>Metrics Table<a class="headerlink" href="#metrics-table" title="Link to this
 <p><img alt="LIT metrics" src="_images/lit-metrics.png" /></p>
 </section>
 <section id="confusion-matrix">
-<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">¶</a></h3>
+<h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">#</a></h3>
 <p>The confusion matrix buckets all datapoints from the dataset (or the current
 selection, if one is made) into buckets in a 2D matrix. This is normally used to
 compare classification predictions on a model versus the ground truth classes of
@@ -644,7 +571,7 @@ <h3>Confusion Matrix<a class="headerlink" href="#confusion-matrix" title="Link t
 <p><img alt="LIT confusion matrix" src="_images/lit-conf-matrix.png" /></p>
 </section>
 <section id="scalars">
-<h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">¶</a></h3>
+<h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">#</a></h3>
 <p>The scalars module shows a set of scatter or jitter plots, one for each scalar
 output of a loaded model (such as a regression score, or a classification score
 for a specific class). Each of them contains all datapoints in the dataset, laid
@@ -668,7 +595,7 @@ <h3>Scalars<a class="headerlink" href="#scalars" title="Link to this heading">¶
 <p><img alt="LIT prediction scores" src="_images/lit-pred-score.png" /></p>
 </section>
 <section id="model-output">
-<h3>Model Output<a class="headerlink" href="#model-output" title="Link to this heading">¶</a></h3>
+<h3>Model Output<a class="headerlink" href="#model-output" title="Link to this heading">#</a></h3>
 <p>Model output modules show the result of a model on the primary selected
 datapoint. The visuals of these modules depend on the model task being
 performed. For a simple classification task, it will show the class scores from
@@ -681,7 +608,7 @@ <h3>Model Output<a class="headerlink" href="#model-output" title="Link to this h
 <p><a class="reference internal" href="_images/lit-structured-prediction.png"><img alt="LIT structured prediction" class="align-center" src="_images/lit-structured-prediction.png" style="width: 800px;" /></a></p>
 </section>
 <section id="salience-maps">
-<h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this heading">¶</a></h3>
+<h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this heading">#</a></h3>
 <p>Salience maps show the influence of different parts of inputs features on a
 model’s prediction on the primary selection. This module can contain multiple
 methodologies for calculating this salience, depending on the capabilities of
@@ -691,20 +618,28 @@ <h3>Salience Maps<a class="headerlink" href="#salience-maps" title="Link to this
 background of each text piece is colored by the salience of that piece on the
 prediction, and hovering on any piece will display the exact value calculated
 for that piece.</p>
-<p>There is an <strong>“autorun”</strong> button by each methodology on the right side of the
-bar (the methodoloy name is on the left side). If it is checked, then that
-calculation is made when a new primary datapoint is selected. If it is
+<p>There is an <strong>“autorun”</strong> button by each methodology. If it is checked, then
+that calculation is made when a new primary datapoint is selected. If it is
 unchecked, the calculation isn’t made until it is checked. This can be valuable
 so that expensive, long-running saliency calculations (such as LIME) aren’t
 performed on every datapoint selection, but only when explicitly asked for.</p>
 <p><img alt="LIT saliency maps" src="_images/lit-salience.png" /></p>
 </section>
+<section id="attention">
+<h3>Attention<a class="headerlink" href="#attention" title="Link to this heading">#</a></h3>
+<p>For models that return attention head information along with predictions, the
+attention module shows which tokens are attended to between layers of a model.
+Dropdowns allow you to select which layer and attention head is being
+visualized. Line opacity is controlled by the magnitude of the attention between
+those tokens.</p>
+<p><a class="reference internal" href="_images/lit-attention.png"><img alt="LIT attention" class="align-center" src="_images/lit-attention.png" style="width: 500px;" /></a></p>
+</section>
 </section>
 <section id="user-journeys">
-<h2>User Journeys<a class="headerlink" href="#user-journeys" title="Link to this heading">¶</a></h2>
+<h2>User Journeys<a class="headerlink" href="#user-journeys" title="Link to this heading">#</a></h2>
 <p>In this section, we explore some example user journeys and how LIT enables them.</p>
 <section id="sentiment-analysis">
-<h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Link to this heading">¶</a></h3>
+<h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Link to this heading">#</a></h3>
 <p>How well does a sentiment classifier handle negation? We load the development
 set of the Stanford Sentiment Treebank, and use the search function in LIT’s
 data table to find the 56 datapoints containing the word “not”. Looking at the
@@ -721,13 +656,48 @@ <h3>Sentiment Analysis<a class="headerlink" href="#sentiment-analysis" title="Li
 model.</p>
 <p><img alt="Sentiment analysis" src="_images/lit-sentiment-analysis.png" /></p>
 </section>
-<section id="sequence-salience">
-<h3>Sequence salience<a class="headerlink" href="#sequence-salience" title="Link to this heading">¶</a></h3>
-<p>Sequence salience generalizes token-based salience to text-to-text models,
-allowing you to explain the impact of the prompt tokens on parts of the model
-output.</p>
-<p>Check out <a class="reference internal" href="components.html#sequence-salience"><span class="std std-ref">here</span></a> for more details on how to
-navigate the Sequence Salience UI module.</p>
+<section id="gender-bias-in-coreference">
+<h3>Gender Bias in Coreference<a class="headerlink" href="#gender-bias-in-coreference" title="Link to this heading">#</a></h3>
+<p>Does a system encode gendered associations, which might lead to incorrect
+predictions? We load a coreference model trained on OntoNotes, and load the
+Winogender dataset into LIT for evaluation. Each Winogender example has a
+pronoun and two candidate referents, one a occupation term like (“technician”)
+and one is an “other participant” (like “customer”). Our model predicts
+coreference probabilities for each candidate. We can explore the model’s
+sensitivity to pronouns by comparing two examples side-by-side (see structured
+predictions in screenshot). We can see how commonly the model makes similar
+errors by paging through the dataset, or by selecting specific slices of
+interest.</p>
+<p><img alt="Winogender analysis" src="_images/lit-winogender.png" /></p>
+<p>In the <em>Metrics</em> table, we can slice this selection by pronoun type and by the
+true referent. On the set of male-dominated occupations (&lt; 25% female by BLS),
+we see the model performs well when the ground-truth agrees with the
+stereotype - e.g. when the answer is the occupation term, male pronouns are
+correctly resolved 83% of the time, compared to female pronouns only 37.5% of
+the time (screenshot section (c)).</p>
+<p><img alt="Winogender analysis" src="_images/lit-winogender-metrics.png" /></p>
+</section>
+<section id="debugging-text-generation">
+<h3>Debugging Text Generation<a class="headerlink" href="#debugging-text-generation" title="Link to this heading">#</a></h3>
+<!-- TODO(lit-dev): T5 no longer makes the mistake documented below. Find a
+     example that fits better with the text generation debugging story -->
+<p>Does the training data explain a particular error in text generation? We analyze
+an older T5 model on the CNN-DM summarization task. LIT’s <em>Scalars</em> module
+allows us to look at per-example ROUGE scores, and quickly select an example
+with middling performance (screenshot section (a)). We find the generated text
+(screenshot section (b)) contains an erroneous constituent: “alastair cook was
+replaced as captain by former captain …”. We can dig deeper, using LIT’s
+language modeling module (screenshot section (c)) to see that the token “by” is
+predicted with high probability (28.7%).</p>
+<p>To find out how T5 arrived at this prediction, we utilize the “similarity
+searcher” component through the datapoint generator (screenshot section (d)).
+This performs a fast approximate nearest-neighbor lookup from a pre-built index
+over the training corpus, using embeddings from the T5 decoder. With one click,
+we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection.
+We see that the words “captain” and “former” appear 34 and 16 times in these
+examples–along with 3 occurrences of “replaced by” (screenshot section (e)),
+suggesting a strong prior toward our erroneous phrase.</p>
+<p><a class="reference internal" href="_images/lit-s2s-journey.png"><img alt="LIT sequence-to-sequence analysis" class="align-center" src="_images/lit-s2s-journey.png" style="width: 500px;" /></a></p>
 </section>
 </section>
 </section>
@@ -816,11 +786,13 @@ <h3>Sequence salience<a class="headerlink" href="#sequence-salience" title="Link
 <li><a class="reference internal" href="#scalars">Scalars</a></li>
 <li><a class="reference internal" href="#model-output">Model Output</a></li>
 <li><a class="reference internal" href="#salience-maps">Salience Maps</a></li>
+<li><a class="reference internal" href="#attention">Attention</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#user-journeys">User Journeys</a><ul>
 <li><a class="reference internal" href="#sentiment-analysis">Sentiment Analysis</a></li>
-<li><a class="reference internal" href="#sequence-salience">Sequence salience</a></li>
+<li><a class="reference internal" href="#gender-bias-in-coreference">Gender Bias in Coreference</a></li>
+<li><a class="reference internal" href="#debugging-text-generation">Debugging Text Generation</a></li>
 </ul>
 </li>
 </ul>
@@ -835,8 +807,8 @@ <h3>Sequence salience<a class="headerlink" href="#sequence-salience" title="Link
     </aside>
   </div>
 </div><script src="_static/documentation_options.js?v=f2a433a1"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="_static/scripts/furo.js?v=4e2eecee"></script>
+    <script src="_static/scripts/furo.js?v=32e29ea5"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/index.html b/docs/index.html
index 65c7ceaa..d6b44ac4 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -110,6 +110,7 @@
   <h3>Built-in capabilities</h3>
   <div>
 <p>Salience maps</p>
+<p>Attention visualization</p>
 <p>Metrics calculations</p>
 <p>Counterfactual generation</p>
 <p>Model and datapoint comparison</p>
@@ -150,7 +151,7 @@ <h2>What's the latest</h2>
 <div class="mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--8-col-phone">
   <img class="home-card-image" src="/lit/assets/images/LIT_Updates.png"/>
   <div class="home-card-action">UPDATES</div>
-  <h3 class="home-card-title">Version 1.2</h3>
+  <h3 class="home-card-title">Version 1.1</h3>
   <div class="home-card-desc">Input salience for text-to-text LLMs, with wrappers for HuggingFace Transformers and KerasNLP models.</div>
   <div class="home-card-cta-button"><a href="https://github.com/PAIR-code/lit/blob/main/RELEASE.md" target="_blank" >See release notes</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
 </div>
diff --git a/docs/tutorials/coref/index.html b/docs/tutorials/coref/index.html
new file mode 100644
index 00000000..ea57ac9d
--- /dev/null
+++ b/docs/tutorials/coref/index.html
@@ -0,0 +1,187 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <!-- Do we need additional share metadata included here? -->
+
+    <!-- Global site tag (gtag.js) - Google Analytics -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-Q74F5RJLXB"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-Q74F5RJLXB');
+</script>
+
+    <title>Gender Bias in Coreference</title>
+    
+        <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
+    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
+    <link rel="stylesheet" href="/lit/assets/css/prism-material-light.css">
+    <link href="https://fonts.googleapis.com/css?family=Open+Sans:400,400italic,500,500italic,700,700italic" rel="stylesheet" type="text/css">
+    <link href="https://fonts.googleapis.com/css?family=Manrope:400,400italic,500,500italic,700,700italic" rel="stylesheet" type="text/css">
+
+    <!-- <link href='/lit/assets/css/main.css' rel='stylesheet' type='text/css'> -->
+    <link href='/lit/assets/css/new.css' rel='stylesheet' type='text/css'>
+    <link rel="icon" href="/lit/assets/images/favicon.png" type="image/png"/>
+
+  </head>
+
+  <body>
+    <div class="mdl-layout mdl-layout--no-desktop-drawer-button mdl-js-layout mdl-layout--fixed-header">
+      
+      <header class="mdl-layout__header">
+        <div class="mdl-layout__header-row">
+          <!-- Title -->
+          <img class='status-emoji' src="/lit/assets/images/favicon.png"></img>
+          <span class="mdl-layout__title">
+            <a href="/lit/">
+              Learning Interpretability Tool
+            </a>
+          </span>
+          <!-- Add spacer, to align navigation to the right -->
+          <div class="mdl-layout-spacer"></div>
+          <!-- Navigation. We hide it in small screens. -->
+          <nav class="mdl-navigation mdl-layout--large-screen-only">
+            <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
+            <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
+            <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
+            <a class="mdl-navigation__link" href="/lit/documentation/">DOCUMENTATION</a>
+            <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
+            <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="-_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+            <a class="mdl-navigation__link" href="https://github.com/pair-code/lit" target="-_blank">GITHUB<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+          </nav>
+        </div>
+      </header>
+      <div class="mdl-layout__drawer">
+        <span class="mdl-layout__title"><a href="/lit/">Learning Interpretability Tool</a></span>
+        <nav class="mdl-navigation">
+          <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
+          <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
+          <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
+          <a class="mdl-navigation__link" href="/lit/documentation/">DOCUMENTATION</a>
+          <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
+          <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+          <a class="mdl-navigation__link" href="https://github.com/pair-code/lit" target="-_blank">GITHUB<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+        </nav>
+      </div>
+
+      
+      <main class="mdl-layout__content hero-banner">
+        <div class="tutorial-page-container mdl-grid">
+          <div class="mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
+            <div class="tutorial-breadcrumbs">
+  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Coreference
+</div>
+            <h2>Gender Bias in Coreference</h2>
+<div class="link-out"><a href="../../demos/coref.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py"><code>examples/coref/coref_demo.py</code></a></p>
+<p>Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence &quot;The technician told the customer that they could pay with cash.&quot;, we understand from the context that &quot;they&quot; refers to &quot;the customer&quot;, the one paying.</p>
+<p>The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:</p>
+<ul>
+<li>&quot;The technician told the customer that <strong>he</strong> could pay with cash.&quot;</li>
+<li>&quot;The technician told the customer that <strong>she</strong> could pay with cash.&quot;</li>
+</ul>
+<p>In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.</p>
+<p>We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-pred.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-pred.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: A coreference prediction.</div>
+</div>
+<p>We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the &quot;Predictions&quot; tab below. To look at two predictions side-by-side, we can enable &quot;Compare datapoints&quot; mode in the toolbar, which will pin our first selection as a &quot;reference&quot; and allow us to select another point to compare:</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-select.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-select.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Selecting two datapoints to compare.</div>
+</div>
+<p>We see that LIT automatically replicates the predictions view, allowing us to see how our model handles &quot;he&quot; and &quot;she&quot; differently on these two sentences:</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-compare.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-compare.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Comparing coreference predictions of two datapoints.</div>
+</div>
+<p>To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-data.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-data.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Datapoints with extra informational feature columns.</div>
+</div>
+<p>We see that &quot;technician&quot; is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify &quot;he&quot; as the technician in the example above.</p>
+<p>Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the &quot;Performance&quot; tab, we see that our model gets around 63% accuracy overall.</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-metric-top.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-metric-top.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Overall model accuracy in the metrics table.</div>
+</div>
+<p>Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (&lt; 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-coref-metrics.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-coref-metrics.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Metrics faceted into sub-groups and scalar results plots.</div>
+</div>
+<p>We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.</p>
+
+          </div>
+          <div class="mdl-cell--4-col hide-me">
+            <div class="tutorial-info-container">
+              <div class="tutorial-info-header">time to read</div>
+              <div class="tutorial-info-copy">10 minutes</div>
+              <div class="tutorial-info-header">takeaways</div>
+              <div class="tutorial-info-copy">Learn about how to explore fairness using datapoint comparison and metrics comparisons.</div>
+            </div>
+          </div>
+          <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
+            The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our <a href="https://github.com/pair-code/lit/issues/new" target="_blank">Github</a>.
+          </div>
+        </div>
+
+        <div class="footer-container mdl-grid">
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+  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone">
+    <a href="https://research.google/teams/language/" target="_blank">Google Research - Language</a>
+  </div>
+  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone"><a href="https://github.com/pair-code" target="_blank">Github</a></div>
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+    <a href="https://medium.com/people-ai-research" target="_blank"><img src="/lit/assets/images/medium.png"/></a>
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+  </div>
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+
+      </main>
+
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+  </body>
+
+  
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+  <script defer src="/lit/assets/js/material.min.js"></script>
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diff --git a/docs/tutorials/generation/index.html b/docs/tutorials/generation/index.html
new file mode 100644
index 00000000..6647a8b1
--- /dev/null
+++ b/docs/tutorials/generation/index.html
@@ -0,0 +1,149 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <!-- Do we need additional share metadata included here? -->
+
+    <!-- Global site tag (gtag.js) - Google Analytics -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-Q74F5RJLXB"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-Q74F5RJLXB');
+</script>
+
+    <title>Debugging a Text Generator</title>
+    
+        <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
+    <link rel="stylesheet" href="/lit/assets/css/material.min.css">
+    <link rel="stylesheet" href="/lit/assets/css/prism-material-light.css">
+    <link href="https://fonts.googleapis.com/css?family=Open+Sans:400,400italic,500,500italic,700,700italic" rel="stylesheet" type="text/css">
+    <link href="https://fonts.googleapis.com/css?family=Manrope:400,400italic,500,500italic,700,700italic" rel="stylesheet" type="text/css">
+
+    <!-- <link href='/lit/assets/css/main.css' rel='stylesheet' type='text/css'> -->
+    <link href='/lit/assets/css/new.css' rel='stylesheet' type='text/css'>
+    <link rel="icon" href="/lit/assets/images/favicon.png" type="image/png"/>
+
+  </head>
+
+  <body>
+    <div class="mdl-layout mdl-layout--no-desktop-drawer-button mdl-js-layout mdl-layout--fixed-header">
+      
+      <header class="mdl-layout__header">
+        <div class="mdl-layout__header-row">
+          <!-- Title -->
+          <img class='status-emoji' src="/lit/assets/images/favicon.png"></img>
+          <span class="mdl-layout__title">
+            <a href="/lit/">
+              Learning Interpretability Tool
+            </a>
+          </span>
+          <!-- Add spacer, to align navigation to the right -->
+          <div class="mdl-layout-spacer"></div>
+          <!-- Navigation. We hide it in small screens. -->
+          <nav class="mdl-navigation mdl-layout--large-screen-only">
+            <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
+            <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
+            <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
+            <a class="mdl-navigation__link" href="/lit/documentation/">DOCUMENTATION</a>
+            <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
+            <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="-_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+            <a class="mdl-navigation__link" href="https://github.com/pair-code/lit" target="-_blank">GITHUB<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+          </nav>
+        </div>
+      </header>
+      <div class="mdl-layout__drawer">
+        <span class="mdl-layout__title"><a href="/lit/">Learning Interpretability Tool</a></span>
+        <nav class="mdl-navigation">
+          <a class="mdl-navigation__link" href="/lit/documentation/getting_started.html">GETTING STARTED</a>
+          <a class="mdl-navigation__link" href="/lit/tutorials/">TUTORIALS</a>
+          <a class="mdl-navigation__link" href="/lit/demos/">DEMOS</a>
+          <a class="mdl-navigation__link" href="/lit/documentation/">DOCUMENTATION</a>
+          <a class="mdl-navigation__link" href="/lit/documentation/faq.html">FAQs</a>
+          <a class="mdl-navigation__link" href="https://groups.google.com/g/lit-annoucements" target="_blank">STAY UP TO DATE<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+          <a class="mdl-navigation__link" href="https://github.com/pair-code/lit" target="-_blank">GITHUB<img class="header-arrow" src="/lit/assets/images/arrow-link-out.png"/></a>
+        </nav>
+      </div>
+
+      
+      <main class="mdl-layout__content hero-banner">
+        <div class="tutorial-page-container mdl-grid">
+          <div class="mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
+            <div class="tutorial-breadcrumbs">
+  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Generator
+</div>
+            <h2>Debugging a Text Generator</h2>
+<div class="link-out"><a href="../../demos/t5.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py"><code>examples/t5_demo.py</code></a></p>
+<p>Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-t5.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-t5.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: The datapoint of interest, with its inaccurate summarization.</div>
+</div>
+<p>We find the generated text contains an erroneous subject: &quot;[A]lastair [C]ook was replaced as captain by former captain ...&quot;. In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-sim-search.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-sim-search.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: Finding the most similar datapoints from the training set.</div>
+</div>
+<p>To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings
+from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.</p>
+<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
+  <img class="tutorial-image" src="/lit/assets/images/lit-datatable-search.png"
+    onclick="event.target.nextElementSibling.showPopover()" />
+  <div class="tutorial-image-popover" popover>
+    <img class="tutorial-image" src="/lit/assets/images/lit-datatable-search.png"
+      onclick="event.target.parentElement.hidePopover()" />
+  </div>
+  <div class="tutorial-caption">Above: An example of the existence of "by former" in a similar datapoint from the training set.</div>
+</div>
+
+          </div>
+          <div class="mdl-cell--4-col hide-me">
+            <div class="tutorial-info-container">
+              <div class="tutorial-info-header">time to read</div>
+              <div class="tutorial-info-copy">3 minutes</div>
+              <div class="tutorial-info-header">takeaways</div>
+              <div class="tutorial-info-copy">Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set.</div>
+            </div>
+          </div>
+          <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
+            The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our <a href="https://github.com/pair-code/lit/issues/new" target="_blank">Github</a>.
+          </div>
+        </div>
+
+        <div class="footer-container mdl-grid">
+  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone"><a href="https://pair.withgoogle.com/" target="_blank"><img src="/lit/assets/images/pair-logo.svg"/></a></div>
+  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone">
+    <a href="https://research.google/teams/language/" target="_blank">Google Research - Language</a>
+  </div>
+  <div class="mdl-cell mdl-cell--2-col mdl-cell--2-col-tablet mdl-cell--4-col-phone"><a href="https://github.com/pair-code" target="_blank">Github</a></div>
+  <div class="footer-icons mdl-cell mdl-cell--4-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
+    <a href="mailto:peopleai@google.com" target="_blank"><img src="/lit/assets/images/mail.png"/></a>
+    <a href="https://medium.com/people-ai-research" target="_blank"><img src="/lit/assets/images/medium.png"/></a>
+    <a href="https://www.youtube.com/channel/UCnnns-uu4yy9BXfYSPIX5AA" target="_blank"><img src="/lit/assets/images/youtube.png"/></a>
+  </div>
+</div>
+
+      </main>
+
+    </div>
+  </body>
+
+  
+
+  <script defer src="/lit/assets/js/material.min.js"></script>
+  <script defer src="/lit/assets/js/material-components-web.min.js"></script>
+</html>
diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html
index c415c6ce..d1d43d44 100644
--- a/docs/tutorials/index.html
+++ b/docs/tutorials/index.html
@@ -115,6 +115,14 @@ <h4 class="tutorial-link-title"><a href="/lit/tutorials/tcav">Global Model Analy
   <h4 class="tutorial-link-title"><a href="/lit/tutorials/sentiment">Exploring a Sentiment Classifier</a></h4>
   <div class="tutorial-link-copy">Learn about how we used LIT to analyze a sentiment classifier.</div>
 </div>
+<div class="tutorial-link-element">
+  <h4 class="tutorial-link-title"><a href="/lit/tutorials/generation">Debugging a Text Generator</a></h4>
+  <div class="tutorial-link-copy">Learn about how we used LIT to debug summarization by a text generation model.</div>
+</div>
+<div class="tutorial-link-element">
+  <h4 class="tutorial-link-title"><a href="/lit/tutorials/coref">Gender Bias in Coreference</a></h4>
+  <div class="tutorial-link-copy">Learn how we used LIT to explore gendered associations in a pronoun resolution model.</div>
+</div>
 <div class="spacer" style="height:50px;"></div>
 </div>
 
diff --git a/docs/tutorials/sentiment/index.html b/docs/tutorials/sentiment/index.html
index 1ee96c27..017f1694 100644
--- a/docs/tutorials/sentiment/index.html
+++ b/docs/tutorials/sentiment/index.html
@@ -77,7 +77,7 @@
 </div>
             <h2>Exploring a Sentiment Classifier</h2>
 <div class="link-out"><a href="../../demos/glue.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code>examples/glue/demo.py</code></a></p>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code>examples/glue_demo.py</code></a></p>
 <p>How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.</p>
 <p>Using the search function in LIT’s data table, we find the 67 datapoints containing the word “not”. By selecting these datapoints and looking at the Metrics Table, we find that our BERT model gets 91% of these correct, which is slightly higher than the accuracy across the entire dataset.</p>
 <div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
diff --git a/docs/tutorials/sequence-salience/index.html b/docs/tutorials/sequence-salience/index.html
index e499ef9a..9e76d614 100644
--- a/docs/tutorials/sequence-salience/index.html
+++ b/docs/tutorials/sequence-salience/index.html
@@ -14,7 +14,7 @@
   gtag('config', 'G-Q74F5RJLXB');
 </script>
 
-    <title>Prompt Debugging with Sequence Salience</title>
+    <title>Prompt Engineering with Sequence Salience</title>
     
         <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
     <link rel="stylesheet" href="/lit/assets/css/material.min.css">
@@ -73,11 +73,11 @@
         <div class="tutorial-page-container mdl-grid">
           <div class="mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
             <div class="tutorial-breadcrumbs">
-  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Prompt Debugging with Sequence Salience
+  <a href="/lit/tutorials">Tutorials</a> > <a href="/lit/tutorials/#analysis">Analysis</a> > Prompt Engineering with Sequence Salience
 </div>
-            <h2>Prompt Debugging with Sequence Salience</h2>
+            <h2>Prompt Engineering with Sequence Salience</h2>
 <div class="link-out"><a href="https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb" target="_blank">Follow along in Google Colab.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run this locally with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py"><code>examples/prompt_debugging/server.py</code></a></p>
+<p>Or, run this locally with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py"><code>examples/lm_salience_demo.py</code></a></p>
 <p>Large language models (LLMs), such as <a href="https://gemini.google.com/">Gemini</a> and <a href="https://arxiv.org/abs/2303.08774">GPT-4</a>, have
 become ubiquitous. Recent releases of &quot;open weights&quot; models, including
 <a href="https://llama.meta.com/">Llama 2</a>, <a href="https://mistral.ai/news/announcing-mistral-7b/">Mistral</a>, and <a href="https://ai.google.dev/gemma">Gemma</a>, have made it easier
@@ -294,7 +294,8 @@ <h2>Case Study 2: Assessing Constitutional Principles in Prompts</h2>
 them in prompt engineering use cases.</p>
 <h2>Case Study 3: Side-by-Side Behavior Comparisons</h2>
 <p>LIT support a <a href="../../documentation/ui_guide.html#comparing-datapoints">side-by-side (SxS) mode</a> that can be used to compare two
-models, or here, compare model behavior on two related examples.
+models, or here, compare model behavior on two related examples. (Check out the
+<a href="../../tutorials/coref/">gender bias tutorial</a> for another case study in SxS analysis.)
 Let's see how we can use this to understand differences in prompt designs with
 Sequence Salience.</p>
 <p><a href="https://github.com/openai/grade-school-math">GSM8K</a> is a benchmark dataset of grade school math problems commonly
@@ -440,8 +441,8 @@ <h2>Conclusion</h2>
 <a href="https://dl.acm.org/doi/full/10.1145/3639372">research</a> area. The LIT team has provided reference
 implementations for computing gradient-based salience—
 <a href="https://aclanthology.org/P18-1032/">Grad L2 Norm</a> and <a href="https://arxiv.org/abs/1412.6815">Grad · Input</a>—for LLMs in two
-popular frameworks: <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py">KerasNLP</a> and
-<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py">HuggingFace Transformers</a>.</p>
+popular frameworks: <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py">KerasNLP</a> and
+<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py">HuggingFace Transformers</a>.</p>
 <p>There is considerable opportunity to research how the model analysis foundations
 described in this tutorial can support richer workflows, particularly as they
 relate to aggregate analysis of salience results over many examples, and the
@@ -468,7 +469,7 @@ <h3>Further Reading</h3>
               <div class="tutorial-info-header">time to read</div>
               <div class="tutorial-info-copy">20 minutes</div>
               <div class="tutorial-info-header">takeaways</div>
-              <div class="tutorial-info-copy">Learn to use LIT's Sequence Salience module for prompt debugging.</div>
+              <div class="tutorial-info-copy">Learn to use LIT's Sequence Salience module for prompt engineering.</div>
             </div>
           </div>
           <div class="tutorial-footer mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
diff --git a/docs/tutorials/tab-feat-attr/index.html b/docs/tutorials/tab-feat-attr/index.html
index 777b5603..5a6d8e6a 100644
--- a/docs/tutorials/tab-feat-attr/index.html
+++ b/docs/tutorials/tab-feat-attr/index.html
@@ -78,7 +78,7 @@
             <h2>Tabular Feature Attribution</h2>
 <div class="link-out"><a href="../../demos/penguins.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
 <p>Or, run your own with
-<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py"><code>examples/penguin/demo.py</code></a></p>
+<a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py"><code>examples/penguin_demo.py</code></a></p>
 <p>LIT supports many techniques like salience maps and counterfactual generators
 for text data. But what if you have a tabular dataset? You might want to find
 out which features (columns) are most relevant to the model’s predictions. LIT's
diff --git a/docs/tutorials/text-salience/index.html b/docs/tutorials/text-salience/index.html
index 58e807a8..aa5cabf7 100644
--- a/docs/tutorials/text-salience/index.html
+++ b/docs/tutorials/text-salience/index.html
@@ -77,7 +77,7 @@
 </div>
             <h2>Salience Maps for Text</h2>
 <div class="link-out"><a href="../../demos/glue.html" target="_blank">Explore this demo yourself.</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
-<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py"><code>examples/glue/demo.py</code></a></p>
+<p>Or, run your own with <a href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py"><code>examples/glue_demo.py</code></a></p>
 <p>LIT enables users to analyze individual predictions for text input using
 salience maps, for which gradient-based and/or blackbox methods are available.
 In this tutorial, we will explore how to use salience maps to analyze a text
diff --git a/pyproject.toml b/pyproject.toml
index 13a7a967..e2c1d89e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "lit-nlp"
-version = "1.1.1"
+version = "1.2"
 authors = [
   { name="Google, LLC", email="lit-dev@google.com" }
 ]