# else range(self.train_size)),
sampler=SubsetRandomSampler(train_idx),
batch_size=self.batch_size,
-
**self.loader_kwargs
+
**self.loader_kwargs,
)
optimizer = optim.SGD(self.model.parameters(), lr=self.lr, momentum=self.momentum)
@@ -907,7 +907,7 @@
Source code for cleanlab.experimental.mnist_pytorch
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=self.batch_size if loader == "train" else self.test_batch_size,
- **self.loader_kwargs
+ **self.loader_kwargs,
)
# sets model.train(False) inactivating dropout and batch-norm layers
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index e8b96997e..c20fa5a24 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 0c5439356..f93d85194 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index e84b4abb0..d83c1de96 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index edb036fe0..ca27b1a80 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index b0299884e..a53343c2d 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index e6695b793..8521f83b6 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index 0ccc1f5fe..cc445bb7f 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index 148853215..9bb3396cb 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index a84aabe3a..8718edcba 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index f5795c029..750d9827f 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 277af2571..f4e13a019 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index 7e3adbe9a..2b1da8cf8 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index 4e4779d95..5e0d39b45 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index 51912f56c..9e1902720 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index d6d1c487e..1bb2a7125 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index f1b32a30f..119a6560d 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bdde87529e35d2d81b9d149e4019128f1a3b520c\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e61c9bd9636b009dfd596ed665dc379eb201c298\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index 76724ecc6..472a459a0 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Use cleanlab to find label issues": [[80, "5.-Use-cleanlab-to-find-label-issues"], [84, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[81, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[81, "Install-and-import-required-dependencies"]], "Create and load the data": [[81, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[81, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[81, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[81, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[81, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[81, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[81, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[82, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. 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Use cleanlab to find issues in your dataset": [[85, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[85, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[86, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[86, "Install-dependencies-and-import-them"], [89, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[86, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[86, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[87, "FAQ"]], "What data can cleanlab detect issues in?": [[87, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[87, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[87, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[87, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[87, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[87, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[87, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[87, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[87, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[87, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[87, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[87, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[87, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[87, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[89, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[89, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[89, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[89, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[89, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[89, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[89, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[89, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[89, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[89, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[89, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[89, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[89, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[89, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[89, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[89, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[89, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[89, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[89, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[89, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[89, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[89, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[90, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[91, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[91, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[91, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[91, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[91, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[91, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[91, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[91, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[91, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[92, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[92, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[92, "2.-Format-data,-labels,-and-model-predictions"], [93, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[92, "3.-Use-cleanlab-to-find-label-issues"], [93, "3.-Use-cleanlab-to-find-label-issues"], [97, "3.-Use-cleanlab-to-find-label-issues"], [100, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[92, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[92, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[92, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[92, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[92, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[93, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. 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Define a regression model and use cleanlab to find potential label errors": [[96, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[96, "4.-Train-a-more-robust-model-from-noisy-labels"], [99, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[96, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[97, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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"cleanlab.token_classification.summary.filter_by_token"]]}})
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Select a classification model and compute out-of-sample predicted probabilities": [[84, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [98, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[84, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[84, "Label-issues"], [85, "Label-issues"], [88, "Label-issues"]], "Outlier issues": [[84, "Outlier-issues"], [85, "Outlier-issues"], [88, "Outlier-issues"]], "Near-duplicate issues": [[84, "Near-duplicate-issues"], [85, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[85, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[85, "2.-Load-and-format-the-text-dataset"], [99, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[85, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[85, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[85, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[86, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[86, "Install-dependencies-and-import-them"], [89, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[86, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[86, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[87, "FAQ"]], "What data can cleanlab detect issues in?": [[87, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[87, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[87, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[87, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[87, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[87, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[87, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[87, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[87, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[87, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[87, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[87, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[87, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[87, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[89, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[89, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[89, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[89, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[89, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[89, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[89, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[89, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[89, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[89, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[89, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[89, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[89, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[89, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[89, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[89, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[89, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[89, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[89, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[89, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[89, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[89, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[90, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[91, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[91, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[91, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[91, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[91, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[91, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[91, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[91, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[91, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[92, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[92, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[92, "2.-Format-data,-labels,-and-model-predictions"], [93, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[92, "3.-Use-cleanlab-to-find-label-issues"], [93, "3.-Use-cleanlab-to-find-label-issues"], [97, "3.-Use-cleanlab-to-find-label-issues"], [100, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[92, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[92, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[92, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[92, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[92, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[93, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[93, "1.-Install-required-dependencies-and-download-data"], [97, "1.-Install-required-dependencies-and-download-data"], [100, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[93, "Get-label-quality-scores"], [97, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[93, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[93, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[93, "Other-uses-of-visualize"]], "Exploratory data analysis": [[93, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[94, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[94, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[94, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[94, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[94, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[94, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[95, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[95, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[95, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[96, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[96, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[96, "4.-Train-a-more-robust-model-from-noisy-labels"], [99, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[96, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[97, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[97, "2.-Get-data,-labels,-and-pred_probs"], [100, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[97, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[97, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[97, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[98, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[98, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[98, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[99, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[99, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[100, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[100, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[100, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[100, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[100, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [10, 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"randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, 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"get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[66, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[67, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[68, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[68, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[68, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.fit"]], 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diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 773e2ac16..52ba33148 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1284,7 +1284,7 @@
5. Use cleanlab to find label issues
-
+