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diff --git a/generate_prob.py b/generate_prob.py
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--- /dev/null
+++ b/generate_prob.py
@@ -0,0 +1,179 @@
+import json
+import csv
+import string
+import re
+import gzip
+import collections
+import math
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+from transformers import BertTokenizer,DebertaTokenizerFast, DebertaV2TokenizerFast, AutoTokenizer
+
+from src.data import read_feedback_examples_and_features, get_feedback_data 
+from model import BertForQuestionAnswering, DebertaSQuAD2, BertForQuestionAnsweringSequence
+from src_utils.merge_data import merge
+from src.eval import RawResult, normalize_answer
+import argparse
+
+
+def get_batch_log_prob(start_probs, end_probs, start_samples, end_samples):
+    bs = start_samples.shape[0]
+    ignored_index = start_probs.size(1)
+    start_samples.clamp_(0, ignored_index)
+    end_samples.clamp_(0, ignored_index)
+    log_prob = start_probs[torch.arange(bs), start_samples].log() + end_probs[torch.arange(bs),
+                                                                          end_samples].log()
+    return log_prob
+
+
+def load_initialization(model, ckpt_name):
+    ckpt = torch.load(ckpt_name)
+
+    model.load_state_dict(ckpt['model_state_dict'])
+    print("Loaded the model state from a saved checkpoint {}".format(ckpt_name))
+    return model
+
+def main(train_batches, model, device, add_classifier):
+    total = 0
+    log_probs = []
+    class_log_probs = []
+    for step, batch in enumerate(train_batches):
+        batch = tuple(t.to(device) for t in batch)
+
+        input_ids, input_mask, segment_ids, start_samples, end_samples, rewards = batch
+        with torch.no_grad():
+            start_probs, end_probs, class_prob = model(batch=batch[:3], return_prob=True, classifier=add_classifier)
+            if args.add_classifier:
+                class_log_prob = class_prob.log()
+
+                class_sample = class_log_prob.argmax(dim=-1).item()
+
+        log_prob = get_batch_log_prob(
+            start_probs, end_probs, start_samples, end_samples)
+
+        log_probs.append(log_prob)
+        if args.add_classifier:
+            class_log_probs.append(class_log_prob[:, class_sample])
+        total += input_ids.size(0)
+
+    print('='*50)
+    print('[logging] Total: %d'%(total))
+    print('='*50)
+
+    if add_classifier:
+        return torch.cat(log_probs, dim=0), torch.cat(class_log_probs, dim=0)
+    else:
+        return torch.cat(log_probs, dim=0), None
+
+
+    
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default='deepset/deberta-v3-base-squad2', type=str)
+    parser.add_argument("--data_file", default=None, type=str, required=True, help='data you wish to generate prob from')
+    parser.add_argument("--checkpoint", default=None, type=str, required=True)
+    parser.add_argument("--add_classifier", action='store_true')
+    parser.add_argument(
+        "--outfile",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model checkpoints and predictions will be written.")
+    args = parser.parse_args()
+
+
+    #### initialization ####
+    model_type = args.model
+    data_file = args.data_file
+    outfile = args.outfile
+    checkpoint = args.checkpoint
+    add_classifier = args.add_classifier
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    batch_size = 1
+
+
+    # tokenization and dataset
+    if model_type == 'deepset/deberta-v3-base-squad2':
+        tokenizer = AutoTokenizer.from_pretrained(model_type, return_offsets_mapping=True)
+    elif 'v3' in model_type:
+        tokenizer = DebertaV2TokenizerFast.from_pretrained(model_type, return_offsets_mapping=True)
+    else:
+        tokenizer = DebertaTokenizerFast.from_pretrained(model_type, return_offsets_mapping=True)
+    
+    train_dataset = get_feedback_data(data_file)  # original train data
+
+    # load model
+    if model_type == "deepset/deberta-v3-base-squad2":
+        model = DebertaSQuAD2(model_type=model_type)
+    elif model_type == 'microsoft/deberta-v3-base':
+        if args.add_classifier:
+            model = BertForQuestionAnsweringSequence(model_type=model_type)
+        else:
+            model = BertForQuestionAnswering(model_type=model_type)
+    if checkpoint:
+        model = load_initialization(model, checkpoint)
+    model = model.to(device)
+
+    # processing examples
+    train_examples, train_features = read_feedback_examples_and_features(input_data=train_dataset,
+                                                                        negative_reward=-0.1,
+                                                                        partial_reward=0.5,
+                                                                        reward_wrong_unans=-1,
+                                                                        reward_correct_span=1,
+                                                                        reward_correct_unans=1,
+                                                                        reward_class_wrong=0,
+                                                                        reward_class_correct_ans=1,
+                                                                        tokenizer=tokenizer,
+                                                                        max_seq_length=512,
+                                                                        prepend_title=True,
+                                                                        load_log_prob=False)
+
+    # read_feedback_examples_and_features(train_dataset, -0.1, 0.5, -1, 1, 1, tokenizer, 512, True)
+    all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
+    all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
+    all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
+    
+    all_start_samples = torch.tensor([f.start_sample for f in train_features], dtype=torch.long)
+    all_end_samples = torch.tensor([f.end_sample for f in train_features], dtype=torch.long)
+    all_rewards = torch.tensor([f.reward for f in train_features], dtype=torch.float)
+
+    data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_samples, all_end_samples, all_rewards)
+    print("***** Train *****")
+    print("  Num examples = %d"%len(train_features))
+    print("  Batch size = %d"%batch_size)
+
+    train_dataloader = DataLoader(data, batch_size=batch_size)
+    train_batches = [batch for batch in train_dataloader]
+
+
+    # main function
+    log_probs, class_log_probs = main(train_batches, model, device, add_classifier)
+    print(log_probs.size(), len(train_dataset))
+    assert log_probs.size(0) == len(train_dataset)
+    if args.add_classifier:
+        assert class_log_probs.size(0) == len(train_dataset)
+        print(class_log_probs.size())
+
+    for i, inst in enumerate(train_dataset):
+        inst['log_prob'] = log_probs[i].item()
+        # print(class_log_probs[i])
+        if args.add_classifier:
+            inst['class_log_prob'] = class_log_probs[i].item()
+        else:
+            inst['class_log_prob'] = 0
+
+    print(train_dataset[0])
+
+    # write data
+    fw = open(outfile, 'w')
+    for l in train_dataset:
+        fw.write(json.dumps(l) + '\n')
+    fw.close()
+
+
+
+
diff --git a/model.py b/model.py
new file mode 100644
index 0000000..c2883d8
--- /dev/null
+++ b/model.py
@@ -0,0 +1,128 @@
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from transformers import BertModel, AutoModel, AutoModelForQuestionAnswering
+import torch
+
+
+class BertForQuestionAnswering(nn.Module):
+    def __init__(self, model_type: str):
+        super(BertForQuestionAnswering, self).__init__()
+        if 'deberta' in model_type:
+            self.bert = AutoModel.from_pretrained(model_type)
+        elif 'bert-' in model_type:
+            self.bert = BertModel.from_pretrained(model_type)
+        else:
+            raise ValueError('Model type!')
+
+        self.qa_outputs = nn.Linear(self.bert.config.hidden_size, 2)  # [N, L, H] => [N, L, 2]
+        self.classifier_coeff = 10
+        self.entropy_penalty = 0
+        # print(self.classifier_coeff)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, batch, classifier=False, return_prob=False, **kwargs):
+        '''
+        each batch is a list of 5 items (training) or 3 items (inference)
+            - input_ids: token id of the input sequence
+            - attention_mask: mask of the sequence (1 for present, 0 for blank)
+            - token_type_ids: indicator of type of sequence.
+            -      e.g. in QA, whether it is question or document
+            - (training) start_positions: list of start positions of the span
+            - (training) end_positions: list of end positions of the span
+        '''
+
+        input_ids, attention_masks, token_type_ids = batch[:3]
+        # pooler_output, last_hidden_state
+        output = self.bert(input_ids=input_ids,
+                           token_type_ids=token_type_ids,
+                           attention_mask=attention_masks)
+        sequence_output = output.last_hidden_state
+        logits = self.qa_outputs(sequence_output)  # (bs, max_input_len, 2)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)  # (bs, max_input_len)
+        end_logits = end_logits.squeeze(-1)  # (bs, max_input_len)
+
+        if len(batch) == 5:
+            start_positions, end_positions = batch[3:]
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+            
+            if classifier:
+                answerable_mask = (start_positions != 0) | (end_positions != 0)
+                loss_fct = CrossEntropyLoss()
+                
+                answerable_logits = self.classification(sequence_output)[:, 0]
+                classifier_loss = loss_fct(answerable_logits, answerable_mask.long())
+                total_loss += self.classifier_coeff * classifier_loss
+                answerable_prob = torch.softmax(answerable_logits, dim=-1)
+
+                total_loss += self.entropy_penalty * (-torch.mean(torch.sum(-answerable_prob * torch.log(answerable_prob), dim=-1)))
+                return total_loss, torch.softmax(self.classification(sequence_output[:, 0]), dim=-1)
+            return total_loss, None
+
+        elif len(batch) == 3 and not classifier:
+            if not return_prob:
+                return start_logits, end_logits, None
+            else:
+                return self.softmax(start_logits), self.softmax(end_logits), None
+        elif len(batch) == 3 and classifier:
+            if return_prob:
+                return self.softmax(start_logits), self.softmax(
+                    end_logits), self.softmax(self.classification(sequence_output[:, 0]))
+            else:
+                return start_logits, end_logits, self.classification(sequence_output[:, 0])
+        else:
+            raise NotImplementedError()
+
+
+class BertForQuestionAnsweringSequence(BertForQuestionAnswering):
+    def __init__(self, model_type: str):
+        super(BertForQuestionAnsweringSequence, self).__init__(model_type=model_type)
+        self.classification = nn.Linear(self.bert.config.hidden_size, 2)  # [N, L, H] => [N, L, 2]
+
+
+
+
+
+class DebertaSQuAD2(nn.Module):
+    def __init__(self, model_type: str):
+        super(DebertaSQuAD2, self).__init__()
+        if model_type == 'deepset/deberta-v3-base-squad2':
+            self.bert = AutoModelForQuestionAnswering.from_pretrained(model_type)
+        else:
+            raise ValueError('Model type!')
+
+    def forward(self, batch, return_prob=False, **kwargs):
+        '''
+        each batch is a list of 5 items (training) or 3 items (inference)
+            - input_ids: token id of the input sequence
+            - attention_mask: mask of the sequence (1 for present, 0 for blank)
+            - token_type_ids: indicator of type of sequence.
+            -      e.g. in QA, whether it is question or document
+            - (training) start_positions: list of start positions of the span
+            - (training) end_positions: list of end positions of the span
+        '''
+
+        input_ids, attention_masks, token_type_ids = batch[:3]
+        # pooler_output, last_hidden_state
+        output = self.bert(input_ids=input_ids,
+                           token_type_ids=token_type_ids,
+                           attention_mask=attention_masks)
+
+        start_logits, end_logits = output.start_logits, output.end_logits
+
+        if len(batch) == 3:
+            if not return_prob:
+                return start_logits, end_logits
+            else:
+                return torch.softmax(start_logits, dim=-1), torch.softmax(end_logits, dim=-1)
+
+        else:
+            raise NotImplementedError()
diff --git a/random_indices_squad2.txt b/random_indices_squad2.txt
new file mode 100644
index 0000000..4fb0557
--- /dev/null
+++ b/random_indices_squad2.txt
@@ -0,0 +1,130091 @@
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diff --git a/random_indices_tydi.txt b/random_indices_tydi.txt
new file mode 100644
index 0000000..2cb14d4
--- /dev/null
+++ b/random_indices_tydi.txt
@@ -0,0 +1,8767 @@
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diff --git a/rehearsal.py b/rehearsal.py
new file mode 100644
index 0000000..3c35f6e
--- /dev/null
+++ b/rehearsal.py
@@ -0,0 +1,1059 @@
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import collections
+import json
+import logging
+import os
+import random
+import time
+from io import open
+import datetime
+from tqdm import trange
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from transformers import BertTokenizer, DebertaTokenizerFast, DebertaV2TokenizerFast, AutoTokenizer
+from transformers import AdamW
+from model import BertForQuestionAnsweringSequence, BertForQuestionAnswering, DebertaSQuAD2
+from transformers import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup
+from datasets import load_dataset
+
+import wandb
+from prettytable import PrettyTable
+
+from src.eval import evaluate
+from src.data import get_feedback_data, get_nq_data, get_tydi_data, read_feedback_examples_and_features, read_squad_examples_and_features, read_tydi_examples_and_features, get_mrqa_data, read_mrqa_examples_and_features
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
+                    datefmt='%m/%d/%Y %H:%M:%S',
+                    level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+PRED_FILE = "predictions-train.json"
+EVAL_FILE = "eval_results.txt"
+TEST_FILE = "test_results.txt"
+CSV_FILE = "results_sheet_test_all.tsv"
+PLOT_CSV_FILE = "plot_per_round.tsv"
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+def turn_off_dropout(m):
+    for mod in m.modules():
+        if isinstance(mod, torch.nn.Dropout):
+            mod.p = 0
+
+
+def load_initialization(model, args):
+    ckpt = torch.load(args.initialize_model_from_checkpoint + '/' + args.checkpoint_name)
+    assert args.model == ckpt['args']['model'], args.model + ' vs ' + ckpt['args']['model']
+    model.load_state_dict(ckpt['model_state_dict'])
+    logger.info("***** Model Initialization *****")
+    logger.info("Loaded the model state from a saved checkpoint {}".format(
+        args.initialize_model_from_checkpoint))
+
+
+def get_log_probs(start_probs, end_probs, start_positions, end_positions, args,
+                      device):
+
+    start_samples, end_samples = start_positions, end_positions
+    ignored_index = start_probs.size(1)
+    start_samples.clamp_(0, ignored_index)
+    end_samples.clamp_(0, ignored_index)
+
+    bs = start_samples.shape[0]
+    log_probs = start_probs[torch.arange(bs), start_samples].log() + end_probs[torch.arange(bs),
+                                                                              end_samples].log()
+    return log_probs
+
+
+def collect_rewards_offline(model, train_batches, args, device, tokenizer, n_gpu, is_initial=False):
+    total_pos = 0
+    total_neg = 0
+
+    for i in trange(len(train_batches)):
+        batch = train_batches[i]
+        batch = tuple(t.to(device) for t in batch)
+
+        if args.load_log_prob:
+            input_ids, input_mask, segment_ids, start_samples, end_samples, class_samples, log_probs, class_log_probs, rewards, class_rewards = batch
+        else:
+            input_ids, input_mask, segment_ids, start_samples, end_samples, class_samples, rewards, class_rewards = batch
+            if is_initial:
+                log_probs = torch.zeros(rewards.size()).to(device)
+                class_log_probs = torch.zeros(rewards.size()).to(device)
+            else:
+                with torch.no_grad():
+                    start_probs, end_probs, class_probs = model(batch=batch[:3], return_prob=True)
+                    log_probs = get_log_probs(start_probs, end_probs, start_samples,
+                                                      end_samples, args, device)
+                    if args.add_classifier:
+                        class_log_probs = class_probs[torch.arange(bs), class_samples].log()
+                    else:
+                        class_log_probs = None
+            train_batches[i] = [
+                input_ids, input_mask, segment_ids, start_samples, end_samples, class_samples, log_probs, class_log_probs, rewards, class_rewards
+            ]
+
+        count_pos = torch.sum(rewards > 0).item()
+        total_pos += count_pos
+        total_neg += input_ids.shape[0] - count_pos
+    return train_batches, total_pos, total_neg
+
+
+def prepare_data(args, filename, tokenizer, data_type, batch_size, data_split='train'):
+    if data_type == 'feedback':
+        logger.info('loading feedback data (%s)...' % (data_split))
+        dataset = get_feedback_data(filename)
+        examples, features = read_feedback_examples_and_features(dataset,
+                                                                 args.negative_reward,
+                                                                 args.partial_reward,
+                                                                 args.reward_wrong_unans,
+                                                                 args.reward_correct_span,
+                                                                 args.reward_correct_unans,
+                                                                 args.reward_class_wrong,
+                                                                 args.reward_class_correct_ans,
+                                                                 tokenizer,
+                                                                 args.max_seq_length,
+                                                                 args.prepend_title,
+                                                                 load_log_prob=args.load_log_prob
+                                                                 and data_split == 'train')
+    elif data_type == 'tydi':
+        logger.info('loading from tydi..., is_training = %s' % (str((data_split == 'train'))))
+        dataset = get_tydi_data(filename)
+        examples, features = read_tydi_examples_and_features(
+            input_data=dataset,
+            is_training=(data_split == 'train'),
+            version_2_with_negative=args.version_2_with_negative,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title)
+    elif data_type == 'squad':
+        logger.info('loading from squad..., is_training = %s' % (str((data_split == 'train'))))
+        input_data = load_dataset("squad")
+        is_training = (data_split == 'train')
+        if is_training:
+            input_data = input_data['train']
+        else:
+            input_data = input_data['validation']
+        examples, dataset, features = read_squad_examples_and_features(
+            is_training=(data_split == 'train'),
+            version_2_with_negative=False,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title,
+            input_data=input_data,
+        )
+    elif data_type == 'squad2':
+        logger.info('loading from squad_v2..., is_training = %s' % (str((data_split == 'train'))))
+        input_data = load_dataset("squad_v2")
+        is_training = (data_split == 'train')
+        if is_training:
+            input_data = input_data['train']
+        else:
+            input_data = input_data['validation']
+        examples, dataset, features = read_squad_examples_and_features(
+            is_training=(data_split == 'train'),
+            version_2_with_negative=True,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title,
+            input_data=input_data)
+    elif data_type == 'nq':
+        logger.info('loading from NQ..., is_training = %s' % (str((data_split == 'train'))))
+        dataset = get_nq_data(filename)
+        assert data_split != 'train'
+        examples, _, features = read_squad_examples_and_features(
+            is_training=(data_split == 'train'),
+            version_2_with_negative=False,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title,
+            get_dataset=False,
+            input_data=dataset)
+    elif data_type == 'tydi+squad':
+        assert (data_split == 'train')
+        dataset = get_tydi_data(filename)
+        examples, features = read_tydi_examples_and_features(
+            input_data=dataset,
+            is_training=True,
+            version_2_with_negative=args.version_2_with_negative,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title)
+        squad_examples, _, squad_features = read_squad_examples_and_features(
+            is_training=True,
+            version_2_with_negative=False,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title)
+
+        random_indices = [int(l.strip('\n')) for l in open('random_indices_squad.txt')]
+        squad_examples = [squad_examples[i] for i in random_indices]
+        squad_features = [squad_features[i] for i in random_indices]
+
+        # add SQuAD examples
+        examples += squad_examples[:len(examples)]
+        features += squad_features[:len(features)]
+
+    elif data_type == 'newsqa' or data_type == 'searchqa' or data_type == 'triviaqa':
+        logger.info('loading from mrqa...')
+        dataset = get_mrqa_data(filename)
+
+        examples, dataset, features = read_mrqa_examples_and_features(
+            input_data=dataset,
+            is_training=(data_split == 'train'),
+            version_2_with_negative=False,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            prepend_title=args.prepend_title,
+            get_dataset=True
+            )
+
+    # shuffle the data
+    if data_split == 'train':
+        if args.train_mode == 'sorted' or args.train_mode == 'random_sorted':
+            features = sorted(features, key=lambda f: np.sum(f.input_mask))
+        else:
+            random.shuffle(features)
+
+    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
+    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
+
+    if data_split == 'train':
+        all_start_samples = torch.tensor([f.start_sample for f in features], dtype=torch.long)
+        all_end_samples = torch.tensor([f.end_sample for f in features], dtype=torch.long)
+        all_class_samples = torch.tensor([f.class_sample for f in features], dtype=torch.long)
+        all_rewards = torch.tensor([f.reward for f in features], dtype=torch.float)
+        all_class_rewards = torch.tensor([f.class_reward for f in features], dtype=torch.float)
+
+        if args.load_log_prob:
+            all_log_probs = torch.tensor([f.log_prob for f in features], dtype=torch.float)
+            all_class_log_probs = torch.tensor([f.class_log_prob for f in features], dtype=torch.float)
+            data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_samples,
+                                 all_end_samples, all_class_samples, all_log_probs, all_class_log_probs, all_rewards, all_class_rewards)
+        else:
+            data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_samples,
+                                 all_end_samples, all_class_samples, all_rewards, all_class_rewards)
+        logger.info("***** Train *****")
+        logger.info("  Num examples = %d", len(features))
+        logger.info("  Batch size = %d", batch_size)
+    else:
+        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+        data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
+        logger.info("***** %s *****" % (data_split))
+        logger.info("  Num orig examples = %d", len(examples))
+        logger.info("  Num split examples = %d", len(features))
+        logger.info("  Batch size = %d", batch_size)
+
+    dataloader = DataLoader(data, batch_size=batch_size)
+    batches = [batch for batch in dataloader]
+    return dataset, examples, features, dataloader, batches
+
+
+def fetch_batch_data(step, train_batches):
+    if step % len(train_batches) == 0:
+        logger.info("shuffling previous data...")
+        random.shuffle(train_batches)
+    return train_batches[step % len(train_batches)]
+
+
+def main(args):
+    # create timestamp: folder name, wandb logging
+    args.timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')
+    # GPU
+    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+    n_gpu = torch.cuda.device_count()
+    logger.info("device: {}, n_gpu: {}".format(device, n_gpu))
+    args.n_gpu = n_gpu
+    # random seeds
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+            args.gradient_accumulation_steps))
+    args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
+
+    # argparse checkers
+    if not args.do_train and not args.do_eval:
+        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+    if args.do_train:
+        assert (args.train_file is not None) and (args.dev_file is not None)
+    if args.eval_test:
+        assert args.test_file is not None
+    else:
+        assert args.dev_file is not None
+    # only evaluate on the test set: need an initialization
+    # if args.eval_test and not args.do_train:
+    #     assert args.initialize_model_from_checkpoint is not None
+
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    if args.do_train:
+        # set up the logging for this experiment: create a folder named by hyperparameters
+        # also, during eval, the folder need not be created again
+        # we use the same folder for storing eval results
+        # set up the logging for this experiment: create a folder named by the timestamp
+        model_name = args.model.split('/')[-1]
+        args.output_dir += '/' + f'round{args.round_index}/rehearsal_round{args.round_index}_{model_name}_{args.scheduler}_{args.learning_rate}_b{args.train_batch_size}_acc{args.gradient_accumulation_steps}_ep{args.num_train_epochs}_nr{args.negative_reward}_pr{args.partial_reward}_rcu{args.reward_correct_unans}/{args.timestamp}'
+        os.makedirs(args.output_dir)
+        # args.output_dir += '/test'
+
+    if args.do_train:
+        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "train.log"), 'w'))
+    else:
+        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "eval.log"), 'w'))
+    logger.info(args)
+
+    if args.model == "deepset/deberta-v3-base-squad2":
+        tokenizer = AutoTokenizer.from_pretrained(args.model, return_offsets_mapping=True)
+    elif args.model == 'microsoft/deberta-v3-base':
+        tokenizer = DebertaV2TokenizerFast.from_pretrained(args.model, return_offsets_mapping=True)
+    else:
+        raise ValueError('Model type!')
+
+    
+    if args.do_train:
+        # multiple dataloaders (current round, all previous rounds, initial data)
+        all_train_batches = []
+        # for training
+
+        ########## Data Preparation Begins ########
+        file_list = [l.strip('\n') for l in open(args.train_file)]
+        print('file len = %d' % len(file_list))
+        # handle batch size in different batch
+        if args.rehearsal:
+            count = len(file_list)
+            train_batch_sizes = [int(args.train_batch_size / count)] * count
+        else:
+            train_batch_sizes = [args.train_batch_size]
+
+        print(args.train_batch_size, train_batch_sizes)
+        assert sum(train_batch_sizes) == args.train_batch_size
+
+        initial_train_dataloader = None
+        for i, file_ in enumerate(file_list):
+            _, _, train_features, train_dataloader, train_batches = prepare_data(args=args,
+                                                                    filename=file_,
+                                                                    tokenizer=tokenizer,
+                                                                    data_type='feedback',
+                                                                    batch_size=train_batch_sizes[i],
+                                                                    data_split='train')
+            if i == 0:
+                logger.info('Reading Current Data From %s' % file_)
+                initial_train_dataloader = train_dataloader
+            else:
+                logger.info('Reading Previous Data From %s' % file_)
+            all_train_batches.append(train_batches)
+
+        num_train_optimization_steps = (len(initial_train_dataloader) //
+                                        args.gradient_accumulation_steps) * args.num_train_epochs
+        logger.info("  Num steps = %d | len current round: %d" %
+                    (num_train_optimization_steps, len(all_train_batches[0])))
+
+        # validation dataset
+        eval_dataset, eval_examples, eval_features, eval_dataloader, _ = prepare_data(
+            args=args,
+            filename=args.dev_file,
+            tokenizer=tokenizer,
+            data_type=args.valid_data_type,
+            batch_size=args.eval_batch_size,
+            data_split='valid')
+
+        ########## Data Preparation Ends ########
+        assert len(all_train_batches) == len(train_batch_sizes)
+        eval_step = max(1, len(all_train_batches[0]) // args.eval_per_epoch)
+        logger.info('Time_Stamp %s ' % args.timestamp + 'eval step: %d' % eval_step)
+
+        # NOTE only tested for one learning rate
+        assert args.learning_rate
+        lr = args.learning_rate
+
+        if args.model == "deepset/deberta-v3-base-squad2":
+            model = DebertaSQuAD2(model_type=args.model)
+            print('loading deepset model')
+        else:
+            if args.add_classifier:
+                model = BertForQuestionAnsweringSequence(model_type=args.model)
+            else:
+                model = BertForQuestionAnswering(model_type=args.model)
+
+        # initial from pretraining  # initialize model; no matter training or test
+        if args.initialize_model_from_checkpoint:
+            load_initialization(model, args)
+
+        if args.turn_off_dropout:
+            turn_off_dropout(model)
+
+        model.to(device)
+
+        if n_gpu > 1:
+            model = torch.nn.DataParallel(model)
+
+        # for setting up loss visualization
+        if args.wandb:
+            wandb.init(
+                project="",
+                entity='',
+                name=
+                f'round{args.round_index}_{args.model}_{args.scheduler}={lr}_b{args.train_batch_size}_ep{args.num_train_epochs}_nr{args.negative_reward}_pr{args.partial_reward}/{args.timestamp}',
+                notes=args.notes,
+                config=vars(args),
+                tags=[
+                    args.tag, 'main experiment - classifier', '200-ex',
+                    'round %d' % args.round_index,
+                    'w/ rehearsal' if args.rehearsal else 'w/o rehearsal',
+                    ':'.join([str(l) for l in train_batch_sizes]), 'max_answer_length=30',
+                    'squad2.0 - 512 initial',
+                    'correct_unans=%f' % args.reward_correct_unans, 'hyperparameter', args.timestamp, '30% unans', 'class_coeff=%2.2f'%(args.class_coeff),
+                    'entropy_coeff=%2.2f'%(args.entropy_coeff), 'main_task', 'reward_class_wrong=%2.2f'%(args.reward_class_wrong)
+                ])
+            wandb.watch(model)
+
+        file_list = [l.strip('\n') for l in open(args.train_file)]
+        for f_ in file_list:
+            logger.info("reading from file: %s" % f_)
+
+        param_optimizer = list(model.named_parameters())
+        param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
+        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+        optimizer_grouped_parameters = [{
+            'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+            'weight_decay':
+            0.01
+        }, {
+            'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
+            'weight_decay':
+            0.0
+        }]
+
+        optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
+        lr_scheduler = get_scheduler(args.scheduler,
+                                     optimizer=optimizer,
+                                     num_warmup_steps=int(num_train_optimization_steps *
+                                                          args.warmup_proportion),
+                                     num_training_steps=num_train_optimization_steps)
+
+        # for offline training
+        if args.setup == 'offline':
+            for i in range(len(all_train_batches)):  # collect reward for each dataloader
+                all_train_batches[i], total_pos, total_neg = collect_rewards_offline(
+                    model, all_train_batches[i], args, device, tokenizer, n_gpu)
+                logger.info("Offline regret computation: {} positives {} negatives".format(
+                    total_pos, total_neg))
+
+        # start training
+        best_result = None
+        tr_loss = 0
+        nb_tr_steps = 0
+        num_train_batches = 0
+        global_step = 0
+        max_valid_reward = 0
+        max_valid_f1 = 0
+        max_valid_em = 0
+        start_time = time.time()
+        simulation_log = None
+        one_epoch_f1 = None
+
+        for epoch in range(int(args.num_train_epochs)):
+            rewards_per_epoch = []
+            class_rewards_per_epoch = []
+            acc_per_epoch = 0
+            model.train()
+            logger.info("Start epoch #{} (lr = {})...".format(epoch, lr))
+            if args.train_mode == 'random' or args.train_mode == 'random_sorted':
+                logger.info("shuffling the main training batches")
+                random.shuffle(all_train_batches[0])
+
+            for step, initial_batch in enumerate(all_train_batches[0]):
+                # prepare batch for rehearsal
+                # use the main (current round) training set to keep track of num_training_epochs
+                sizes = [initial_batch[0].size(0)]
+                all_batches = [[t] for t in initial_batch]
+                for loader_index in range(1, len(all_train_batches)):
+                    # fetch data from dataloader
+                    partial_batch = fetch_batch_data(num_train_batches,
+                                                     all_train_batches[loader_index])
+                    assert (len(initial_batch) == len(partial_batch)) and (len(all_batches)
+                                                                           == len(partial_batch))
+                    sizes.append(partial_batch[0].size(0))
+                    # add data from loader to the current batch
+                    for j in range(len(partial_batch)):
+                        all_batches[j].append(partial_batch[j])
+
+
+                batch = [torch.cat(all_batches[j], dim=0) for j in range(len(all_batches))]
+                batch = tuple(t.to(device) for t in batch)
+                num_train_batches += 1
+
+                
+                ########## do Bandit Learning #########
+                start_probs, end_probs, class_probs = model(batch=batch[:3], return_prob=True, classifier=args.add_classifier)
+
+                bs = start_probs.shape[0]
+                if args.setup == 'online':  # should not use online
+                    input_ids, _, _, start_samples, end_samples, rewards = batch
+                    start_samples, end_samples, log_prob, rewards = get_batch_rewards(
+                        start_probs, end_probs, start_samples, end_samples, rewards, args, device)
+                    count_pos = torch.sum(rewards > 0).item()
+                    total_pos += count_pos
+                    total_neg += bs - count_pos
+                else:
+                    input_ids, _, _, start_samples, end_samples, class_samples, old_log_probs, old_class_log_probs, old_rewards, old_class_rewards = batch
+
+                    ignored_index = start_probs.size(1)
+                    start_samples.clamp_(0, ignored_index)
+                    end_samples.clamp_(0, ignored_index)
+
+                    log_probs = start_probs[torch.arange(bs),
+                                           start_samples].log() + end_probs[torch.arange(bs),
+                                                                            end_samples].log()
+                    ratios = torch.exp(log_probs - old_log_probs)
+                    rewards = torch.clamp(ratios, 0, 1) * old_rewards
+                    rewards = rewards.detach()
+
+                    if args.add_classifier:
+                        class_log_probs = class_probs[torch.arange(bs), class_samples].log()
+                        class_ratios = torch.exp(class_log_probs - old_class_log_probs)
+                        class_rewards = torch.clamp(class_ratios, 0, 1) * old_class_rewards
+                        class_rewards = class_rewards.detach()
+
+                        class_pred = class_probs.argmax(dim=-1)
+                        acc = ((class_samples == class_pred) == (old_class_rewards > 0)).long().sum()
+                        # print('acc', acc)
+                        acc_per_epoch += acc
+                    
+            
+                rewards_per_epoch.append(rewards.mean().item())
+                if args.add_classifier:
+                    class_rewards_per_epoch.append(class_rewards.mean().item())
+
+                ########## Update Model ###########
+                detached_advantages = rewards
+                loss = (-log_probs * detached_advantages) / 2
+
+                if args.add_classifier:
+                    class_detached_advantages = class_rewards
+                    class_loss = (-class_log_probs * class_detached_advantages)
+                    classifier_entropy = torch.mean(torch.sum(-class_probs * class_probs.log(), dim=-1))
+                    loss = loss + args.class_coeff * class_loss - args.entropy_coeff * classifier_entropy
+                else:
+                    class_loss = torch.zeros((1,))
+                    classifier_entropy = torch.zeros((1,))
+
+                loss = loss.mean()
+
+
+
+                if n_gpu > 1:
+                    loss = loss.mean()
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+
+                tr_loss += loss.item()
+                nb_tr_steps += 1
+
+                loss.backward()
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad()
+                    global_step += 1
+
+                if args.wandb and (global_step + 1) % 5 == 0:
+                    wandb.log(
+                        {
+                            '(Train) batch policy loss': loss.item(),
+                            '(Train) Span loss': (-log_probs * detached_advantages / 2).mean().item(),
+                            '(Train) classification loss': class_loss.mean().item(),
+                            '(Train) batch advantage': detached_advantages.mean().item(),
+                            'IPS ratios':
+                            torch.clamp(ratios, 0, 1).mean().item(),
+                            '(Train) cls entropy': classifier_entropy.item(),
+
+                        },
+                        step=global_step)
+                    if args.add_classifier:
+                        wandb.log(
+                            {
+                            '(Train) batch classification advantage': class_detached_advantages.mean().item(),
+                            '(Train) batch class advantage':
+                            ((class_detached_advantages).sum() / class_detached_advantages.size(0)).item(),
+                            '(Train) class_log_probs':
+                            class_log_probs.mean().item(),
+                            'IPS ratios of classification':
+                            torch.clamp(class_ratios, 0, 1).mean().item(),
+                            }, step=global_step)
+                    if simulation_log is not None:
+                        wandb.log(simulation_log, step=global_step)
+
+
+                if step != 0 and (step) % eval_step == 0:
+                    ## record training related info
+                    logger.info(
+                        'Epoch: {}, Step: {} / {}, used_time = {:.2f}s, loss = {:.6f}'.format(
+                            epoch, step + 1, len(all_train_batches[0]),
+                            time.time() - start_time, tr_loss / (nb_tr_steps+1)))
+
+                    tr_loss = 0
+                    nb_tr_steps = 0
+
+                    save_model = False
+
+                    ######## validation ########
+                    if args.do_eval:
+                        result, has_ans_eval, no_ans_eval, _ = \
+                            evaluate(args, model, device, eval_dataset,
+                                     eval_dataloader, eval_examples, eval_features, 
+                                     args.na_prob_thresh, tokenizer, args.valid_data_type, 
+                                     calculate_score=True, classifier=args.add_classifier)
+                        model.train()
+                        result['global_step'] = global_step
+                        result['epoch'] = epoch
+                        result['learning_rate'] = lr
+                        result['batch_size'] = args.train_batch_size
+
+                        if global_step > 1 and ((best_result is None) or (result[args.eval_metric] >
+                                                     best_result[args.eval_metric])):
+                            best_result = result
+                            save_model = True
+                            logger.info(
+                                "!!! Best dev %s (lr=%s, epoch=%d): %.2f" %
+                                (args.eval_metric, str(lr), epoch, result[args.eval_metric]))
+
+                        # record max f1, em, reward
+                        max_valid_em = max(max_valid_em, result['exact'])
+                        max_valid_f1 = max(max_valid_f1, result['f1'])
+                        if args.valid_data_type == 'feedback':
+                            max_valid_reward = max(max_valid_reward, result['reward'])
+
+                        if args.wandb:
+                            ## record F1, EM for both feedback and TyDi/SQuAD data
+                            logger.info('log valid...')
+                            wandb.log(
+                                {
+                                    '(Valid) F1':
+                                    result['f1'],
+                                    '(Valid) Exact':
+                                    result['exact'],
+                                    '(Valid) Has Ans F1':
+                                    has_ans_eval['f1'],
+                                    '(Valid) Has Ans Exact':
+                                    has_ans_eval['exact'],
+                                    '(Valid) No Ans F1': 
+                                    no_ans_eval['f1'],
+                                    '(Valid) No Ans Exact': 
+                                    no_ans_eval['exact'],
+                                    '(Valid) Max F1':
+                                    max_valid_f1,
+                                    '(Valid) Max Exact':
+                                    max_valid_em,
+                                    '(Valid) Max Reward':
+                                    max_valid_reward,
+                                    '(Valid) Reward':
+                                    result['reward'],
+                                    '(Valid) perc. UNANS':
+                                    result['perc. UNANS'],
+                                    'perc. UNANS in ANS subset':
+                                    result['perc. UNANS in ANS subset'],
+                                    'perc. UNANS in UNANS subset':
+                                    result['perc. UNANS in UNANS subset'],
+                                    'F1 in predicted ANS subset':
+                                    result['F1 in predicted ANS subset'],
+                                    'F1 in predicted UNANS subset':
+                                    result['F1 in predicted UNANS subset'],
+                                    'classification_acc':
+                                    result['classification_acc'],
+                                },
+                                step=global_step)
+                            if args.valid_data_type == 'feedback':  # validation on feedback
+                                wandb.log({
+                                    '(Valid) Reward': result['reward'],
+                                }, step=global_step)
+                    else:
+                        save_model = True
+                    ######## validation ########
+
+                    if args.save_every:
+                        save_model = True
+
+                    if global_step == 0:
+                        save_model = False
+
+                    #### model saving ####
+                    if save_model and (not args.not_save):
+                        logger.info('=====Saving!!!!=====')
+                        # save the config; handle multi-gpu
+                        if n_gpu > 1:
+                            model.module.bert.config.to_json_file(
+                                os.path.join(args.output_dir, 'config.json'))
+                        else:
+                            model.bert.config.to_json_file(
+                                os.path.join(args.output_dir, 'config.json'))
+                        # save the model
+                        ckpt_name = 'saved_checkpoint_%d' % epoch if args.save_every else 'saved_checkpoint'
+                        torch.save(
+                            {
+                                'global_step':
+                                global_step,
+                                'args':
+                                vars(args),
+                                'model_state_dict':
+                                model.module.state_dict()
+                                if n_gpu > 1 else model.state_dict(),  # handle multi-gpu
+                                'optimizer_state_dict':
+                                optimizer.state_dict(),
+                            },
+                            os.path.join(args.output_dir, ckpt_name))
+
+                        if best_result:
+                            # i.e. best_result is not None
+                            filename = EVAL_FILE
+                            with open(os.path.join(args.output_dir, filename), "w") as writer:
+                                for key in sorted(best_result.keys()):
+                                    writer.write("%s = %s\n" % (key, str(best_result[key])))
+                                if epoch == 0 and args.eval_metric == 'f1':
+                                    one_epoch_f1 = best_result['f1']
+                                writer.write("%s = %s\n" % ('one_epoch_f1', one_epoch_f1))
+
+                    #### model saving ####
+
+            ## training reward
+            logger.info('(Train) Weighted Reward Per Epoch = %f' %
+                        (sum(rewards_per_epoch) / len(rewards_per_epoch)))
+            if args.add_classifier:
+                logger.info('(Train) Weighted Class Reward Per Epoch = %f' %
+                            (sum(class_rewards_per_epoch) / len(class_rewards_per_epoch)))
+            if args.wandb:
+                wandb.log(
+                    {
+                        '(Train) Weighted Reward Per Epoch':
+                        sum(rewards_per_epoch) / len(rewards_per_epoch),
+                    },
+                    step=global_step)
+            print('ACC PER EPOCH:', acc_per_epoch)
+
+
+    # that's for testing
+    if args.do_eval:
+        if args.eval_test:
+            table = PrettyTable()
+            plot_writer = open(os.path.join(args.output_dir, PLOT_CSV_FILE), "w")
+
+
+            csv_writer = open(os.path.join(args.output_dir, CSV_FILE), "w")
+            csv_writer.write('\t')
+            for _ in range(2):
+                csv_writer.write(
+                    "F1 \t has ans F1 \t no ans F1 \t  EM \t reward \t % unans \t F1 in predicted ANS subset \t"
+                )
+            for _ in range(2):
+                csv_writer.write(
+                    "F1 \t has ans F1 \t no ans F1 \t  EM \t % unans \t F1 in predicted ANS subset \t"
+                )
+            csv_writer.write("F1 \t EM \n")
+            csv_writer.write('round %d\t' % args.round_index)
+            # look at data/test_files.txt
+            # should be [data_type]\t[data_path]
+            test_data_list = [tuple(l.strip('\n').split('\t')) for l in open(args.test_file)]
+            for test_data_type, test_data_file in test_data_list:
+                eval_dataset, eval_examples, eval_features, eval_dataloader, _ = prepare_data(
+                    args,
+                    test_data_file,
+                    tokenizer,
+                    data_type=test_data_type,
+                    batch_size=args.eval_batch_size,
+                    data_split='test')
+
+                # NOTE old: model = BertForQuestionAnsweringSequence.from_pretrained(args.output_dir)
+                # model = BertForQuestionAnsweringSequence(model_type=args.model)
+                # NOTE change: only evaluate on the test set
+                if not args.do_train:
+                    if args.model == "deepset/deberta-v3-base-squad2":
+                        model = DebertaSQuAD2(model_type=args.model)
+                        print('loading deepset model')
+                        if args.initialize_model_from_checkpoint:
+                            load_initialization(model=model, args=args)
+                    else:
+                        if args.add_classifier:
+                            model = BertForQuestionAnsweringSequence(model_type=args.model)
+                        else:
+                            model = BertForQuestionAnswering(model_type=args.model)
+                        load_initialization(model=model, args=args)
+                
+
+
+                model.to(device)
+
+                logger.info('output_dir: %s' % args.output_dir)
+                na_prob_thresh = args.na_prob_thresh
+                if args.version_2_with_negative:
+                    eval_result_file = os.path.join(args.output_dir, "eval_results.txt")
+                    if os.path.isfile(eval_result_file):
+                        with open(eval_result_file) as f:
+                            for line in f.readlines():
+                                if line.startswith('best_f1_thresh'):
+                                    na_prob_thresh = float(line.strip().split()[-1])
+                                    logger.info("na_prob_thresh = %.6f" % na_prob_thresh)
+
+                result, _, _, preds = \
+                    evaluate(args, model, device, eval_dataset,
+                             eval_dataloader, eval_examples, eval_features,
+                             na_prob_thresh=na_prob_thresh,
+                             tokenizer=tokenizer,
+                             dataset_name=test_data_type,
+                             calculate_score=not args.not_calculate_score,
+                             classifier=args.add_classifier
+                             )
+                with open(
+                        os.path.join(
+                            args.output_dir,
+                            PRED_FILE.split('.')[0] + '-%s.' % test_data_type +
+                            PRED_FILE.split('.')[1]), "w") as writer:
+                    writer.write(json.dumps(preds, indent=4) + "\n")
+
+                if not args.not_calculate_score:
+                    with open(os.path.join(args.output_dir, TEST_FILE), "w") as writer:
+                        for key in sorted(result.keys()):
+                            writer.write("%s = %s\n" % (key, str(result[key])))
+
+                    table.add_column("[%s] %s" % (test_data_type, "F1"), ['%2.2f' % result['f1']])
+                    table.add_column("[%s] %s" % (test_data_type, "EM"),
+                                     ['%2.2f' % result['exact']])
+
+                    print(test_data_type)
+                    print(result)
+                    if 'NoAns_f1' in result:
+                        csv_writer.write("%2.2f\t%2.2f\t%2.2f\t%2.2f\t" %
+                                         (result['f1'], result['HasAns_f1'], result['NoAns_f1'],
+                                          result['exact']))
+                    else:
+                        assert  test_data_type == 'squad', 'only squad should be without NoAns_f1!'
+                        csv_writer.write("%2.2f\t%2.2f\t" % (result['f1'], result['exact']))
+
+                    if 'reward' in result:
+                        csv_writer.write("%2.2f\t" % (result['reward']))
+                        table.add_column("[%s] %s" % (test_data_type, "Reward"),
+                                         ['%2.2f' % result['reward']])
+                    
+                    if 'classification_acc' in result:
+                        csv_writer.write("%2.2f\t" % (result['classification_acc']))
+                    if 'perc. UNANS' in result:
+                        csv_writer.write("%2.2f\t" % (100*result['perc. UNANS']))
+
+                    if 'F1 in predicted ANS subset' in result:
+                        csv_writer.write("%2.2f\t" % (100*result['F1 in predicted ANS subset']))
+
+                    if test_data_type == 'feedback' or test_data_type == 'tydi' or test_data_type == 'squad2':
+                        plot_writer.write(test_data_file + '\t')
+                        plot_writer.write("%2.2f\t%2.2f\t%2.2f\t"%(result['f1'], result['HasAns_f1'], result['NoAns_f1']))
+                        plot_writer.write("%2.2f\t%2.2f\t%2.2f\t"%(100*result['F1 in predicted ANS subset'], 100*result['F1 in predicted UNANS subset'], result['classification_acc']))
+                        plot_writer.write("%2.2f\t%2.2f\t%2.2f\t"%(100*result['perc. UNANS'], 100*result['perc. UNANS in ANS subset'], 100*result['perc. UNANS in UNANS subset']))
+                        plot_writer.write('\n')
+                    # Round   F1  Ans F1  Unans F1    Predicted Ans F1    Predicted Unans F1  CLS Acc %unans  %unans|an   %unans|un
+            print(table)
+            csv_writer.write('\n')
+            csv_writer.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default=None, type=str, required=True)
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model checkpoints and predictions will be written.")
+    parser.add_argument("--train_file",
+                        default='data/train-460.jsonl.gz',
+                        type=str,
+                        help="SQuAD json for training. E.g., train-v1.1.json")
+    parser.add_argument("--initial_train_file",
+                        default='data/tydiqa-v1.0-train-90%.jsonl.gz',
+                        type=str,
+                        help="Initial TyDi File for training")
+    parser.add_argument("--dev_file",
+                        default='data/tydiqa-v1.0-train-10%.jsonl.gz',
+                        type=str,
+                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
+    parser.add_argument("--test_file", default=None, type=str)
+    parser.add_argument("--eval_per_epoch",
+                        default=10,
+                        type=int,
+                        help="How many times it evaluates on dev set per epoch")
+    parser.add_argument(
+        "--max_seq_length",
+        default=512,
+        type=int,
+        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
+        "longer than this will be truncated, and sequences shorter than this will be padded.")
+    parser.add_argument("--doc_stride",
+                        default=128,
+                        type=int,
+                        help="When splitting up a long document into chunks, "
+                        "how much stride to take between chunks.")
+    parser.add_argument(
+        "--max_query_length",
+        default=64,
+        type=int,
+        help="The maximum number of tokens for the question. Questions longer than this will "
+        "be truncated to this length.")
+    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
+    parser.add_argument("--do_eval",
+                        action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--eval_test",
+                        action='store_true',
+                        help='Wehther to run eval on the test set.')
+    parser.add_argument("--train_batch_size",
+                        default=32,
+                        type=int,
+                        help="Total batch size for training.")
+    parser.add_argument("--eval_batch_size",
+                        default=8,
+                        type=int,
+                        help="Total batch size for predictions.")
+    parser.add_argument("--learning_rate",
+                        default=None,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--eval_metric", default='f1', type=str)
+    parser.add_argument("--train_mode",
+                        type=str,
+                        default='random_sorted',
+                        choices=['random', 'sorted', 'random_sorted'])
+    parser.add_argument(
+        "--warmup_proportion",
+        default=0.1,
+        type=float,
+        help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
+        "of training.")
+    parser.add_argument(
+        "--n_best_size",
+        default=20,
+        type=int,
+        help="The total number of n-best predictions to generate in the nbest_predictions.json "
+        "output file.")
+    parser.add_argument("--max_answer_length",
+                        default=30,
+                        type=int,
+                        help="The maximum length of an answer that can be generated. "
+                        "This is needed because the start "
+                        "and end predictions are not conditioned on one another.")
+    parser.add_argument(
+        "--verbose_logging",
+        action='store_true',
+        help="If true, all of the warnings related to data processing will be printed. "
+        "A number of warnings are expected for a normal SQuAD evaluation.")
+    parser.add_argument("--no_cuda",
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument("--turn_off_dropout", action='store_true', help="Whether turn off dropout")
+    parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
+    parser.add_argument(
+        '--gradient_accumulation_steps',
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.")
+    parser.add_argument('--scheduler', default='linear', type=str, help='Learning rate scheduler.')
+    parser.add_argument('--initialize_model_from_checkpoint',
+                        default=None,
+                        help='Relative filepath to a saved checkpoint as model initialization.')
+
+    parser.add_argument('--version_2_with_negative',
+                        action='store_true',
+                        help='If true, the SQuAD examples contain some that do not have an answer.')
+
+    #### for bandit learning ####
+    parser.add_argument('--wandb', action='store_true', help='Whether to use wandb for logging.')
+    parser.add_argument('--notes', default='', help='Notes for this experiment: wandb logging')
+    parser.add_argument(
+        '--reward_fn',
+        default='binary_reward',
+        type=str,
+        choices=['binary_reward'],
+        help='the type of reward function used during training: stick with binary in this work')
+    parser.add_argument('--negative_reward',
+                        default=-0.1,
+                        type=float,
+                        help='value for negative update')
+    parser.add_argument('--partial_reward',
+                        default=0.5,
+                        type=float,
+                        help='value for negative update')
+    parser.add_argument('--reward_wrong_unans',
+                        default=-2,
+                        type=float,
+                        help='value for negative update')
+    parser.add_argument('--reward_correct_span',
+                        default=2,
+                        type=float,
+                        help='value for negative update')
+    parser.add_argument('--reward_correct_unans',
+                        default=0.4,
+                        type=float,
+                        help='value for negative update')
+    parser.add_argument('--reward_class_wrong',
+                        default=-1,
+                        type=float,
+                        help='value for wrong classification prediction')
+    parser.add_argument('--reward_class_correct_ans',
+                        default=1,
+                        type=float,
+                        help='value for correct classification prediction ans')
+    parser.add_argument('--setup',
+                        default='offline',
+                        type=str,
+                        choices=['offline'],
+                        help='offline setup')
+
+    # below are added
+    parser.add_argument('--na_prob_thresh',
+                        type=float,
+                        default=0.0,
+                        help='0.0 means no threshholding for na probs')
+
+    parser.add_argument('--test_data_type', type=str, help='which data type to test on')
+    parser.add_argument(
+        '--prepend_title',
+        action='store_true',
+        help='whether to prepend the document title to the document text before tokenization')
+    parser.add_argument('--feedback_as_valid',
+                        action='store_true',
+                        help='whether use feedback data as validation data')
+    parser.add_argument('--valid_data_type', type=str, help='which data type to valid on')
+    parser.add_argument('--tag', type=str, default='', help='wandb tag for experiments')
+    parser.add_argument('--checkpoint_name', type=str, default='saved_checkpoint')
+    parser.add_argument('--not_save',
+                        action='store_true',
+                        help='whether not save when doing validation')
+    parser.add_argument('--save_every',
+                        action='store_true',
+                        help='whether save checkpoint every time do validation')
+    parser.add_argument('--load_log_prob',
+                        action='store_true',
+                        help='whether directly load log probability from data')
+    parser.add_argument('--rehearsal', action='store_true', help='whether use rehearsal')
+    parser.add_argument('--round_index', type=int, default=0)
+    parser.add_argument('--not_calculate_score', action='store_true')
+    parser.add_argument('--add_classifier', action='store_true', help='whether to add a separate classifier for unans/ans')
+    parser.add_argument('--entropy_coeff', type=float, default=0.0)
+    parser.add_argument('--class_coeff', type=float, default=1.0)
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..977f013
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,86 @@
+backcall==0.2.0
+bert-score==0.3.9
+boto3==1.17.104
+botocore==1.20.104
+certifi==2021.10.8
+chardet==4.0.0
+click==8.0.1
+configparser==5.0.2
+cycler==0.10.0
+datasets==1.9.0
+decorator==5.0.9
+dill==0.3.4
+docker-pycreds==0.4.0
+filelock==3.0.12
+flake8==3.9.2
+fsspec==2021.7.0
+gitdb==4.0.7
+GitPython==3.1.17
+huggingface-hub==0.0.8
+idna==2.10
+importlib-metadata==3.10.0
+ipython==7.22.0
+ipython-genutils==0.2.0
+jedi==0.17.2
+jmespath==0.10.0
+joblib==1.0.1
+kiwisolver==1.3.1
+matplotlib==3.4.2
+mccabe==0.6.1
+mkl-fft==1.3.0
+mkl-random==1.2.1
+mkl-service==2.3.0
+multiprocess==0.70.12.2
+nltk==3.6.2
+numpy==1.20.2
+packaging==20.9
+pandas==1.3.0
+parso==0.7.0
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==8.2.0
+pip==21.1.2
+portalocker==2.0.0
+promise==2.3
+prompt-toolkit==3.0.17
+protobuf==3.17.3
+psutil==5.8.0
+ptyprocess==0.7.0
+pyarrow==4.0.1
+pycodestyle==2.7.0
+pyflakes==2.3.1
+Pygments==2.9.0
+pyparsing==2.4.7
+python-dateutil==2.8.1
+pytorch-pretrained-bert==0.6.2
+pytz==2021.1
+PyYAML==5.4.1
+regex==2021.4.4
+requests==2.25.1
+s3transfer==0.4.2
+sacrebleu==1.5.1
+sacremoses==0.0.45
+scipy==1.7.1
+sentry-sdk==1.1.0
+setuptools==52.0.0.post20210125
+shortuuid==1.0.1
+six==1.15.0
+smmap==4.0.0
+subprocess32==3.5.4
+tokenizers==0.10.3
+torch==1.8.1
+torch-dct==0.1.5
+torchaudio==0.8.1
+torchvision==0.9.1
+tqdm==4.59.0
+traitlets==5.0.5
+transformers==4.6.1
+typing-extensions==3.10.0.0
+urllib3==1.26.5
+wandb==0.11.0
+wcwidth==0.2.5
+wheel==0.36.2
+xxhash==2.0.2
+yapf==0.31.0
+zipp==3.4.1
diff --git a/run_tydi.py b/run_tydi.py
new file mode 100644
index 0000000..a1c0789
--- /dev/null
+++ b/run_tydi.py
@@ -0,0 +1,787 @@
+# Copyright (c) 2019, Facebook, Inc. and its affiliates. All Rights Reserved
+"""Run BERT on Tydi.
+
+
+GPU-low
+for training
+python run_tydi.py \
+  --do_train \
+  --do_eval \
+  --model microsoft/deberta-base \
+  --train_file data/tydiqa-v1.0-train.jsonl.gz \
+  --dev_file data/tydiqa-v1.0-dev.jsonl.gz \
+  --train_batch_size 40 \
+  --gradient_accumulation_steps 8 \
+  --eval_batch_size 5  \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2 \
+  --eval_per_epoch 800 \
+  --max_seq_length 512 \
+  --doc_stride 512 \
+  --eval_metric f1 \
+  --na_prob_thresh 0 \
+  --output_dir tydi_output \
+  --version_2_with_negative
+
+
+for testing 
+python run_tydi.py \
+  --do_eval \
+  --eval_test \
+  --model microsoft/deberta-base \
+  --test_file data/tydiqa-v1.0-train.jsonl.gz \
+  --eval_batch_size 5  \
+  --max_seq_length 512 \
+  --doc_stride 512 \
+  --eval_metric f1 \
+  --na_prob_thresh 0 \
+  --initialize_model_from_checkpoint tydi_output/20220607135748982502 \
+  --output_dir tydi_output \
+  --version_2_with_negative
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import collections
+import json
+import logging
+import math
+import os
+import random
+import time
+import re
+import string
+from io import open
+import gzip
+import datetime
+
+from IPython import embed
+
+import wandb
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+import torch.nn as nn
+
+from transformers import BertTokenizer, DebertaTokenizer, DebertaTokenizerFast, DebertaV2TokenizerFast
+from transformers import AdamW
+from model import BertForQuestionAnswering
+from transformers import get_scheduler
+from datasets import load_dataset
+
+from pytorch_pretrained_bert.tokenization import BasicTokenizer  # used in evaluation
+
+from pytorch_pretrained_bert.tokenization import whitespace_tokenize
+from src.data import get_nq_data, get_tydi_data, read_squad_examples_and_features, read_tydi_examples_and_features, get_mrqa_data, read_mrqa_examples_and_features
+from src.eval import evaluate
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
+                    datefmt='%m/%d/%Y %H:%M:%S',
+                    level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+PRED_FILE = "predictions.json"
+EVAL_FILE = "eval_results.txt"
+TEST_FILE = "test_results.txt"
+
+
+
+# https://github.com/google-research-datasets/tydiqa/blob/43cde6d598c1cf88c1a8b9ed32e89263ffb5e03b/tydi_eval.py#L239
+# return a string
+def byte_slice(text, start, end):
+    byte_str = bytes(text, 'utf-8')
+    # return str(byte_str[start:end])
+    return byte_str[start:end].decode('utf-8')
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+        return re.sub(regex, ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def load_initialization(model, args):
+    ckpt = torch.load(args.initialize_model_from_checkpoint + '/saved_checkpoint')
+    assert args.model == ckpt['args']['model'], args.model + ' vs ' + ckpt['args']['model']
+    model.load_state_dict(ckpt['model_state_dict'])
+    logger.info("***** Model Initialization *****")
+    logger.info("Loaded the model state from a saved checkpoint {}".format(
+        args.initialize_model_from_checkpoint))
+
+
+def main(args):
+    print('rand seed', int(args.random_indices.split('.')[0].split('_')[-1]))
+    # create timestamp: folder name, wandb logging
+    args.timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')
+    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+    # NOTE not testable yet for n-gpu training
+    n_gpu = torch.cuda.device_count()
+    logger.info("device: {}, n_gpu: {}, 16-bits training: {}".format(device, n_gpu, args.fp16))
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+            args.gradient_accumulation_steps))
+    args.train_batch_size = \
+        args.train_batch_size // args.gradient_accumulation_steps
+
+    if not args.do_train and not args.do_eval:
+        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+
+    if args.do_train:
+        assert (args.train_file is not None) and (args.dev_file is not None)
+
+    if args.eval_test:
+        assert args.test_file is not None
+    else:
+        assert args.dev_file is not None
+
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    # set up the logging for this experiment: create a folder named by the timestamp
+    # args.output_dir += '/' + args.timestamp
+    if args.do_train:
+        # set up the logging for this experiment: create a folder named by hyperparameters 
+        # also, during eval, the folder need not be created again
+        args.output_dir += '/%s_initial_data_%d_wclassifier_%d/'%(args.data_type, args.num_initial_data, int(args.random_indices.split('.')[0].split('_')[-1])) 
+        os.makedirs(args.output_dir)
+        logger.info('output_dir: %s'%(args.output_dir))
+        # args.output_dir += '/test'
+
+    if args.do_train:
+        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "train.log"), 'w'))
+    else:
+        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "eval.log"), 'w'))
+    logger.info(args)
+
+    if 'deberta' in args.model:
+        tokenizer = DebertaV2TokenizerFast.from_pretrained(args.model, return_offsets_mapping=True)
+    elif 'bert-' in args.model:
+        tokenizer = BertTokenizer.from_pretrained(args.model, do_lower_case=args.do_lower_case)
+    else:
+        raise ValueError('Model type!')
+
+
+    if args.do_train:
+        if args.data_type == 'tydi':
+            # for training 
+            train_dataset = get_tydi_data(args.train_file)
+            eval_dataset = get_tydi_data(args.dev_file)
+
+            unfiltered_train_examples, unfiltered_train_features = read_tydi_examples_and_features(
+                input_data=train_dataset,
+                is_training=True,
+                version_2_with_negative=args.version_2_with_negative,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title
+                )
+
+            eval_examples, eval_features = read_tydi_examples_and_features(
+                input_data=eval_dataset,
+                is_training=False,
+                version_2_with_negative=args.version_2_with_negative,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title
+                )
+        elif args.data_type == 'squad2':
+            logger.info('loading from squad_v2...')
+            input_data = load_dataset("squad_v2")
+            
+            unfiltered_train_examples, unfiltered_train_dataset, unfiltered_train_features = read_squad_examples_and_features(
+                is_training=True,
+                version_2_with_negative=True,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title,
+                input_data=input_data['train']
+                )
+
+            eval_examples, eval_dataset, eval_features = read_squad_examples_and_features(
+                is_training=False,
+                version_2_with_negative=True,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title,
+                input_data=input_data['validation']
+                )
+        elif args.data_type == 'squad':
+            logger.info('loading from squad...')
+            input_data = load_dataset("squad")
+            
+            unfiltered_train_examples, _, unfiltered_train_features = read_squad_examples_and_features(
+                is_training=True,
+                version_2_with_negative=False,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title,
+                input_data=input_data['train']
+                )
+
+            eval_examples, eval_dataset, eval_features = read_squad_examples_and_features(
+                is_training=False,
+                version_2_with_negative=False,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title,
+                input_data=input_data['validation']
+                )
+        elif args.data_type == 'newsqa' or args.data_type == 'searchqa' or args.data_type == 'triviaqa':
+            logger.info('loading from newsqa...')
+            train_dataset = get_mrqa_data(args.train_file)
+            eval_dataset = get_mrqa_data(args.dev_file)
+
+            unfiltered_train_examples, _,  unfiltered_train_features = read_mrqa_examples_and_features(
+                input_data=train_dataset,
+                is_training=True,
+                version_2_with_negative=False,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title
+                )
+
+            eval_examples, _, eval_features = read_mrqa_examples_and_features(
+                input_data=eval_dataset,
+                is_training=False,
+                version_2_with_negative=False,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title
+                )
+
+
+        if args.upsample:
+            # if do upsample, then we want (answerable: unanswerable = 3:1)
+            # so we make the number of answerable examples 7x, and keep the unanswerable ones
+            train_examples = []
+            train_features = []
+            num_unanswerable = 0
+            for i, l in enumerate(unfiltered_train_examples):
+                if l.start_sample == -1:
+                    train_examples.append(l)
+                    train_features.append(unfiltered_train_features[i])
+                    num_unanswerable += 1
+                else :
+                    for _ in range(7):
+                        train_examples.append(l)
+                        train_features.append(unfiltered_train_features[i])
+            print('num_unanswerable', num_unanswerable)
+            print('train_examples', len(train_examples))
+        else:
+            train_examples = unfiltered_train_examples
+            train_features = unfiltered_train_features
+            train_dataset = unfiltered_train_dataset
+
+        if args.num_initial_data:  # vary the number of initial data used
+            random_indices = [int(l) for l in open(args.random_indices)]
+            assert len(random_indices) == len(train_features)
+            train_examples = [train_examples[i] for i in random_indices]
+            train_features = [train_features[i] for i in random_indices]
+            train_dataset = [train_dataset[i] for i in random_indices]
+            train_examples = train_examples[:args.num_initial_data]
+            train_features = train_features[:args.num_initial_data]
+            train_dataset = train_dataset[:args.num_initial_data]
+            
+            unans = 0
+            for f in train_features:
+                if f.start_sample == -1 and f.end_sample == -1:
+                    unans += 1
+            logger.info('Num UnAns in Random Subset: %d / %d'%(unans, len(train_features)))
+
+            eval_examples = train_examples
+            eval_features = train_features
+            eval_dataset = train_dataset
+
+        if args.add_squad_examples:
+            # add squad examples to the training set 
+            squad_examples, _, squad_features = read_squad_examples_and_features(
+                is_training=True,
+                version_2_with_negative=False,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                prepend_title=args.prepend_title
+                )
+            
+            random_indices = list(range(len(squad_examples)))
+            random.shuffle(random_indices)
+            squad_examples = [squad_examples[i] for i in random_indices]
+            squad_features = [squad_features[i] for i in random_indices]
+            
+            # add different number of SQuAD examples
+            squad_examples = squad_examples[:len(train_examples)]
+            squad_features = squad_features[:len(train_examples)]
+
+            train_examples += squad_examples
+            train_features += squad_features
+
+
+
+        logger.info("***** Dev *****")
+        logger.info("  Num orig examples = %d", len(eval_examples))
+        logger.info("  Num split examples = %d", len(eval_features))
+        logger.info("  Batch size = %d", args.eval_batch_size)
+        for i, f in enumerate(eval_features):
+            f.example_index = i
+        all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
+        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
+        eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size)
+
+
+
+        if args.train_mode == 'sorted' or args.train_mode == 'random_sorted':
+            train_features = sorted(train_features, key=lambda f: np.sum(f.input_mask))
+        else:
+            random.shuffle(train_features)
+        all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
+        all_start_positions = torch.tensor([f.start_sample for f in train_features],
+                                           dtype=torch.long)
+        all_end_positions = torch.tensor([f.end_sample for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                   all_start_positions, all_end_positions)
+        train_dataloader = DataLoader(train_data, batch_size=args.train_batch_size)
+        train_batches = [batch for batch in train_dataloader]
+
+        num_train_optimization_steps = \
+            len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
+
+        logger.info("***** Train *****")
+        logger.info("  Num orig examples = %d", len(train_examples))
+        logger.info("  Num split examples = %d", len(train_features))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_optimization_steps)
+        if args.num_initial_data:
+            logger.info("  Num Initial Data = %d", args.num_initial_data)
+
+        eval_step = max(1, len(train_batches) // args.eval_per_epoch)
+        best_result = None
+        # NOTE only tested for one learning rate
+        lrs = [args.learning_rate] if args.learning_rate else \
+            [1e-6, 2e-6, 3e-6, 5e-6, 1e-5, 2e-5, 3e-5, 5e-5]
+        for lr in lrs:
+            # NOTE old: model = BertForQuestionAnswering.from_pretrained(args.model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE)
+            model = BertForQuestionAnswering(model_type=args.model)
+            if args.fp16:
+                model.half()
+
+            if args.add_classifier:
+                model.classification = nn.Linear(model.bert.config.hidden_size, 2)
+            model.to(device)
+            
+
+
+            if n_gpu > 1:
+                model = torch.nn.DataParallel(model)
+            param_optimizer = list(model.named_parameters())
+            param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
+            no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+            optimizer_grouped_parameters = [{
+                'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+                'weight_decay':
+                0.01
+            }, {
+                'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
+                'weight_decay':
+                0.0
+            }]
+
+            optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
+            lr_scheduler = get_scheduler(args.scheduler,
+                                         optimizer=optimizer,
+                                         num_warmup_steps=int(num_train_optimization_steps *
+                                                              args.warmup_proportion),
+                                         num_training_steps=num_train_optimization_steps)
+
+            if args.wandb:
+                wandb.init(
+                    project='',
+                    entity='',
+                    name=f'{args.model}_{args.scheduler}={lr}_b{args.train_batch_size}_ep{args.num_train_epochs}/{args.timestamp}',
+                    notes=args.notes,
+                    config=vars(args),
+                    tags=['title_seg=2', 'initial-v3-base', args.data_type, 'squad2_initial_data_256_wclassifier'])
+                wandb.watch(model)
+# 
+            tr_loss = 0
+            nb_tr_examples = 0
+            nb_tr_steps = 0
+            global_step = 0
+            max_valid_em = 0
+            max_valid_f1 = 0
+            start_time = time.time()
+            for epoch in range(int(args.num_train_epochs)):
+                model.train()
+                logger.info("Start epoch #{} (lr = {})...".format(epoch, lr))
+                if args.train_mode == 'random' or args.train_mode == 'random_sorted':
+                    random.shuffle(train_batches)
+                for step, batch in enumerate(train_batches):
+                    if n_gpu == 1:
+                        batch = tuple(t.to(device) for t in batch)
+                    input_ids, input_mask, segment_ids, start_positions, end_positions = batch
+                    loss, classifier_prob = model(batch, classifier=args.add_classifier)
+                    if n_gpu > 1:
+                        loss = loss.mean()
+
+                    
+
+                    if args.gradient_accumulation_steps > 1:
+                        loss = loss / args.gradient_accumulation_steps
+
+                    tr_loss += loss.item()
+                    nb_tr_examples += input_ids.size(0)
+                    nb_tr_steps += 1
+
+                    if args.fp16:
+                        optimizer.backward(loss)
+                    else:
+                        loss.backward()
+                    if (step + 1) % args.gradient_accumulation_steps == 0:
+                        optimizer.step()
+                        lr_scheduler.step()
+                        optimizer.zero_grad()
+                        global_step += 1
+                        if args.wandb:
+                            wandb.log(
+                                {
+                                    'Train Loss': loss * args.gradient_accumulation_steps,
+                                }, step=global_step)
+                            if args.add_classifier:
+                                classifier_pred = classifier_prob.argmax(dim=-1)
+                                answerable_mask = (start_positions != 0) | (end_positions != 0)
+                                acc = 100 * (classifier_pred == answerable_mask).long().sum()/float(classifier_pred.size(0))
+
+                                wandb.log(
+                                    {
+                                        'Pert. Unans': 100 * (1-classifier_pred).sum() / float(classifier_pred.size(0)),
+                                        'classification_acc': acc,
+                                    }, step=global_step)
+
+                    if (step + 1) % eval_step == 0:
+                        logger.info(
+                            'Epoch: {}, Step: {} / {}, used_time = {:.2f}s, loss = {:.6f}'.format(
+                                epoch, step + 1, len(train_batches),
+                                time.time() - start_time, tr_loss / nb_tr_steps))
+
+                        save_model = False
+                        if args.do_eval:
+                            result, _, _, preds = \
+                                evaluate(args, model, device, eval_dataset,
+                                         eval_dataloader, eval_examples, eval_features, 
+                                         args.na_prob_thresh, tokenizer, args.data_type,
+                                         calculate_score=args.calculate_score, classifier=args.add_classifier)
+                            model.train()
+
+                            if args.calculate_score:
+                                result['global_step'] = global_step
+                                result['epoch'] = epoch
+                                result['learning_rate'] = lr
+                                result['batch_size'] = args.train_batch_size
+                                
+                                if (best_result is None) or (result[args.eval_metric] >
+                                                             best_result[args.eval_metric]):
+                                    best_result = result
+                                    save_model = True
+                                    logger.info(
+                                        "!!! Best dev %s (lr=%s, epoch=%d): %.2f" %
+                                        (args.eval_metric, str(lr), epoch, result[args.eval_metric]))
+
+                                max_valid_em = max(max_valid_em, result['exact'])
+                                max_valid_f1 = max(max_valid_f1, result['f1'])
+                                if args.wandb:
+                                    wandb.log(
+                                                {
+                                                    '(Valid) F1': result['f1'],
+                                                    '(Valid) Exact': result['exact'],
+                                                    '(Valid) Max F1': max_valid_f1,
+                                                    '(Valid) Max Exact': max_valid_em,
+                                                }, step=global_step
+                                            )
+                            else:
+                                save_model = True
+                        else:
+                            save_model = True
+                        if save_model:
+                            if n_gpu > 1:
+                                # save the config
+                                model.module.bert.config.to_json_file(
+                                    os.path.join(args.output_dir, 'config.json'))
+                                # save the model
+                                torch.save(
+                                    {
+                                        'global_step': global_step,
+                                        'args': vars(args),
+                                        'model_state_dict': model.module.state_dict(),
+                                        'optimizer_state_dict': optimizer.state_dict(),
+                                    }, os.path.join(args.output_dir, 'saved_checkpoint'))
+                            else:
+
+                                # save the config
+                                model.bert.config.to_json_file(
+                                    os.path.join(args.output_dir, 'config.json'))
+                                # save the model
+                                torch.save(
+                                    {
+                                        'global_step': global_step,
+                                        'args': vars(args),
+                                        'model_state_dict': model.state_dict(),
+                                        'optimizer_state_dict': optimizer.state_dict(),
+                                    }, os.path.join(args.output_dir, 'saved_checkpoint'))
+                            if best_result:
+                                # i.e. best_result is not None
+                                filename = EVAL_FILE
+                                if len(lrs) != 1:
+                                    filename = str(lr) + '_' + EVAL_FILE
+                                with open(os.path.join(args.output_dir, filename), "w") as writer:
+                                    for key in sorted(best_result.keys()):
+                                        writer.write("%s = %s\n" % (key, str(best_result[key])))
+                                    if epoch == 0:
+                                        one_epoch_f1 = best_result['f1']
+                                    writer.write("%s = %s\n" % ('one_epoch_f1', one_epoch_f1))
+
+    if args.do_eval:
+        if args.eval_test:
+            if not args.test_on_squad:
+                # test on TyDiQA data
+                eval_dataset = get_tydi_data(args.test_file)
+                eval_examples, eval_features = read_tydi_examples_and_features(
+                    input_data=eval_dataset,
+                    is_training=False,
+                    version_2_with_negative=args.version_2_with_negative,
+                    tokenizer=tokenizer,
+                    max_seq_length=args.max_seq_length,
+                    prepend_title=args.prepend_title
+                    )
+            else:
+                # test on SQuAD data
+                print('test on squad....')
+                eval_examples, eval_dataset, eval_features = read_squad_examples_and_features(
+                # eval_examples, eval_dataset,  eval_features = read_squad_examples_and_features(
+                    is_training=False,
+                    version_2_with_negative=False,
+                    tokenizer=tokenizer,
+                    max_seq_length=args.max_seq_length,
+                    prepend_title=args.prepend_title
+                    )
+
+            logger.info("***** Test *****")
+            logger.info("  Num orig examples = %d", len(eval_examples))
+            logger.info("  Num split examples = %d", len(eval_features))
+            logger.info("  Batch size = %d", args.eval_batch_size)
+            all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
+            all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
+            all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
+            all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+            eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                      all_example_index)
+            eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size)
+        # NOTE old: model = BertForQuestionAnswering.from_pretrained(args.output_dir)
+        # model = BertForQuestionAnswering(model_type=args.model)
+        # NOTE change: only evaluate on the test set
+        if not args.do_train:
+            model = BertForQuestionAnswering(model_type=args.model)
+            load_initialization(model=model, args=args)
+        if args.fp16:
+            model.half()
+        model.to(device)
+
+        na_prob_thresh = args.na_prob_thresh
+        if args.version_2_with_negative:
+            eval_result_file = os.path.join(args.output_dir, "eval_results.txt")
+            if os.path.isfile(eval_result_file):
+                with open(eval_result_file) as f:
+                    for line in f.readlines():
+                        if line.startswith('best_f1_thresh'):
+                            na_prob_thresh = float(line.strip().split()[-1])
+                            logger.info("na_prob_thresh = %.6f" % na_prob_thresh)
+
+        result, _, _, preds = \
+            evaluate(args, model, device, eval_dataset,
+                     eval_dataloader, eval_examples, eval_features,
+                     na_prob_thresh=na_prob_thresh, tokenizer=tokenizer, dataset_name=args.data_type,
+                     calculate_score=True, classifier=args.add_classifier)
+        with open(os.path.join(args.output_dir, PRED_FILE), "w") as writer:
+            writer.write(json.dumps(preds, indent=4) + "\n")
+        with open(os.path.join(args.output_dir, TEST_FILE), "w") as writer:
+            for key in sorted(result.keys()):
+                writer.write("%s = %s\n" % (key, str(result[key])))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default=None, type=str, required=True)
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model checkpoints and predictions will be written.")
+    parser.add_argument("--train_file",
+                        default=None,
+                        type=str,
+                        help="SQuAD json for training. E.g., train-v1.1.json")
+    parser.add_argument("--dev_file",
+                        default=None,
+                        type=str,
+                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
+    parser.add_argument("--test_file", default=None, type=str)
+    parser.add_argument("--eval_per_epoch",
+                        default=10,
+                        type=int,
+                        help="How many times it evaluates on dev set per epoch")
+    parser.add_argument(
+        "--max_seq_length",
+        default=384,
+        type=int,
+        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
+        "longer than this will be truncated, and sequences shorter than this will be padded.")
+    parser.add_argument("--doc_stride",
+                        default=128,
+                        type=int,
+                        help="When splitting up a long document into chunks, "
+                        "how much stride to take between chunks.")
+    parser.add_argument(
+        "--max_query_length",
+        default=64,
+        type=int,
+        help="The maximum number of tokens for the question. Questions longer than this will "
+        "be truncated to this length.")
+    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
+    parser.add_argument("--do_eval",
+                        action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--do_lower_case",
+                        action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+    parser.add_argument("--eval_test",
+                        action='store_true',
+                        help='Wehther to run eval on the test set.')
+    parser.add_argument("--train_batch_size",
+                        default=32,
+                        type=int,
+                        help="Total batch size for training.")
+    parser.add_argument("--eval_batch_size",
+                        default=8,
+                        type=int,
+                        help="Total batch size for predictions.")
+    parser.add_argument("--learning_rate",
+                        default=None,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--eval_metric", default='f1', type=str)
+    parser.add_argument("--train_mode",
+                        type=str,
+                        default='random_sorted',
+                        choices=['random', 'sorted', 'random_sorted'])
+    parser.add_argument(
+        "--warmup_proportion",
+        default=0.1,
+        type=float,
+        help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
+        "of training.")
+    parser.add_argument(
+        "--n_best_size",
+        default=20,
+        type=int,
+        help="The total number of n-best predictions to generate in the nbest_predictions.json "
+        "output file.")
+    parser.add_argument("--max_answer_length",
+                        default=512,
+                        type=int,
+                        help="The maximum length of an answer that can be generated. "
+                        "This is needed because the start "
+                        "and end predictions are not conditioned on one another.")
+    parser.add_argument(
+        "--verbose_logging",
+        action='store_true',
+        help="If true, all of the warnings related to data processing will be printed. "
+        "A number of warnings are expected for a normal SQuAD evaluation.")
+    parser.add_argument("--no_cuda",
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
+    parser.add_argument(
+        '--gradient_accumulation_steps',
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.")
+    parser.add_argument('--fp16',
+                        action='store_true',
+                        help="Whether to use 16-bit float precision instead of 32-bit")
+    parser.add_argument(
+        '--loss_scale',
+        type=float,
+        default=0,
+        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+        "0 (default value): dynamic loss scaling.\n"
+        "Positive power of 2: static loss scaling value.\n")
+    parser.add_argument('--version_2_with_negative',
+                        action='store_true',
+                        help='If true, the SQuAD examples contain some that do not have an answer.')
+
+    # below are added
+    parser.add_argument('--scheduler', default='linear', type=str, help='Learning rate scheduler.')
+    parser.add_argument('--na_prob_thresh',
+                        type=float,
+                        default=0.0,
+                        help='0.0 means no threshholding for na probs')
+    parser.add_argument('--initialize_model_from_checkpoint',
+                        default=None,
+                        help='Relative filepath to a saved checkpoint as model initialization.')
+    parser.add_argument('--add_squad_examples',
+                        action='store_true',
+                        help='If true, add squad examples to balance unanswerable (do data augmentation)')
+    parser.add_argument('--upsample',
+                        action='store_true',
+                        help='whether to upsample answerable data')
+    parser.add_argument('--dev_fewer_unans',
+                        action='store_true',
+                        help='whether to include fewer unanswerable examples for dev set')
+    parser.add_argument('--test_on_squad',
+                        action='store_true',
+                        help='whether test on SQuAD Dev data')
+    parser.add_argument('--prepend_title',
+                        action='store_true',
+                        help='whether to prepend the document title to the document text before tokenization')
+    parser.add_argument('--squad_file', default='data/squad_train.json', type=str, help='squad train file path (for add_squad_exapmles) for data augmentation, not for evaluation')
+    parser.add_argument('--wandb',
+                        action='store_true',
+                        help='If true, log with wandb.')
+    parser.add_argument('--notes', default='', type=str, help='notes added for wandb')
+    parser.add_argument('--num_initial_data', default=None, type=int, help='number of init data')
+    parser.add_argument('--data_type', default='tydi', type=str, help='type of input data')
+    parser.add_argument('--random_indices', default='', type=str, help='random indices for dataset')
+    parser.add_argument('--add_classifier', action='store_true', help='whether to add a separate classifier for unans/ans')
+    parser.add_argument('--calculate_score', action='store_true', help='whether to add a separate classifier for unans/ans')
+
+    args = parser.parse_args()
+
+    main(args)
+
diff --git a/scripts/gen_data.sh b/scripts/gen_data.sh
new file mode 100644
index 0000000..6bc3e93
--- /dev/null
+++ b/scripts/gen_data.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# Readme:
+# data_file -> path to the collected data
+# outfile -> path to the output file (will write to that file)
+# checkpoint -> model checkpoint for collecting that round
+# model -> model type
+r_index=10
+model_source="fewer"
+outfile="data/bandit_parallel/train/round${r_index}/train-data-parallel-round${r_index}-200-wprob-${model_source}.jsonl"
+c_name="2023042511370472"
+checkpoint="parallel_exp/round10/fewer-r10-20230426123730412874/saved_checkpoint"
+
+python generate_prob.py --data_file data/collected_data/train/parallel/round${r_index}/round${r_index}-${c_name}-${model_source}-feedback.jsonl.gz \
+                             --outfile ${outfile} \
+                             --model microsoft/deberta-v3-base \
+                             --checkpoint ${checkpoint} \
+                             --add_classifier
+
+gzip ${outfile}
\ No newline at end of file
diff --git a/scripts/pretrain.sh b/scripts/pretrain.sh
new file mode 100644
index 0000000..8b1929a
--- /dev/null
+++ b/scripts/pretrain.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+python run_tydi.py \
+  --do_train \
+  --do_eval \
+  --model microsoft/deberta-v3-base \
+  --train_file data/initial_data/NewsQA-train.jsonl.gz \
+  --dev_file data/initial_data/NewsQA-dev.jsonl.gz \
+  --train_batch_size 10 \
+  --gradient_accumulation_steps 1 \
+  --eval_batch_size 5  \
+  --learning_rate 3e-5 \
+  --num_train_epochs 10 \
+  --eval_per_epoch 5 \
+  --max_seq_length 512 \
+  --doc_stride 512 \
+  --eval_metric f1 \
+  --na_prob_thresh 0 \
+  --version_2_with_negative \
+  --prepend_title \
+  --data_type squad2 \
+  --output_dir [output_dir] \
+  --num_initial_data 512 \
+  --add_classifier \
+  --random_indices rand/random_indices_squad2.txt 
\ No newline at end of file
diff --git a/scripts/rehearsal.sh b/scripts/rehearsal.sh
new file mode 100644
index 0000000..d0fd6fa
--- /dev/null
+++ b/scripts/rehearsal.sh
@@ -0,0 +1,40 @@
+python rehearsal.py   --do_train  \
+                      --do_eval   \
+                      --model microsoft/deberta-v3-base   \
+                      --train_file [train_file_name]   \
+                      --initial_train_file data/tydiqa-v1.0-train-90%.jsonl.gz   \
+                      --dev_file data/Dev-400.jsonl.gz   \
+                      --train_batch_size 30   \
+                      --gradient_accumulation_steps 5   \
+                      --eval_batch_size 6    \
+                      --negative_reward -0.1   \
+                      --partial_reward 0.5   \
+                      --num_train_epochs 30   \
+                      --learning_rate 3e-6   \
+                      --eval_per_epoch 4   \
+                      --max_seq_length 512   \
+                      --doc_stride 512   \
+                      --na_prob_thresh 0   \
+                      --output_dir [output_dir]   \
+                      --version_2_with_negative   \
+                      --prepend_title   \
+                      --setup offline   \
+                      --eval_metric f1   \
+                      --valid_data_type feedback   \
+                      --tag fewer2   --seed 42   \
+                      --load_log_prob   \
+                      --wandb   \
+                      --round_index 7   \
+                      --reward_wrong_unans -1   \
+                      --reward_correct_unans 1   \
+                      --reward_correct_span 1   \
+                      --reward_class_wrong 0   \
+                      --reward_class_correct_ans 1   \
+                      --entropy_coeff 5.0   \
+                      --train_mode random   \
+                      --initialize_model_from_checkpoint [model_path]   \
+                      --checkpoint_name saved_checkpoint    \
+                      --turn_off_dropout   \
+                      --add_classifier   \
+                      --scheduler linear   \
+                      --rehearsal   
\ No newline at end of file
diff --git a/scripts/test.sh b/scripts/test.sh
new file mode 100644
index 0000000..1e942d2
--- /dev/null
+++ b/scripts/test.sh
@@ -0,0 +1,30 @@
+checkpoint_list=("checkpoint_path")
+r=1
+
+for i in "${checkpoint_list[@]}"; do   
+    for checkpoint in $i
+    do
+      echo "$checkpoint | round $r"
+      python rehearsal.py \
+          --do_eval \
+          --eval_test \
+          --model microsoft/deberta-v3-base \
+          --eval_batch_size 32  \
+          --max_seq_length 512 \
+          --doc_stride 512 \
+          --eval_metric f1 \
+          --na_prob_thresh 0 \
+          --output_dir  ${checkpoint} \
+          --test_file data/test_feedback.txt \
+          --initialize_model_from_checkpoint ${checkpoint} \
+          --version_2_with_negative \
+          --test_data_type - \
+          --checkpoint_name saved_checkpoint  \
+          --n_best_size 20 \
+          --max_answer_length 30 \
+          --prepend_title \
+          --round_index ${r} \
+          --add_classifier
+      r=$((r+1))
+    done
+done
\ No newline at end of file
diff --git a/src/data.py b/src/data.py
new file mode 100644
index 0000000..b43f7f0
--- /dev/null
+++ b/src/data.py
@@ -0,0 +1,1084 @@
+import collections
+import json
+import logging
+import os
+import random
+import time
+from io import open
+import gzip
+import datetime
+import csv
+import string
+import re
+
+from torch.utils.data import DataLoader, TensorDataset
+from datasets import load_dataset
+
+INITIAL_REWARD = 1
+
+
+class SquadExample(object):
+    """
+    A single training/test example for the Squad dataset.
+    For examples without an answer, the start and end position are -1.
+    """
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 paragraph_text,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.paragraph_text = paragraph_text
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (self.qas_id)
+        s += ", question_text: %s" % (self.question_text)
+        s += ", paragraph_text: %s" % (self.paragraph_text)
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.end_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.is_impossible:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
+
+class FeedbackFeatures(object):
+    """A single set of features of data."""
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 tokens,
+                 token_to_orig_map,
+                 doc_token_offset,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 token_is_max_context,
+                 start_sample=None,
+                 end_sample=None,
+                 class_sample=None,
+                 reward=0,
+                 class_reward=0,
+                 log_prob=0,
+                 class_log_prob=0):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.doc_token_offset = doc_token_offset
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.token_is_max_context = token_is_max_context
+        self.start_sample = start_sample
+        self.end_sample = end_sample
+        self.class_sample = class_sample
+        self.reward = reward
+        self.class_reward = class_reward
+        self.log_prob = log_prob
+        self.class_log_prob = class_log_prob
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+        return re.sub(regex, ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def get_tydi_data(input_file):
+    """
+    Only load Enligsh data from tydi qa dataset
+    """
+    input_data = []
+    with gzip.GzipFile(input_file, 'r') as reader:
+        # skip header
+        content = reader.read().decode('utf-8').strip().split('\n')[1:]
+        for line in content:
+            ex = json.loads(line)
+            # only choose english
+            if ex['language'] == 'english':
+                YES_NO = [annotation['yes_no_answer'] for annotation in ex['annotations']]
+                # ignore YES/NO examples
+                if 'YES' not in YES_NO:
+                    input_data.append(ex)
+    assert (len(input_data) != 0)
+    return input_data
+
+def get_mrqa_data(input_file):
+    input_data = []
+    id_=0
+    with gzip.GzipFile(input_file, 'r') as reader:
+        # skip header
+        content = reader.read().decode('utf-8').strip().split('\n')[1:]
+        for line in content:
+            ex = json.loads(line)
+            for qa in ex['qas']:
+                inst = {}
+                inst['title'] = ''
+                inst['context'] = ex['context']
+                inst['question'] = qa['question']
+                inst['example_id'] = str(id_)
+                inst['id'] = str(inst['example_id'])
+                inst['annotations'] = [{'orig_answer_text':l} for l in qa['answers']]
+                inst['detected_answers'] = qa['detected_answers']
+                id_ += 1
+                input_data.append(inst)
+    assert (len(input_data) != 0)
+    return input_data
+
+def get_nq_data(input_file):
+    """
+    Only load Enligsh data from tydi qa dataset
+    """
+    input_data = []
+    id_ = 0
+    with gzip.GzipFile(input_file, 'r') as reader:
+        # skip header
+        content = reader.read().decode('utf-8').strip().split('\n')[1:]
+        for line in content:
+            ex = json.loads(line)
+
+            for qa in ex['qas']:
+                inst = {}
+                inst['title'] = ''
+                inst['context'] = ex['context']
+                inst['question'] = qa['question']
+                inst['example_id'] = str(id_)
+                inst['id'] = str(inst['example_id'])
+                inst['annotations'] = [{'orig_answer_text': l} for l in qa['answers']]
+                id_ += 1
+                input_data.append(inst)
+
+    assert (len(input_data) != 0)
+    return input_data
+
+
+def get_feedback_data(input_file):
+    # read data
+    with gzip.GzipFile(input_file, 'r') as reader:
+        content = reader.read().decode('utf-8').strip().split('\n')
+        input_data = [json.loads(line) for line in content]
+
+    for i, inst in enumerate(input_data):
+        inst['example_id'] = str(i)
+
+        ## for validation, keep the format the same as SQuAD data
+        if 'annotations' in inst:
+            if 'orig_answer_text' not in inst['annotations'][0]:
+                inst['annotations'] = [{'orig_answer_text': l} for l in inst['annotations']]
+        elif 'annotation' in inst:
+            inst['annotations'] = [{'orig_answer_text': inst['annotation']}]
+    return input_data
+
+
+# https://github.com/google-research-datasets/tydiqa/blob/43cde6d598c1cf88c1a8b9ed32e89263ffb5e03b/tydi_eval.py#L239
+# return a string
+def byte_slice(text, start, end):
+    byte_str = bytes(text, 'utf-8')
+    # return str(byte_str[start:end])
+    return byte_str[start:end].decode('utf-8')
+
+
+
+def read_mrqa_examples_and_features(is_training,
+                                     version_2_with_negative,
+                                     tokenizer,
+                                     max_seq_length,
+                                     prepend_title,
+                                     input_data,
+                                     get_dataset=False):
+    """Read a SQuAD json file into a list of SquadExample."""
+
+    assert (len(input_data) != 0)
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    dataset = []
+    features = []
+    truncated_cnt = 0
+    filtered = 0
+    unique_id = 1000000000
+    example_index = 0
+    for entry in input_data:
+        title = entry["title"]
+        paragraph_text = entry["context"]
+        qas_id = entry["id"]
+        question_text = entry["question"]
+        start_position = None
+        end_position = None
+        orig_answer_text = None
+        is_impossible = False
+        padding_length = 0
+        
+        # prepare inputs
+        query_tokens = tokenizer.tokenize(question_text)
+        context_tokens = tokenizer.tokenize(paragraph_text)
+        if prepend_title:
+            title_tokens = tokenizer.tokenize(title)
+
+        # truncate if needed
+        if prepend_title:
+            max_context_length = max_seq_length - len(query_tokens) - len(title_tokens) - 4
+        else:
+            max_context_length = max_seq_length - len(query_tokens) - 3
+
+        if len(query_tokens) > 100:
+            filtered += 1
+            continue
+
+        if len(context_tokens) > max_context_length:
+            context_tokens = context_tokens[0:max_context_length]
+        else:
+            padding_length = max_context_length - len(context_tokens)
+
+        # convert to indices
+        tokens = []
+        segment_ids = []
+        token_is_max_context = {}
+        doc_token_offset = 0
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        # prepend title tokens
+        if prepend_title:
+            for token in title_tokens:
+                tokens.append(token)
+                segment_ids.append(2)
+            tokens.append("[SEP]")
+            segment_ids.append(2)
+
+        doc_token_offset = len(tokens)
+        for token in context_tokens:
+            token_is_max_context[len(tokens)] = True
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        if padding_length > 0:
+            for _ in range(padding_length):
+                tokens.append("[PAD]")
+                segment_ids.append(0)
+
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * (max_seq_length - padding_length) + [0] * padding_length
+
+        # filter [CLS] token to get the real offset mapping
+        offset_mapping = tokenizer(paragraph_text, 
+                                   return_offsets_mapping=True, 
+                                   truncation=True, 
+                                   max_length=max_context_length + 
+                                   2)['offset_mapping'][1:-1] 
+
+        # get character to wrod offset for the document(context)
+        char_to_word_offset = {}
+        for index, offset in enumerate(offset_mapping):  
+            assert (offset[0] + offset[1])
+            for o in range(offset[0], offset[1]):
+                char_to_word_offset[o] = index
+
+        
+        orig_answer_text = entry["detected_answers"][0]["text"]
+        answer_length = len(orig_answer_text)
+        char_start = entry["detected_answers"][0]["char_spans"][0][0]
+        char_end = entry["detected_answers"][0]["char_spans"][0][1]
+        # char_end = char_start + answer_length - 1
+
+        # check if the answer is truncated by tokenization
+        if (char_start not in char_to_word_offset or char_end not in char_to_word_offset):
+            max_char = max([k for k in char_to_word_offset.keys()])
+
+            start_position = -1
+            end_position = -1
+            orig_answer_text = ""
+
+            if is_training:
+                truncated_cnt += 1
+                continue
+        else:
+            start_position = char_to_word_offset[char_start] + doc_token_offset
+            end_position = char_to_word_offset[char_end] + doc_token_offset
+
+        # check if the tokenized answer matches the original one
+        if normalize_answer(orig_answer_text).replace(" ", "") != normalize_answer("".join(
+            paragraph_text[offset_mapping[start_position-doc_token_offset][0]:
+                offset_mapping[end_position - 
+                    doc_token_offset][1]])).replace(" ", ""):
+            if is_training:
+                filtered += 1
+                continue    
+
+
+        example = SquadExample(qas_id=qas_id,
+                       question_text=question_text,
+                       paragraph_text=paragraph_text,
+                       orig_answer_text=orig_answer_text,
+                       start_position=start_position,
+                       end_position=end_position,
+                       is_impossible=False)
+        examples.append(example)
+
+        features.append(
+                FeedbackFeatures(unique_id=unique_id,
+                     example_index=example_index,
+                     tokens=tokens,
+                     token_to_orig_map=offset_mapping,
+                     token_is_max_context=token_is_max_context,
+                     doc_token_offset=doc_token_offset,
+                     input_ids=input_ids,
+                     input_mask=input_mask,
+                     segment_ids=segment_ids,
+                     start_sample=start_position,
+                     end_sample=end_position,
+                     reward=INITIAL_REWARD
+                )
+            )
+        unique_id += 1
+        example_index += 1
+
+        if get_dataset:
+            dataset.append(entry)
+
+    print('filtered %d examples..., truncated %d examples'%(filtered, truncated_cnt))
+    
+    assert len(features) == len(examples)
+    if get_dataset:
+        assert len(dataset) == len(examples)
+    return examples, dataset, features
+
+def read_squad_examples_and_features(is_training,
+                                     version_2_with_negative,
+                                     tokenizer,
+                                     max_seq_length,
+                                     prepend_title,
+                                     input_data,
+                                     get_dataset=True):
+    """Read a SQuAD json file into a list of SquadExample."""
+
+    assert (len(input_data) != 0)
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    dataset = []
+    features = []
+    truncated_cnt = 0
+    filtered = 0
+    unique_id = 1000000000
+    example_index = 0
+    for entry in input_data:
+        title = entry["title"]
+        paragraph_text = entry["context"]
+        qas_id = entry["id"]
+        question_text = entry["question"]
+        start_position = None
+        end_position = None
+        orig_answer_text = None
+        is_impossible = False
+        padding_length = 0
+
+        # prepare inputs
+        query_tokens = tokenizer.tokenize(question_text)
+        context_tokens = tokenizer.tokenize(paragraph_text)
+        if prepend_title:
+            title_tokens = tokenizer.tokenize(title)
+
+        # truncate if needed
+        if prepend_title:
+            max_context_length = max_seq_length - len(query_tokens) - len(title_tokens) - 4
+        else:
+            max_context_length = max_seq_length - len(query_tokens) - 3
+
+        if len(query_tokens) > 100:
+            filtered += 1
+            continue
+
+        if len(context_tokens) > max_context_length:
+            context_tokens = context_tokens[0:max_context_length]
+        else:
+            padding_length = max_context_length - len(context_tokens)
+
+        # convert to indices
+        tokens = []
+        segment_ids = []
+        token_is_max_context = {}
+        doc_token_offset = 0
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        # prepend title tokens
+        if prepend_title:
+            for token in title_tokens:
+                tokens.append(token)
+                segment_ids.append(2)
+            tokens.append("[SEP]")
+            segment_ids.append(2)
+
+        doc_token_offset = len(tokens)
+        for token in context_tokens:
+            token_is_max_context[len(tokens)] = True
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        if padding_length > 0:
+            for _ in range(padding_length):
+                tokens.append("[PAD]")
+                segment_ids.append(0)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * (max_seq_length - padding_length) + [0] * padding_length
+
+        # filter [CLS] token to get the real offset mapping
+        offset_mapping = tokenizer(paragraph_text,
+                                   return_offsets_mapping=True,
+                                   truncation=True,
+                                   max_length=max_context_length + 2)['offset_mapping'][1:-1]
+
+        # get character to wrod offset for the document(context)
+        char_to_word_offset = {}
+        for index, offset in enumerate(offset_mapping):
+            assert (offset[0] + offset[1])
+            for o in range(offset[0], offset[1]):
+                char_to_word_offset[o] = index
+
+        # get the start & end positions for the answer
+        if is_training:
+            if version_2_with_negative:
+                is_impossible = (len(entry["answers"]["text"]) == 0)
+
+            if (len(entry["answers"]["text"]) != 1) and (not is_impossible):
+                raise ValueError("For training, each question should have exactly 1 answer.")
+            if not is_impossible:
+                orig_answer_text = entry["answers"]["text"][0]
+                answer_length = len(orig_answer_text)
+                char_start = entry["answers"]["answer_start"][0]
+                char_end = char_start + answer_length - 1
+
+                # check if the answer is truncated by tokenization
+                if char_start not in char_to_word_offset or char_end not in char_to_word_offset:
+                    truncated_cnt += 1
+                    start_position = -1
+                    end_position = -1
+                    orig_answer_text = ""
+                    is_impossible = True
+                    continue
+                else:
+                    start_position = char_to_word_offset[char_start] + doc_token_offset
+                    end_position = char_to_word_offset[char_end] + doc_token_offset
+
+                # check if the tokenized answer matches the original one
+                if normalize_answer(orig_answer_text).replace(" ", "") != normalize_answer("".join(
+                        paragraph_text[offset_mapping[start_position - doc_token_offset][0]:
+                                       offset_mapping[end_position -
+                                                      doc_token_offset][1]])).replace(" ", ""):
+                    filtered += 1
+                    continue
+            else:
+                start_position = -1
+                end_position = -1
+                orig_answer_text = ""
+
+        example = SquadExample(qas_id=qas_id,
+                               question_text=question_text,
+                               paragraph_text=paragraph_text,
+                               orig_answer_text=orig_answer_text,
+                               start_position=start_position,
+                               end_position=end_position,
+                               is_impossible=version_2_with_negative and is_impossible)
+        examples.append(example)
+        if get_dataset:
+            dataset.append({
+                'question_text':
+                question_text,
+                'example_id':
+                qas_id,
+                'context':
+                paragraph_text,
+                # for SQuAD 2.0, 'annotations' would be an empty list
+                'annotations': [{
+                    'orig_answer_text': a
+                } for a in entry['answers']['text']],
+                'is_impossible': (len(entry["answers"]["text"]) == 0),
+            })
+
+        features.append(
+            FeedbackFeatures(unique_id=unique_id,
+                             example_index=example_index,
+                             tokens=tokens,
+                             token_to_orig_map=offset_mapping,
+                             token_is_max_context=token_is_max_context,
+                             doc_token_offset=doc_token_offset,
+                             input_ids=input_ids,
+                             input_mask=input_mask,
+                             segment_ids=segment_ids,
+                             start_sample=start_position,
+                             end_sample=end_position,
+                             reward=INITIAL_REWARD))
+        unique_id += 1
+        example_index += 1
+
+    print('filtered %d examples..., truncated %d examples' % (filtered, truncated_cnt))
+    if get_dataset:
+        assert len(examples) == len(dataset)
+    assert len(features) == len(examples)
+    return examples, dataset, features
+
+
+def read_tydi_examples_and_features(input_data, is_training, version_2_with_negative, tokenizer,
+                                    max_seq_length, prepend_title):
+    """Read a tydi json file into a list of SquadExample."""
+    unique_id = 1000000000
+
+    examples = []
+    features = []
+    num_impos = 0
+    example_index = 0
+    truncated_cnt = 0
+    for entry in input_data:
+
+        is_impossible_list = []
+        for idx in range(len(entry['annotations'])):
+            is_impossible_list.append(
+                entry['annotations'][idx]['minimal_answer']['plaintext_start_byte'] == -1)
+        is_impossible = (is_impossible_list.count(True) > is_impossible_list.count(False))
+
+        if is_training:
+            # for training, only 1 annotation
+            pas_index = entry['annotations'][0]['passage_answer']['candidate_index']
+            if pas_index == -1:
+                pas_index = 0
+        else:
+            # for eval
+            if is_impossible:  # if is unanswerable, should be unanswerable for any passage (?)
+                pas_index = 0
+            else:
+                pas_index = -1
+                annotation_idx = 0
+                while pas_index == -1:
+                    pas_index = entry['annotations'][annotation_idx]['passage_answer'][
+                        'candidate_index']
+                    annotation_idx += 1
+
+        # skip pas_index=1 if only having 1 passage
+        if pas_index >= len(entry['passage_answer_candidates']):
+            continue
+
+        # get passage and answer starting position in byte
+        pas_start_byte = entry['passage_answer_candidates'][pas_index]['plaintext_start_byte']
+        pas_end_byte = entry['passage_answer_candidates'][pas_index]['plaintext_end_byte']
+        plaintext_start_byte = entry['annotations'][0]['minimal_answer']['plaintext_start_byte']
+        plaintext_end_byte = entry['annotations'][0]['minimal_answer']['plaintext_end_byte']
+        relative_start_byte = plaintext_start_byte - pas_start_byte
+        relative_end_byte = plaintext_end_byte - pas_start_byte
+
+        paragraph_text = byte_slice(entry['document_plaintext'], pas_start_byte, pas_end_byte)
+
+        # get byte to char offset
+        byte_to_char = []
+        for idx, c in enumerate(paragraph_text):
+            for _ in range(len(c.encode())):
+                byte_to_char.append(idx)
+
+        qas_id = entry['example_id']  # FIXME shared by two passages
+        question_text = entry['question_text']
+        title = entry['document_title']
+        start_position = None
+        end_position = None
+        orig_answer_text = byte_slice(text=entry['document_plaintext'],
+                                      start=plaintext_start_byte,
+                                      end=plaintext_end_byte)
+
+        padding_length = 0
+
+        # prepare inputs
+        query_tokens = tokenizer.tokenize(question_text)
+        context_tokens = tokenizer.tokenize(paragraph_text)
+        if prepend_title:
+            title_tokens = tokenizer.tokenize(title)
+
+        # truncate if needed
+        if prepend_title:
+            max_context_length = max_seq_length - len(query_tokens) - len(title_tokens) - 4
+        else:
+            max_context_length = max_seq_length - len(query_tokens) - 3
+
+        if len(context_tokens) > max_context_length:
+            context_tokens = context_tokens[0:max_context_length]
+        else:
+            padding_length = max_context_length - len(context_tokens)
+
+        # convert to indices
+        tokens = []
+        segment_ids = []
+        token_is_max_context = {}
+        doc_token_offset = 0
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        # prepend title tokens
+        if prepend_title:
+            for token in title_tokens:
+                tokens.append(token)
+                segment_ids.append(2)
+            tokens.append("[SEP]")
+            segment_ids.append(2)
+
+        doc_token_offset = len(tokens)
+        for token in context_tokens:
+            token_is_max_context[len(tokens)] = True
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        if padding_length > 0:
+            for _ in range(padding_length):
+                tokens.append("[PAD]")
+                segment_ids.append(0)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * (max_seq_length - padding_length) + [0] * padding_length
+
+        # filter [CLS] token to get the real offset mapping
+        offset_mapping = tokenizer(paragraph_text,
+                                   return_offsets_mapping=True,
+                                   truncation=True,
+                                   max_length=max_context_length + 2)['offset_mapping'][1:-1]
+
+        # get character to wrod offset for the document(context)
+        char_to_word_offset = {}
+        for index, offset in enumerate(offset_mapping):
+            assert (offset[0] + offset[1])
+            for o in range(offset[0], offset[1]):
+                char_to_word_offset[o] = index
+
+        if is_training:
+            if version_2_with_negative:
+                assert is_impossible == (relative_start_byte < 0) or (
+                    relative_start_byte >=
+                    (pas_end_byte - pas_start_byte)) or (relative_end_byte >=
+                                                         (pas_end_byte - pas_start_byte))
+
+            if (len(entry['annotations']) != 1) and (not is_impossible):
+                raise ValueError("For training, each question should have exactly 1 answer.")
+
+            if not is_impossible:
+                char_start = byte_to_char[relative_start_byte]
+                char_end = byte_to_char[relative_end_byte - 1]
+
+                if char_start not in char_to_word_offset:
+                    truncated_cnt += 1
+                    start_position = -1
+                    end_position = -1
+                    orig_answer_text = ""
+                    is_impossible = True
+                else:
+                    start_position = char_to_word_offset[char_start] + doc_token_offset
+                    end_position = char_to_word_offset[char_end] + doc_token_offset
+
+            else:
+                start_position = -1
+                end_position = -1
+                orig_answer_text = ""
+                num_impos += 1
+
+        example = SquadExample(
+            qas_id=qas_id,
+            question_text=question_text,
+            paragraph_text=paragraph_text,
+            orig_answer_text=orig_answer_text,
+            start_position=(start_position if start_position == None else start_position -
+                            doc_token_offset),
+            end_position=(end_position if end_position == None else end_position -
+                          doc_token_offset),
+            is_impossible=is_impossible)
+        examples.append(example)
+
+        features.append(
+            FeedbackFeatures(unique_id=unique_id,
+                             example_index=example_index,
+                             tokens=tokens,
+                             token_to_orig_map=offset_mapping,
+                             token_is_max_context=token_is_max_context,
+                             doc_token_offset=doc_token_offset,
+                             input_ids=input_ids,
+                             input_mask=input_mask,
+                             segment_ids=segment_ids,
+                             start_sample=start_position,
+                             end_sample=end_position,
+                             reward=INITIAL_REWARD))
+        unique_id += 1
+        example_index += 1
+
+    print('truncated', truncated_cnt)
+    assert len(examples) == len(features)
+    return examples, features
+
+
+def read_feedback_examples_and_features(input_data,
+                                        negative_reward,
+                                        partial_reward,
+                                        reward_wrong_unans,
+                                        reward_correct_span,
+                                        reward_correct_unans,
+                                        reward_class_wrong,
+                                        reward_class_correct_ans,
+                                        tokenizer,
+                                        max_seq_length,
+                                        prepend_title,
+                                        load_log_prob=False):
+    unique_id = 1000000000
+
+    examples = []
+    features = []
+
+    i = 0
+    for inst in input_data:
+        qas_id = inst['example_id']
+        question_text = inst['question']
+        paragraph_text = inst['context']
+        feedback = inst['feedback']
+        title = inst['topic'] + ' [SEP] ' + inst['aspect']
+        startidx = inst['startidx']
+        endidx = inst['endidx']
+
+        padding_length = 0
+
+        # prepare inputs
+        query_tokens = tokenizer.tokenize(question_text)
+        context_tokens = tokenizer.tokenize(paragraph_text)
+        if prepend_title:
+            title_tokens = tokenizer.tokenize(title)
+
+        # truncate if needed
+        if prepend_title:
+            max_context_length = max_seq_length - len(query_tokens) - len(title_tokens) - 4
+        else:
+            max_context_length = max_seq_length - len(query_tokens) - 3
+
+        if len(context_tokens) > max_context_length:
+            context_tokens = context_tokens[0:max_context_length]
+        else:
+            padding_length = max_context_length - len(context_tokens)
+
+        # convert to indices
+        tokens = []
+        segment_ids = []
+        token_is_max_context = {}
+        doc_token_offset = 0
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        # prepend title tokens
+        if prepend_title:
+            for token in title_tokens:
+                tokens.append(token)
+                segment_ids.append(2)
+            tokens.append("[SEP]")
+            segment_ids.append(2)
+
+        doc_token_offset = len(tokens)
+        for token in context_tokens:
+            token_is_max_context[len(tokens)] = True
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        if padding_length > 0:
+            for _ in range(padding_length):
+                tokens.append("[PAD]")
+                segment_ids.append(0)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * (max_seq_length - padding_length) + [0] * padding_length
+
+        offset_mapping = tokenizer(
+            paragraph_text,
+            return_offsets_mapping=True,
+            truncation=True,
+            max_length=max_context_length +
+            2)['offset_mapping'][1:-1]  # filter [CLS] token to get the real offset mapping
+
+        # get character to wrod offset for the document(context)
+        char_to_word_offset = {}
+        for index, offset in enumerate(offset_mapping):
+            assert (offset[0] + offset[1])
+            for o in range(offset[0], offset[1]):
+                char_to_word_offset[o] = index
+
+        if feedback in ['Unanswerable', 'Answerable']:
+            # just as placeholder, not for eval or training
+            start_position = -1
+            end_position = -1
+            class_position = 0
+            reward = 0
+            class_reward = 0
+        else:
+            pred = inst['pred']
+            # handle unanswerable
+            if pred == '[Unanswerable given the paragraph below]':
+                start_position = -1
+                end_position = -1
+                class_position = 0
+            else:
+                start_position = startidx
+                end_position = endidx
+                class_position = 1
+
+            if pred == '[Unanswerable given the paragraph below]': # predict unans, second action reward = 0
+                reward = 0
+                if feedback == 'Correct':
+                    class_reward = 1
+                elif feedback == 'Wrong':
+                    class_reward = -1
+                else:
+                    class_reward = 0
+            else:
+                if feedback == 'Correct':
+                    class_reward = reward_class_correct_ans
+                    reward = reward_correct_span
+                elif feedback == 'Wrong':
+                    class_reward = reward_class_wrong
+                    reward = negative_reward
+                else:
+                    class_reward = reward_class_correct_ans
+                    reward = partial_reward * reward_correct_span
+
+
+        if load_log_prob:
+            assert 'log_prob' in inst
+            log_prob = float(inst['log_prob'])
+            assert 'class_log_prob' in inst
+            class_log_prob = float(inst['class_log_prob'])
+        else:
+            log_prob = 0
+            class_log_prob = 0
+
+        assert log_prob <= 0
+        assert class_log_prob <= 0
+        features.append(
+            FeedbackFeatures(unique_id=unique_id,
+                             example_index=i,
+                             tokens=tokens,
+                             token_to_orig_map=offset_mapping,
+                             token_is_max_context=token_is_max_context,
+                             doc_token_offset=doc_token_offset,
+                             input_ids=input_ids,
+                             input_mask=input_mask,
+                             segment_ids=segment_ids,
+                             start_sample=start_position,
+                             end_sample=end_position,
+                             class_sample=class_position,
+                             reward=reward,
+                             class_reward=class_reward,
+                             log_prob=log_prob,
+                             class_log_prob=class_log_prob
+                             ))
+        examples.append(
+            SquadExample(qas_id=qas_id,
+                         question_text=question_text,
+                         paragraph_text=paragraph_text,
+                         start_position=start_position,
+                         end_position=end_position))
+
+        # keep track of counts
+        i += 1
+        unique_id += 1
+
+    return examples, features
+
+
+def read_feedback_examples_and_features_supervised(input_data,
+                                        tokenizer,
+                                        max_seq_length,
+                                        prepend_title):
+    unique_id = 1000000000
+
+    dataset = []
+    examples = []
+    features = []
+
+    i = 0
+    for inst in input_data:
+        qas_id = inst['example_id']
+        question_text = inst['question']
+        paragraph_text = inst['context']
+        feedback = inst['feedback']
+
+        if feedback != 'Correct' and feedback not in ['Unanswerable', 'Answerable']:
+            continue
+
+        title = inst['topic'] + ' [SEP] ' + inst['aspect']
+        startidx = inst['startidx']
+        endidx = inst['endidx']
+
+        padding_length = 0
+
+        # prepare inputs
+        query_tokens = tokenizer.tokenize(question_text)
+        context_tokens = tokenizer.tokenize(paragraph_text)
+        if prepend_title:
+            title_tokens = tokenizer.tokenize(title)
+
+        # truncate if needed
+        if prepend_title:
+            max_context_length = max_seq_length - len(query_tokens) - len(title_tokens) - 4
+        else:
+            max_context_length = max_seq_length - len(query_tokens) - 3
+
+        if len(context_tokens) > max_context_length:
+            context_tokens = context_tokens[0:max_context_length]
+        else:
+            padding_length = max_context_length - len(context_tokens)
+
+        # convert to indices
+        tokens = []
+        segment_ids = []
+        token_is_max_context = {}
+        doc_token_offset = 0
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        # prepend title tokens
+        if prepend_title:
+            for token in title_tokens:
+                tokens.append(token)
+                segment_ids.append(2)
+            tokens.append("[SEP]")
+            segment_ids.append(2)
+
+        doc_token_offset = len(tokens)
+        for token in context_tokens:
+            token_is_max_context[len(tokens)] = True
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        if padding_length > 0:
+            for _ in range(padding_length):
+                tokens.append("[PAD]")
+                segment_ids.append(0)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * (max_seq_length - padding_length) + [0] * padding_length
+
+        offset_mapping = tokenizer(
+            paragraph_text,
+            return_offsets_mapping=True,
+            truncation=True,
+            max_length=max_context_length +
+            2)['offset_mapping'][1:-1]  # filter [CLS] token to get the real offset mapping
+
+        # get character to wrod offset for the document(context)
+        char_to_word_offset = {}
+        for index, offset in enumerate(offset_mapping):
+            assert (offset[0] + offset[1])
+            for o in range(offset[0], offset[1]):
+                char_to_word_offset[o] = index
+
+        
+        if feedback in ['Unanswerable', 'Answerable']:
+            # just as placeholder, not for eval or training
+            start_position = -1
+            end_position = -1
+            reward = 0
+        else:
+            pred = inst['pred']
+            # handle unanswerable
+            if pred == '[Unanswerable given the paragraph below]':
+                start_position = -1
+                end_position = -1
+            else:
+                start_position = startidx
+                end_position = endidx
+
+            
+        features.append(
+            FeedbackFeatures(unique_id=unique_id,
+                             example_index=i,
+                             tokens=tokens,
+                             token_to_orig_map=offset_mapping,
+                             token_is_max_context=token_is_max_context,
+                             doc_token_offset=doc_token_offset,
+                             input_ids=input_ids,
+                             input_mask=input_mask,
+                             segment_ids=segment_ids,
+                             start_sample=start_position,
+                             end_sample=end_position,
+                             reward=INITIAL_REWARD,
+                             log_prob=0))
+        examples.append(
+            SquadExample(qas_id=qas_id,
+                         question_text=question_text,
+                         paragraph_text=paragraph_text,
+                         start_position=start_position,
+                         end_position=end_position))
+
+        dataset.append(inst)
+        # keep track of counts
+        i += 1
+        unique_id += 1
+
+    return examples, dataset, features
diff --git a/src/eval.py b/src/eval.py
new file mode 100644
index 0000000..d20029e
--- /dev/null
+++ b/src/eval.py
@@ -0,0 +1,537 @@
+from __future__ import absolute_import, division, print_function
+import collections
+import json
+import logging
+import os
+import re
+import string
+import torch
+import math
+
+from IPython import embed
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
+                    datefmt='%m/%d/%Y %H:%M:%S',
+                    level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+RawResult = collections.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits", "answerable"])
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+
+def make_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length,
+                     version_2_with_negative):
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+    _PrelimPrediction = collections.namedtuple(
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+        result = unique_id_to_result[features[0].unique_id]
+        if result.answerable != None and not result.answerable:
+            all_predictions[example.qas_id] = ''
+            continue
+        prelim_predictions = []
+        score_null = 1000000
+        min_null_feature_index = 0
+        null_start_logit = 0
+        null_end_logit = 0
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            if version_2_with_negative:
+                feature_null_score = result.start_logits[0] + result.end_logits[0]
+                if feature_null_score < score_null:
+                    score_null = feature_null_score
+                    min_null_feature_index = feature_index
+                    null_start_logit = result.start_logits[0]
+                    null_end_logit = result.end_logits[0]
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+
+                    if not feature.token_is_max_context.get(start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    if start_index == 0 and end_index != 0:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(feature_index=feature_index,
+                                          start_index=start_index,
+                                          end_index=end_index,
+                                          start_logit=result.start_logits[start_index],
+                                          end_logit=result.end_logits[end_index]))
+        if version_2_with_negative:
+            if result.answerable == None or (not result.answerable):
+                prelim_predictions.append(
+                    _PrelimPrediction(feature_index=min_null_feature_index,
+                                      start_index=0,
+                                      end_index=0,
+                                      start_logit=null_start_logit,
+                                      end_logit=null_end_logit))
+    
+        prelim_predictions = sorted(prelim_predictions,
+                                    key=lambda x: (x.start_logit + x.end_logit),
+                                    reverse=True)
+        _NbestPrediction = collections.namedtuple("NbestPrediction",
+                                                  ["text", "start_logit", "end_logit"])
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+
+            feature = features[pred.feature_index]
+            if pred.start_index > 0:
+                # this should work!
+                orig_doc_start_char = feature.token_to_orig_map[pred.start_index -
+                                                                feature.doc_token_offset][0]
+
+                # getting the end index of the answer span (in character)
+                if pred.end_index - feature.doc_token_offset >= len(feature.token_to_orig_map):
+                    # if the end index is in the [PAD] area, then make it the last token in context
+                    orig_doc_end_char = feature.token_to_orig_map[-1][1]
+                else:
+                    orig_doc_end_char = feature.token_to_orig_map[pred.end_index -
+                                                                  feature.doc_token_offset][1]
+
+                ans_text = example.paragraph_text[orig_doc_start_char:orig_doc_end_char]
+
+                ans_text = ans_text.replace(" ##", "")
+                ans_text = ans_text.replace("##", "")
+                ans_text = ans_text.strip()
+                final_text = ans_text
+
+                if final_text in seen_predictions:
+                    continue
+                seen_predictions[final_text] = True
+            else:
+                final_text = ""
+                seen_predictions[final_text] = True
+
+            nbest.append(
+                _NbestPrediction(text=final_text,
+                                 start_logit=pred.start_logit,
+                                 end_logit=pred.end_logit))
+
+        if version_2_with_negative:
+            if "" not in seen_predictions:
+                nbest.append(
+                    _NbestPrediction(text="",
+                                     start_logit=null_start_logit,
+                                     end_logit=null_end_logit))
+            if len(nbest) == 1:
+                nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+        if not nbest:
+            nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+        assert len(nbest) >= 1
+
+        all_predictions[example.qas_id] = nbest[0].text
+    return all_predictions
+
+
+def make_qid_to_has_ans(dataset, dataset_name='tydi', pred=None):
+    qid_to_has_ans = {}
+    for entry in dataset:
+        if dataset_name == 'tydi':
+            # NOTE changed tydi eval
+            is_impossible_list = []
+            for idx in range(len(entry['annotations'])):
+                is_impossible_list.append(
+                    entry['annotations'][idx]['minimal_answer']['plaintext_start_byte'] == -1)
+            is_impossible = (is_impossible_list.count(True) > is_impossible_list.count(False))
+            qid_to_has_ans[entry['example_id']] = not is_impossible
+            # qid_to_has_ans[entry['example_id']] = sum([int(entry['annotations'][i]['minimal_answer']['plaintext_start_byte'] == -1) for i in range(len(entry['annotations']))]) == 0
+        elif dataset_name == 'squad' or dataset_name == 'nq' or dataset_name == 'newsqa' or dataset_name == 'searchqa':
+            qid_to_has_ans[entry['example_id']] = True
+        elif dataset_name == 'squad2':
+            qid_to_has_ans[entry['example_id']] = not entry['is_impossible']
+        elif dataset_name == 'feedback':
+            is_impossible_list = []
+            for idx in range(len(entry['annotations'])):
+                is_impossible_list.append(entry['annotations'][idx]['orig_answer_text'] == '')
+            is_impossible = (is_impossible_list.count(True) > is_impossible_list.count(False))
+            qid_to_has_ans[entry['example_id']] = not is_impossible
+
+        else:
+            raise NotImplementedError
+    return qid_to_has_ans
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+        return re.sub(regex, ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def get_tokens(s):
+    if not s:
+        return []
+    return normalize_answer(s).split()
+
+
+def compute_exact(a_gold, a_pred):
+    return int(normalize_answer(a_gold) == normalize_answer(a_pred))
+
+
+def compute_f1(a_gold, a_pred):
+    gold_toks = get_tokens(a_gold)
+    pred_toks = get_tokens(a_pred)
+    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+    num_same = sum(common.values())
+    if len(gold_toks) == 0 or len(pred_toks) == 0:
+        return int(gold_toks == pred_toks)
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def get_raw_scores(dataset, preds, partial_reward, negative_reward, calculate_reward=False):
+    exact_scores = {}
+    f1_scores = {}
+    rewards = {}
+    classification_accs = {}
+    for entry in dataset:
+        qid = entry['example_id']
+        gold_answers = []
+        for a in entry['annotations']:
+            if 'orig_answer_text' in a:
+                # if the example belongs to SQuAD/SQuAD2.0/feedback
+                if normalize_answer(a['orig_answer_text']):
+                    gold_answers.append(a['orig_answer_text'])
+                else:
+                    gold_answers.append('')
+            else:
+                # if the example belongs to TyDiQA
+                plaintext_start_byte = a['minimal_answer']['plaintext_start_byte']
+                plaintext_end_byte = a['minimal_answer']['plaintext_end_byte']
+                paragraph_text = entry['document_plaintext']
+                orig_answer_text = paragraph_text[plaintext_start_byte:plaintext_end_byte]
+                if normalize_answer(orig_answer_text):
+                    gold_answers.append(orig_answer_text)
+                else:
+                    gold_answers.append('')
+
+        # changed tydi eval (and for feedback also)
+        # deal with majority vote of unans
+        if gold_answers.count('') > (len(gold_answers) - gold_answers.count('')):
+            gold_answers = ['']
+        else:
+            span_answers = []
+            for span in gold_answers:
+                if span != '':
+                    span_answers.append(span)
+            gold_answers = span_answers
+
+        # for SQuAD 2.0 unanswerable
+        if len(gold_answers) == 0:
+            gold_answers = ['']
+
+        if qid not in preds:
+            print('Missing prediction for %s' % qid)
+            continue
+        a_pred = preds[qid]
+
+        if gold_answers[0] == '':
+            classification_accs[qid] = (a_pred == '')
+        else:
+            classification_accs[qid] = (a_pred != '')
+
+        exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
+        f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
+        inst_rewards = []
+        if calculate_reward:
+            for a in gold_answers:
+                f1_score = compute_f1(a, a_pred)
+                if a_pred == '':
+                    if f1_score == 1:
+                        inst_rewards.append(0.4)
+                    else:
+                        inst_rewards.append(-2)
+                else:
+                    if f1_score == 1:
+                        inst_rewards.append(2)
+                    elif f1_score == 0:
+                        inst_rewards.append(-0.1)
+                    else:
+                        inst_rewards.append(1)
+
+            rewards[qid] = max(inst_rewards)
+    return exact_scores, f1_scores, rewards, classification_accs
+
+
+def make_eval_dict(exact_scores=None, f1_scores=None, rewards=None, classification_accs=None, qid_list=None):
+    if not qid_list:
+        total = len(exact_scores)
+        out = [('total', total)]
+        if exact_scores != None:
+            out.append(('exact', 100.0 * sum(exact_scores.values()) / total))
+        if f1_scores != None:
+            out.append(('f1', 100.0 * sum(f1_scores.values()) / total))
+        if rewards != None:
+            out.append(('reward', sum(rewards.values())))
+        if classification_accs != None:
+            out.append(('classification_acc', 100.0 * sum(classification_accs.values()) / total))
+        return collections.OrderedDict(out)
+    else:
+        total = len(qid_list)
+        out = [('total', total)]
+        if exact_scores != None:
+            out.append(('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total))
+        if f1_scores != None:
+            out.append(('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total))
+        if rewards != None:
+            out.append(('reward', sum(rewards[k] for k in qid_list)))
+        if classification_accs != None:
+            out.append(('classification_acc', 100.0 * sum(classification_accs.values()) / total))
+        return collections.OrderedDict(out)
+
+
+def merge_eval(main_eval, new_eval, prefix):
+    for k in new_eval:
+        main_eval['%s_%s' % (prefix, k)] = new_eval[k]
+
+
+def evaluate(args,
+             model,
+             device,
+             eval_dataset,
+             eval_dataloader,
+             eval_examples,
+             eval_features,
+             na_prob_thresh,
+             tokenizer,
+             dataset_name,
+             calculate_score=True,
+             classifier=False):
+    all_results = []
+    model.eval()
+
+    for idx, (input_ids, input_mask, segment_ids, example_indices) in enumerate(eval_dataloader):
+        if idx % 100 == 0:
+            logger.info("Running test: %d / %d" % (idx, len(eval_dataloader)))
+        input_ids = input_ids.to(device)
+        input_mask = input_mask.to(device)
+        segment_ids = segment_ids.to(device)
+        with torch.no_grad():
+            if classifier:
+                batch_start_logits, batch_end_logits, answerable_logits = model([input_ids, input_mask, segment_ids], classifier=True)
+                pred_answerable = answerable_logits.argmax(dim=-1)
+            else:
+                batch_start_logits, batch_end_logits, _ = model([input_ids, input_mask, segment_ids], classifier=False)
+
+        for i, example_index in enumerate(example_indices):
+            start_logits = batch_start_logits[i].detach().cpu().tolist()
+            end_logits = batch_end_logits[i].detach().cpu().tolist()
+            whether_answerable = pred_answerable[i] if classifier else None
+            eval_feature = eval_features[example_index.item()]
+            unique_id = int(eval_feature.unique_id)
+            all_results.append(
+                RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits, answerable=whether_answerable))
+
+    ### predict for all data points
+    preds = make_predictions(eval_examples, eval_features, all_results, args.n_best_size,
+                             args.max_answer_length, args.version_2_with_negative)
+
+
+    if calculate_score:
+        ## calculate scores
+        has_ans_eval = None
+        no_ans_eval = None
+        if args.version_2_with_negative:
+            # decide which data to test on
+            logger.info('validation on: %s' % dataset_name)
+
+            if dataset_name == 'feedback':
+                qid_to_has_ans = make_qid_to_has_ans(eval_dataset, dataset_name, pred=preds)
+                # split the question ids into answerable/unanswerable
+                has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
+                no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
+                # get the EM/F1/reward scores
+                exacts, f1s, rewards, classification_accs = get_raw_scores(eval_dataset,
+                                                      preds,
+                                                      args.partial_reward,
+                                                      args.negative_reward,
+                                                      calculate_reward=True)
+
+                stats = {'Correct': 0, 'Partially Correct': 0, 'Wrong': 0, 'Total': 0, 'Unans_Correct':0, 'Ans_Correct':0, 'Unans_Wrong':0, 'Ans_Wrong':0, 'Unans_Partially Correct':0, 'Ans_Partially Correct':0, 'Unans_Total':0, 'Ans_Total':0,}
+                for qid, s in exacts.items():
+                    stats['Total'] += 1
+                    if s == 1:
+                        stats['Correct'] += 1
+                    elif f1s[qid] > 0.1:
+                        stats['Partially Correct'] += 1
+                    else:
+                        stats['Wrong'] += 1
+
+                for qid, s in exacts.items():
+                    if preds[qid] == '':
+                        stats['Unans_Total'] += 1
+                        if s == 1:
+                            stats['Unans_Correct'] += 1
+                        elif f1s[qid] > 0.1:
+                            stats['Unans_Partially Correct'] += 1
+                        else:
+                            stats['Unans_Wrong'] += 1
+                    else:
+                        stats['Ans_Total'] += 1
+                        if s == 1:
+                            stats['Ans_Correct'] += 1
+                        elif f1s[qid] > 0.1:
+                            stats['Ans_Partially Correct'] += 1
+                        else:
+                            stats['Ans_Wrong'] += 1
+
+                logger.info('====================')
+                logger.info(
+                    'Feedback Type [All]: Total: %d | Correct: %2.2f | Partially Correct: %2.2f | Wrong: %2.2f' %
+                    (stats['Total'],
+                     (100 * stats['Correct'] / float(stats['Total'])),
+                     (100 * stats['Partially Correct'] / float(stats['Total'])),
+                     (100 * stats['Wrong'] / float(stats['Total'])))
+                    )
+
+                logger.info('Feedback Type : Correct: %d | Partially Correct: %d | Wrong: %d' %
+                            ((stats['Correct']), (stats['Partially Correct']), (stats['Wrong'])))
+                logger.info('====================')
+
+
+                result = make_eval_dict(exact_scores=exacts, f1_scores=f1s, rewards=rewards, classification_accs=classification_accs)
+                # merge the eval results from all subsets into a single dict
+                if has_ans_qids:
+                    has_ans_eval = make_eval_dict(exact_scores=exacts,
+                                                  f1_scores=f1s,
+                                                  rewards=rewards,
+                                                  classification_accs=classification_accs,
+                                                  qid_list=has_ans_qids)
+                    merge_eval(result, has_ans_eval, 'HasAns')
+                if no_ans_qids:
+                    no_ans_eval = make_eval_dict(exact_scores=exacts,
+                                                 f1_scores=f1s,
+                                                 rewards=rewards,
+                                                 classification_accs=classification_accs,
+                                                 qid_list=no_ans_qids)
+                    merge_eval(result, no_ans_eval, 'NoAns')
+            else:
+                # 'tydi'
+                qid_to_has_ans = make_qid_to_has_ans(eval_dataset, dataset_name)
+                has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
+                no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
+                exacts, f1s, _, classification_accs = get_raw_scores(eval_dataset, preds, 0, 0, calculate_reward=False)
+
+                result = make_eval_dict(exact_scores=exacts, f1_scores=f1s, classification_accs=classification_accs)
+                if has_ans_qids:
+                    has_ans_eval = make_eval_dict(exact_scores=exacts,
+                                                  f1_scores=f1s,
+                                                  classification_accs=classification_accs,
+                                                  qid_list=has_ans_qids)
+                    merge_eval(result, has_ans_eval, 'HasAns')
+                if no_ans_qids:
+                    no_ans_eval = make_eval_dict(exact_scores=exacts,
+                                                 f1_scores=f1s,
+                                                 classification_accs=classification_accs,
+                                                 qid_list=no_ans_qids)
+                    merge_eval(result, no_ans_eval, 'NoAns')
+
+            # log UNANS ratio: todo add this to the feedback data part?
+            result['perc. UNANS'] = sum([int(v == '') for k, v in preds.items()]) * 1.0 / len(preds)
+            if has_ans_qids:
+                result['perc. UNANS in ANS subset'] = sum(
+                    [int(preds[k] == '') for k in has_ans_qids]) * 1.0 / len(has_ans_qids)
+            if no_ans_qids:
+                result['perc. UNANS in UNANS subset'] = sum(
+                    [int(preds[k] == '') for k in no_ans_qids]) * 1.0 / len(no_ans_qids)
+            # log precision
+            ans_f1, unans_f1, ans_count = 0.0, 0.0, 0
+            for k, v in preds.items():
+                if v != '':
+                    ans_f1 += f1s[k]
+                    ans_count += 1
+                else:
+                    unans_f1 += f1s[k]
+            if ans_count:
+                result['F1 in predicted ANS subset'] = ans_f1 / ans_count
+            else:
+                result['F1 in predicted ANS subset'] = 0
+            if (len(preds) - ans_count) == 0:
+                result['F1 in predicted UNANS subset'] = 0
+            else:
+                result['F1 in predicted UNANS subset'] = unans_f1 / (len(preds) - ans_count)
+        else:
+            exact_raw, f1_raw, _, _ = get_raw_scores(eval_dataset, preds, 0, 0, calculate_reward=False)
+            result = make_eval_dict(exact_raw, f1_raw)
+        logger.info("***** Eval results *****")
+        for key in sorted(result.keys()):
+            logger.info("  %s = %s", key, str(result[key]))
+        return result, has_ans_eval, no_ans_eval, preds
+    else:
+        return None, None, None, preds
diff --git a/src_analysis/analysis_parse_preds.py b/src_analysis/analysis_parse_preds.py
new file mode 100644
index 0000000..f2e8f48
--- /dev/null
+++ b/src_analysis/analysis_parse_preds.py
@@ -0,0 +1,78 @@
+import json
+import csv
+import argparse
+
+from run_tydi import get_data, byte_slice, read_squad_dataset
+from IPython import embed
+import pathlib
+
+
+
+def main(args):
+    
+
+    pred_file = args.pred_file
+    data_file = args.data_file
+
+    if args.test_on_squad:
+        eval_dataset = read_squad_dataset(data_file)
+    else:
+        eval_dataset = get_data(data_file)
+
+    # reads it back
+    with open(pred_file, "r") as f:
+        preds_data = f.read()
+    # decoding the JSON to dictionay
+    preds = json.loads(preds_data)
+
+    
+    total = 0
+    predicted_no_answer = 0
+    accuracy = 0
+    parsed = [['qid', 'question', 'annotation', 'prediction', 'context' if args.test_on_squad else 'document_url']] 
+
+    for entry in eval_dataset:
+        qid = entry['example_id']
+        question = entry['question_text']
+        if args.test_on_squad:
+            orig_answer_text = '; '.join([l['orig_answer_text'] for l in entry['annotations']])
+        else:
+            orig_answer_text = byte_slice(text=entry['document_plaintext'],
+                                          start=entry['annotations'][0]['minimal_answer']['plaintext_start_byte'],
+                                          end=entry['annotations'][0]['minimal_answer']['plaintext_end_byte'])
+        pred = preds[str(qid)]
+        total += 1
+        if len(pred) == 0:
+            predicted_no_answer += 1
+            if len(orig_answer_text) == 0:
+                accuracy += 1
+        else:
+            if len(orig_answer_text) != 0:
+                accuracy += 1
+
+        parsed.append([qid, question, orig_answer_text, pred, entry['context'] if args.test_on_squad else entry['document_url']])
+    
+    print('total: %d | predicted_no_answer: %d \n no answer percentage: %2.3f \n No Answer Accuracy: %2.2f'%(total, predicted_no_answer, (predicted_no_answer/float(total))*100, (accuracy/float(total)*100)))
+
+
+    pathlib.Path('/'.join(args.outfile.split('/')[:-1])).mkdir(parents=True, exist_ok=True)
+
+    file = open(args.outfile, 'w', newline='')
+
+    # writing the data into the file
+    with file:
+        write = csv.writer(file, delimiter='\t')
+        write.writerows(parsed)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pred_file", default=None, type=str, required=True)
+    parser.add_argument("--data_file", default=None, type=str, required=True)
+    parser.add_argument("--outfile", default=None, type=str, required=True, help='output file')
+    parser.add_argument('--test_on_squad',
+                        action='store_true',
+                        help='whether test on SQuAD Dev data')
+    args = parser.parse_args()
+
+    main(args)
\ No newline at end of file
diff --git a/src_analysis/analysis_question.py b/src_analysis/analysis_question.py
new file mode 100644
index 0000000..1fa8d9a
--- /dev/null
+++ b/src_analysis/analysis_question.py
@@ -0,0 +1,232 @@
+from rehearsal import get_feedback_data
+from transformers import BertTokenizer, DebertaTokenizer, DebertaV2TokenizerFast, DebertaTokenizerFast
+import re
+import json
+from tqdm import tqdm
+import csv
+import collections
+import matplotlib.pyplot as plt
+
+
+
+def analyze_data(data, round_idx, test=False):
+    stats = {'correct': 0, 'wrong':0, 'unans_correct':0, 'ans_correct':0, 'unans_wrong':0, 'ans_wrong':0, 'feedback_total': 0, 'num_unans':0, 'time':0, 'annotation_total':0, 'annotation_answerable':0, 'annotation_unanswerable':0}
+    seen_workers = set()
+    seen_workers = set()
+    class_reward = 0.0
+    reward = 0.0
+    total_reward = 0.0
+    total_ans = 0
+    data_per_file = []
+
+    for dict_ in tqdm(data):
+        if dict_['workerId'] not in seen_workers:
+            seen_workers.add(dict_['workerId'])
+        
+        if not dict_['question']:
+            continue
+        if dict_['feedback'] in ["Answerable", "Unanswerable"]:
+            stats['annotation_total'] += 1
+            if dict_['feedback'] == 'Answerable':
+                stats['annotation_answerable'] += 1
+            else:
+                stats['annotation_unanswerable'] += 1
+        else:
+
+            stats['feedback_total'] += 1
+            unans = int(dict_['pred'] == '[Unanswerable given the paragraph below]')
+            stats['num_unans'] += unans
+            total_ans += (1 - unans)
+
+            if dict_['feedback'] == 'Correct': # if correct
+                stats['correct'] += 1
+                stats['unans_correct'] += unans
+                stats['ans_correct'] += (1 - unans)
+                class_reward += 1
+                if unans != 1:
+                    reward += 1
+                    total_reward += 2
+                else:
+                    total_reward += 1
+            elif dict_['feedback'] == 'Wrong': # if wrong
+                stats['wrong'] += 1
+                stats['unans_wrong'] += unans
+                stats['ans_wrong'] += (1 - unans)
+                if unans == 1:  # if unans
+                    class_reward -= 1
+                    total_reward -= 1
+                else:
+                    reward -= 0.1
+                    total_reward -= 0.1
+            else:
+                if unans != 1:  # if ans
+                    class_reward += 1
+                    reward += 0.5
+                    total_reward += 1.5
+
+
+    stats['correct'] /= float(stats['feedback_total'])
+    stats['wrong'] /= float(stats['feedback_total'])
+    stats['partial'] = 1 - stats['correct'] - stats['wrong']
+
+    stats['unans_correct'] /= float(stats['num_unans'])
+    stats['unans_wrong'] /= float(stats['num_unans'])
+    stats['unans_partial'] = 1 - stats['unans_correct'] - stats['unans_wrong']
+
+    stats['ans_correct'] /= float(stats['feedback_total'] - stats['num_unans'])
+    stats['ans_wrong'] /= float(stats['feedback_total'] - stats['num_unans'])
+    stats['ans_partial'] = 1 - stats['ans_correct'] - stats['ans_wrong']
+
+    print('======= Data Results =======')
+    print('Total: %d | Correct Per.: %2.2f | Partially Correct Per.: %2.2f | Wrong Per.: %2.2f'%(stats['feedback_total'], stats['correct'] * 100, stats['partial'] * 100, stats['wrong'] * 100))
+    print('UnAns: %d | Correct Per.: %2.2f | Partially Correct Per.: %2.2f | Wrong Per.: %2.2f'%(stats['num_unans'], stats['unans_correct'] * 100, stats['unans_partial'] * 100, stats['unans_wrong'] * 100))
+    print('Ans: %d | Correct Per.: %2.2f | Partially Correct Per.: %2.2f | Wrong Per.: %2.2f'%(stats['feedback_total'] - stats['num_unans'], stats['ans_correct'] * 100, stats['ans_partial'] * 100, stats['ans_wrong'] * 100))
+
+    if test:
+        print('======= Annotation Results =======')
+        print('Total: %d | Answerable: %d | Unanswerable: %d'%(stats['annotation_total'], stats['annotation_answerable'], stats['annotation_unanswerable']))
+
+    print('=============================')
+    print('Num Unique Workers: %d'%len(seen_workers))
+
+    csv_data = [['Round %d - Feedback'%round_idx, '', '', '', '', '', '', 'Round %d - Test'%round_idx], ['', '(%) exs', '# exs', 'Correct (%)', 'Partially Correct (%)', 'Wrong (%)', '', '# exs', 'Answerable (%)', 'Unanswerable (%)']]
+    csv_data.append(['All', '100', '%d'%stats['feedback_total'], '%2.2f'%(stats['correct'] * 100), '%2.2f'%(stats['partial'] * 100), '%2.2f'%(stats['wrong'] * 100), '', '%d'%stats['annotation_total'], '%2.2f'%(100*stats['annotation_answerable']/float(stats['annotation_total'])), '%2.2f'%(100*stats['annotation_unanswerable']/float(stats['annotation_total']))])
+    csv_data.append(['Unanswerable', '%2.2f'%(100*stats['num_unans']/float(stats['feedback_total'])), '%d'%stats['num_unans'], '%2.2f'%(stats['unans_correct'] * 100), '%2.2f'%(stats['unans_partial'] * 100), '%2.2f'%(stats['unans_wrong'] * 100)])
+    csv_data.append(['Answerable', '%2.2f'%(100*(stats['feedback_total'] - stats['num_unans'])/float(stats['feedback_total'])), '%d'%(stats['feedback_total'] - stats['num_unans']), '%2.2f'%(stats['ans_correct'] * 100), '%2.2f'%(stats['ans_partial'] * 100), '%2.2f'%(stats['ans_wrong'] * 100)])
+    return csv_data
+
+def compute_question_answer_lengths(data, tokenizer, round_idx, pred_list=None, is_training=False):
+    # for lengths
+    data_list = [data]
+    lengths = [[] for _ in range(len(data_list))]
+    if not is_training:
+        answer_lengths = [{'Correct':[0], 'Partially Correct':[0], 'Wrong':[0], 'All':[]} for _ in range(len(data_list))]
+    else:
+        answer_lengths = [{'Correct':[], 'Partially Correct':[], 'Wrong':[], 'All':[]} for _ in range(len(data_list))]
+    csv_data = [['']]
+
+    for i, dataset_ in enumerate(data_list):
+        for j, entry in enumerate(dataset_):
+            # print(entry)
+            paragraph_text = entry["context"]
+            question_text = entry["question"]
+            start_index = entry['startidx']
+            end_index = entry['endidx']
+            if pred_list:
+                prediction = pred_list[j]
+            else:
+                prediction = entry['pred']
+
+            query_tokens = tokenizer.tokenize(question_text)
+            lengths[i].append(len(query_tokens))
+            if start_index != 0 or end_index != 0:
+                answer_tokens = tokenizer.tokenize(prediction)
+                if len(answer_tokens) != 0:
+                    answer_lengths[i][entry['feedback']].append(len(answer_tokens))
+                    answer_lengths[i]['All'].append(len(answer_tokens))
+
+    bins = [i*2 for i in range(50)]
+    plt.hist(answer_lengths[0]['All'], bins=bins)
+    plt.xlabel('Lengths')
+    plt.ylabel('# Examples')
+    plt.title('Histogram of Answer Lengths')
+    plt.xlim(0, 100)
+    plt.grid(True)
+    plt.savefig('Histogram_of_Answer_Length.png')
+
+    for i, length in enumerate(lengths):
+        print('Avg Question Length of Round %d: %f'%(i, sum(length)/len(length)))
+        print('==================================')
+        print('Avg Answer Length of Round %d'%i)
+        report_strings = []
+        answer_dict = answer_lengths[i]
+        for k, v in answer_dict.items():
+            report_strings.append("%s: %f"%(k, sum(v)/len(v)))
+        print(' | '.join(report_strings))
+
+        csv_data.append(['Question Length', '%2.2f'%(sum(length)/len(length))])
+        csv_data.append([''])
+        csv_data.append(['', 'Correct', 'Partially Correct', 'Wrong', 'All'])
+        csv_data.append(['Answer Lengths'] + ['%2.2f'%(sum(v)/len(v)) for k, v in answer_dict.items()])
+    return csv_data
+
+def compute_question_type(data, round_idx):
+    # for question type
+    # What, When, Where, Why, How, How many, How much, 
+    csv_data = [['']]
+    re_list = [r'^[wW]hen|^[iIoO]n what year|^[aA]t what age|[wW]hat year|[wW]hat age|[wW]hat time', r'^[wW]ho', r'^[wW]here|In what city|In which city|In what country|In which country|[wW]hat city|[wW]hat country|[wW]hich city|[wW]hich country', r'^[wW]hy', r'^[wW]hich', r'^[wW]hat', r'^[hH]ow many|^[hH]ow much', r'^[hH]ow', r'^[dD]o|^[dD]oes|^[dD]id|[aA]re|[iI]s|[wW]as|[wW]ere|[cC]an|[cC]ould|[hH]a[sd]|[hH]ave']
+    types = ['When', 'Who', 'Where', 'Why', 'Which', 'What', 'How many | How much', 'How (others)', 'Yes/No']
+    data_list = [data]
+    for i, dataset_ in enumerate(data_list):
+        type_count = {'When':0, 'Who':0, 'Where':0, 'Why':0, 'Which':0, 'What':0, 'How many | How much':0, 'How (others)':0, 'Yes/No':0, 'Misc':0}
+        for entry in dataset_:
+            prediction = entry['pred']
+            question_text = entry["question"]
+
+            no_match = True
+            for j, r in enumerate(re_list):
+                if re.match(r, question_text) != None:
+                    # print(re.match(r, question_text), question_text)
+                    type_count[types[j]] += 1
+                    no_match = False
+                    break
+            if no_match:
+                type_count['Misc'] += 1
+                print(question_text)
+
+        print(type_count)
+        total = sum([v for k, v in type_count.items()])
+        csv_data.append([''] + [k for k, v in type_count.items()])
+        csv_data.append(['Question Type Count'] + [v for k, v in type_count.items()])        
+        csv_data.append(['Question Type (%)'] + ['%2.2f'%(100*v/float(total)) for k, v in type_count.items()])
+
+    return csv_data
+
+
+def compute_answer_lengths(data_list, pred_list, tokenizer):
+    all_lengths = []
+    answer_lengths = [{'Correct':[], 'Partially Correct':[], 'Wrong':[], 'All':[]} for _ in range(len(data_list))]
+    csv_data = [['']]
+
+    for i, dataset_ in enumerate(data_list):
+        preds = pred_list[i]
+        for index, entry in enumerate(dataset_):
+            answer = preds[str(index)]
+            if answer != "":
+                answer_tokens = tokenizer.tokenize(answer)
+                if len(answer_tokens) != 0:
+                    # assert len(answer_tokens) == (end_index - start_index + i)
+                    answer_lengths[i][entry['feedback']].append(len(answer_tokens))
+                    answer_lengths[i]['All'].append(len(answer_tokens))
+                    all_lengths.append(len(answer_tokens))
+
+    for i, dataset_ in enumerate(data_list):
+        print('==================================')
+        print('Avg Answer Length of Round %d'%i)
+        report_strings = []
+        answer_dict = answer_lengths[i]
+        for k, v in answer_dict.items():
+            report_strings.append("%s: %f"%(k, sum(v)/len(v)))
+        print(' | '.join(report_strings))
+
+    print('Avg: %2.2f'%(sum(all_lengths)/float(len(all_lengths))))
+
+
+if __name__ == '__main__':
+    answer_source = 'short'
+    round1_data = get_feedback_data('data/bandit_parallel/train/round1/train-data-parallel-round1-200-wprob-%s.jsonl.gz'%(answer_source))
+    # round1_data = get_feedback_data('data/bandit/valid/Dev-400-full-annotations.jsonl.gz')
+    tokenizer = DebertaV2TokenizerFast.from_pretrained('microsoft/deberta-v3-base', return_offsets_mapping=True)
+    round_idx = 1
+
+    csv_data = []
+    csv_data += analyze_data(round1_data, round_idx)
+
+    pred_list=None
+    csv_data += compute_question_answer_lengths(round1_data, tokenizer, round_idx, pred_list=pred_list, is_training=True)
+    csv_data += compute_question_type(round1_data, round_idx)
+    print(csv_data[2])
+    with open('out.tsv', 'w', newline='') as csvfile:
+        writer = csv.writer(csvfile, delimiter='\t')
+        writer.writerows(csv_data)
+
diff --git a/src_utils/merge_data.py b/src_utils/merge_data.py
new file mode 100644
index 0000000..b9d5f15
--- /dev/null
+++ b/src_utils/merge_data.py
@@ -0,0 +1,39 @@
+import gzip 
+import json
+import argparse
+
+def get_data(input_file):
+    with gzip.GzipFile(input_file, 'r') as reader:
+        # skip header
+        content = reader.read().decode('utf-8').strip().split('\n')
+        input_data = [json.loads(line) for line in content]
+
+    return input_data
+
+def test_same(data_1, data_2):
+    for d1, d2 in zip(data_1, data_2):
+        assert d1['question'] == d2['question']
+
+def merge(file_list, outfile):
+    data = []
+    for f_ in file_list:
+        data += get_data(f_)
+
+    fw = open(outfile + '.jsonl', 'w')
+    for l in data:
+        fw.write(json.dumps(l))
+        fw.write('\n')
+    fw.close()
+
+if __name__ == '__main__':
+    
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--outfile", default=None, type=str, required=True, help='data you wish to merge to (output)')
+    parser.add_argument('-f','--input_file', action='append', help='<Required> Set flag', required=True)
+    args = parser.parse_args()
+
+    print(args.input_file)
+    
+    filename = args.outfile
+    merge(args.input_file, filename)
diff --git a/src_utils/nq_eval.py b/src_utils/nq_eval.py
new file mode 100644
index 0000000..ad210c4
--- /dev/null
+++ b/src_utils/nq_eval.py
@@ -0,0 +1,463 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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"""Official evaluation script for Natural Questions.
+
+  https://ai.google.com/research/NaturalQuestions
+
+  ------------------------------------------------------------------------------
+
+  Example usage:
+
+  nq_eval --gold_path=<path-to-gold-files> --predictions_path=<path_to_json>
+
+  This will compute both the official F1 scores as well as recall@precision
+  tables for both long and short answers. Note that R@P are only meaningful
+  if your model populates the score fields of the prediction JSON format.
+
+  gold_path should point to the five way annotated dev data in the
+  original download format (gzipped jsonlines).
+
+  predictions_path should point to a json file containing the predictions in
+  the format given below.
+
+  ------------------------------------------------------------------------------
+
+  Prediction format:
+
+  {'predictions': [
+    {
+      'example_id': -2226525965842375672,
+      'long_answer': {
+        'start_byte': 62657, 'end_byte': 64776,
+        'start_token': 391, 'end_token': 604
+      },
+      'long_answer_score': 13.5,
+      'short_answers': [
+        {'start_byte': 64206, 'end_byte': 64280,
+         'start_token': 555, 'end_token': 560}, ...],
+      'short_answers_score': 26.4,
+      'yes_no_answer': 'NONE'
+    }, ... ]
+  }
+
+  The prediction format mirrors the annotation format in defining each long or
+  short answer span both in terms of byte offsets and token offsets. We do not
+  expect participants to supply both.
+
+  The order of preference is:
+
+    if start_byte >= 0 and end_byte >=0, use byte offsets,
+    else if start_token >= 0 and end_token >= 0, use token offsets,
+    else no span is defined (null answer).
+
+  The short answer metric takes both short answer spans, and the yes/no answer
+  into account. If the 'short_answers' list contains any non/null spans, then
+  'yes_no_answer' should be set to 'NONE'.
+
+  -----------------------------------------------------------------------------
+
+  Metrics:
+
+  If >= 2 of the annotators marked a non-null long answer, then the prediction
+  must match any one of the non-null long answers to be considered correct.
+
+  If >= 2 of the annotators marked a non-null set of short answers, or a yes/no
+  answer, then the short answers prediction must match any one of the non-null
+  sets of short answers *or* the yes/no prediction must match one of the
+  non-null yes/no answer labels.
+
+  All span comparisons are exact and each individual prediction can be fully
+  correct, or incorrect.
+
+  Each prediction should be provided with a long answer score, and a short
+  answers score. At evaluation time, the evaluation script will find a score
+  threshold at which F1 is maximized. All predictions with scores below this
+  threshold are ignored (assumed to be null). If the score is not provided,
+  the evaluation script considers all predictions to be valid. The script
+  will also output the maximum recall at precision points of >= 0.5, >= 0.75,
+  and >= 0.9.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+import json
+import os
+import pickle
+from absl import app
+from absl import flags
+from absl import logging
+import eval_utils as util
+import six
+
+flags.DEFINE_string(
+    'gold_path', None, 'Path to the gzip JSON data. For '
+    'multiple files, should be a glob '
+    'pattern (e.g. "/path/to/files-*"')
+flags.DEFINE_string('predictions_path', None, 'Path to prediction JSON.')
+flags.DEFINE_bool(
+    'cache_gold_data', False,
+    'Whether to cache gold data in Pickle format to speed up '
+    'multiple evaluations.')
+flags.DEFINE_integer('num_threads', 10, 'Number of threads for reading.')
+flags.DEFINE_bool('pretty_print', False, 'Whether to pretty print output.')
+
+FLAGS = flags.FLAGS
+
+
+def safe_divide(x, y):
+  """Compute x / y, but return 0 if y is zero."""
+  if y == 0:
+    return 0
+  else:
+    return x / y
+
+
+def score_long_answer(gold_label_list, pred_label):
+  """Scores a long answer as correct or not.
+
+  1) First decide if there is a gold long answer with LONG_NO_NULL_THRESHOLD.
+  2) The prediction will get a match if:
+     a. There is a gold long answer.
+     b. The prediction span match exactly with *one* of the non-null gold
+        long answer span.
+
+  Args:
+    gold_label_list: A list of NQLabel, could be None.
+    pred_label: A single NQLabel, could be None.
+
+  Returns:
+    gold_has_answer, pred_has_answer, is_correct, score
+  """
+  gold_has_answer = util.gold_has_long_answer(gold_label_list)
+
+  pred_has_answer = pred_label and (
+      not pred_label.long_answer_span.is_null_span())
+
+  is_correct = False
+  score = pred_label.long_score
+
+  # Both sides are non-null spans.
+  if gold_has_answer and pred_has_answer:
+    for gold_label in gold_label_list:
+      # while the voting results indicate there is an long answer, each
+      # annotator might still say there is no long answer.
+      if gold_label.long_answer_span.is_null_span():
+        continue
+
+      if util.nonnull_span_equal(gold_label.long_answer_span,
+                                 pred_label.long_answer_span):
+        is_correct = True
+        break
+
+  return gold_has_answer, pred_has_answer, is_correct, score
+
+
+def score_short_answer(gold_label_list, pred_label):
+  """Scores a short answer as correct or not.
+
+  1) First decide if there is a gold short answer with SHORT_NO_NULL_THRESHOLD.
+  2) The prediction will get a match if:
+     a. There is a gold short answer.
+     b. The prediction span *set* match exactly with *one* of the non-null gold
+        short answer span *set*.
+
+  Args:
+    gold_label_list: A list of NQLabel.
+    pred_label: A single NQLabel.
+
+  Returns:
+    gold_has_answer, pred_has_answer, is_correct, score
+  """
+
+  # There is a gold short answer if gold_label_list not empty and non null
+  # answers is over the threshold (sum over annotators).
+  gold_has_answer = util.gold_has_short_answer(gold_label_list)
+
+  # There is a pred long answer if pred_label is not empty and short answer
+  # set is not empty.
+  pred_has_answer = pred_label and (
+      (not util.is_null_span_list(pred_label.short_answer_span_list)) or
+      pred_label.yes_no_answer != 'none')
+
+  is_correct = False
+  score = pred_label.short_score
+
+  # Both sides have short answers, which contains yes/no questions.
+  if gold_has_answer and pred_has_answer:
+    if pred_label.yes_no_answer != 'none':  # System thinks its y/n questions.
+      for gold_label in gold_label_list:
+        if pred_label.yes_no_answer == gold_label.yes_no_answer:
+          is_correct = True
+          break
+    else:
+      for gold_label in gold_label_list:
+        if util.span_set_equal(gold_label.short_answer_span_list,
+                               pred_label.short_answer_span_list):
+          is_correct = True
+          break
+
+  return gold_has_answer, pred_has_answer, is_correct, score
+
+
+def score_answers(gold_annotation_dict, pred_dict):
+  """Scores all answers for all documents.
+
+  Args:
+    gold_annotation_dict: a dict from example id to list of NQLabels.
+    pred_dict: a dict from example id to list of NQLabels.
+
+  Returns:
+    long_answer_stats: List of scores for long answers.
+    short_answer_stats: List of scores for short answers.
+  """
+  gold_id_set = set(gold_annotation_dict.keys())
+  pred_id_set = set(pred_dict.keys())
+
+  if gold_id_set.symmetric_difference(pred_id_set):
+    raise ValueError('ERROR: the example ids in gold annotations and example '
+                     'ids in the prediction are not equal.')
+
+  long_answer_stats = []
+  short_answer_stats = []
+
+  for example_id in gold_id_set:
+    gold = gold_annotation_dict[example_id]
+    pred = pred_dict[example_id]
+
+    long_answer_stats.append(score_long_answer(gold, pred))
+    short_answer_stats.append(score_short_answer(gold, pred))
+
+  # use the 'score' column, which is last
+  long_answer_stats.sort(key=lambda x: x[-1], reverse=True)
+  short_answer_stats.sort(key=lambda x: x[-1], reverse=True)
+
+  return long_answer_stats, short_answer_stats
+
+
+def compute_f1(answer_stats, prefix=''):
+  """Computes F1, precision, recall for a list of answer scores.
+
+  Args:
+    answer_stats: List of per-example scores.
+    prefix (''): Prefix to prepend to score dictionary.
+
+  Returns:
+    Dictionary mapping string names to scores.
+  """
+
+  has_gold, has_pred, is_correct, _ = list(zip(*answer_stats))
+  precision = safe_divide(sum(is_correct), sum(has_pred))
+  recall = safe_divide(sum(is_correct), sum(has_gold))
+  f1 = safe_divide(2 * precision * recall, precision + recall)
+
+  return OrderedDict({
+      prefix + 'n': len(answer_stats),
+      prefix + 'f1': f1,
+      prefix + 'precision': precision,
+      prefix + 'recall': recall
+  })
+
+
+def compute_final_f1(long_answer_stats, short_answer_stats):
+  """Computes overall F1 given long and short answers, ignoring scores.
+
+  Note: this assumes that the answers have been thresholded.
+
+  Arguments:
+     long_answer_stats: List of long answer scores.
+     short_answer_stats: List of short answer scores.
+
+  Returns:
+     Dictionary of name (string) -> score.
+  """
+  scores = compute_f1(long_answer_stats, prefix='long-answer-')
+  scores.update(compute_f1(short_answer_stats, prefix='short-answer-'))
+  return scores
+
+
+def compute_pr_curves(answer_stats, targets=None):
+  """Computes PR curve and returns R@P for specific targets.
+
+  The values are computed as follows: find the (precision, recall) point
+  with maximum recall and where precision > target.
+
+  Arguments:
+    answer_stats: List of statistic tuples from the answer scores.
+    targets (None): List of precision thresholds to target.
+
+  Returns:
+    List of table with rows: [target, r, p, score].
+  """
+  total_correct = 0
+  total_has_pred = 0
+  total_has_gold = 0
+
+  # Count the number of gold annotations.
+  for has_gold, _, _, _ in answer_stats:
+    total_has_gold += has_gold
+
+  # Keep track of the point of maximum recall for each target.
+  max_recall = [0 for _ in targets]
+  max_precision = [0 for _ in targets]
+  max_scores = [None for _ in targets]
+
+  # Only keep track of unique thresholds in this dictionary.
+  scores_to_stats = OrderedDict()
+
+  # Loop through every possible threshold and compute precision + recall.
+  for has_gold, has_pred, is_correct, score in answer_stats:
+    total_correct += is_correct
+    total_has_pred += has_pred
+
+    precision = safe_divide(total_correct, total_has_pred)
+    recall = safe_divide(total_correct, total_has_gold)
+
+    # If there are any ties, this will be updated multiple times until the
+    # ties are all counted.
+    scores_to_stats[score] = [precision, recall]
+
+  best_f1 = 0.0
+  best_precision = 0.0
+  best_recall = 0.0
+  best_threshold = 0.0
+
+  for threshold, (precision, recall) in six.iteritems(scores_to_stats):
+    # Match the thresholds to the find the closest precision above some target.
+    for t, target in enumerate(targets):
+      if precision >= target and recall > max_recall[t]:
+        max_recall[t] = recall
+        max_precision[t] = precision
+        max_scores[t] = threshold
+
+    # Compute optimal threshold.
+    f1 = safe_divide(2 * precision * recall, precision + recall)
+    if f1 > best_f1:
+      best_f1 = f1
+      best_precision = precision
+      best_recall = recall
+      best_threshold = threshold
+
+  return ((best_f1, best_precision, best_recall, best_threshold),
+          list(zip(targets, max_recall, max_precision, max_scores)))
+
+
+def print_r_at_p_table(answer_stats):
+  """Pretty prints the R@P table for default targets."""
+  opt_result, pr_table = compute_pr_curves(
+      answer_stats, targets=[0.5, 0.75, 0.9])
+  f1, precision, recall, threshold = opt_result
+  print('Optimal threshold: {:.5}'.format(threshold))
+  print(' F1     /  P      /  R')
+  print('{: >7.2%} / {: >7.2%} / {: >7.2%}'.format(f1, precision, recall))
+  for target, recall, precision, row in pr_table:
+    print('R@P={}: {:.2%} (actual p={:.2%}, score threshold={:.4})'.format(
+        target, recall, precision, row))
+
+
+def get_metrics_as_dict(gold_path, prediction_path, num_threads=10):
+  """Library version of the end-to-end evaluation.
+
+  Arguments:
+    gold_path: Path to the gzip JSON data. For multiple files, should be a glob
+      pattern (e.g. "/path/to/files-*")
+    prediction_path: Path to the JSON prediction data.
+    num_threads (10): Number of threads to use when parsing multiple files.
+
+  Returns:
+    metrics: A dictionary mapping string names to metric scores.
+  """
+
+  nq_gold_dict = util.read_annotation(gold_path, n_threads=num_threads)
+  nq_pred_dict = util.read_prediction_json(prediction_path)
+  long_answer_stats, short_answer_stats = score_answers(nq_gold_dict,
+                                                        nq_pred_dict)
+
+  return get_metrics_with_answer_stats(long_answer_stats, short_answer_stats)
+
+
+def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats):
+  """Generate metrics dict using long and short answer stats."""
+
+  def _get_metric_dict(answer_stats, prefix=''):
+    """Compute all metrics for a set of answer statistics."""
+    opt_result, pr_table = compute_pr_curves(
+        answer_stats, targets=[0.5, 0.75, 0.9])
+    f1, precision, recall, threshold = opt_result
+    metrics = OrderedDict({
+        'best-threshold-f1': f1,
+        'best-threshold-precision': precision,
+        'best-threshold-recall': recall,
+        'best-threshold': threshold,
+    })
+    for target, recall, precision, _ in pr_table:
+      metrics['recall-at-precision>={:.2}'.format(target)] = recall
+      metrics['precision-at-precision>={:.2}'.format(target)] = precision
+
+    # Add prefix before returning.
+    return dict([(prefix + k, v) for k, v in six.iteritems(metrics)])
+
+  metrics = _get_metric_dict(long_answer_stats, 'long-')
+  metrics.update(_get_metric_dict(short_answer_stats, 'short-'))
+  return metrics
+
+
+def main(_):
+  cache_path = os.path.join(os.path.dirname(FLAGS.gold_path), 'cache')
+  if FLAGS.cache_gold_data and os.path.exists(cache_path):
+    logging.info('Reading from cache: %s', format(cache_path))
+    nq_gold_dict = pickle.load(open(cache_path, 'r'))
+  else:
+    nq_gold_dict = util.read_annotation(
+        FLAGS.gold_path, n_threads=FLAGS.num_threads)
+    if FLAGS.cache_gold_data:
+      logging.info('Caching gold data for next time to: %s', format(cache_path))
+      pickle.dump(nq_gold_dict, open(cache_path, 'w'))
+
+  nq_pred_dict = util.read_prediction_json(FLAGS.predictions_path)
+
+  long_answer_stats, short_answer_stats = score_answers(nq_gold_dict,
+                                                        nq_pred_dict)
+
+  if FLAGS.pretty_print:
+    print('*' * 20)
+    print('LONG ANSWER R@P TABLE:')
+    print_r_at_p_table(long_answer_stats)
+    print('*' * 20)
+    print('SHORT ANSWER R@P TABLE:')
+    print_r_at_p_table(short_answer_stats)
+
+    scores = compute_final_f1(long_answer_stats, short_answer_stats)
+    print('*' * 20)
+    print('METRICS IGNORING SCORES (n={}):'.format(scores['long-answer-n']))
+    print('              F1     /  P      /  R')
+    print('Long answer  {: >7.2%} / {: >7.2%} / {: >7.2%}'.format(
+        scores['long-answer-f1'], scores['long-answer-precision'],
+        scores['long-answer-recall']))
+    print('Short answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format(
+        scores['short-answer-f1'], scores['short-answer-precision'],
+        scores['short-answer-recall']))
+  else:
+    metrics = get_metrics_with_answer_stats(long_answer_stats,
+                                            short_answer_stats)
+    print(json.dumps(metrics))
+
+
+if __name__ == '__main__':
+  flags.mark_flag_as_required('gold_path')
+  flags.mark_flag_as_required('predictions_path')
+  app.run(main)
\ No newline at end of file
diff --git a/src_utils/util_mrqa_official_eval.py b/src_utils/util_mrqa_official_eval.py
new file mode 100644
index 0000000..f894d50
--- /dev/null
+++ b/src_utils/util_mrqa_official_eval.py
@@ -0,0 +1,143 @@
+# Copyright (c) 2019, Facebook, Inc. and its affiliates. All Rights Reserved
+"""Official evaluation script for the MRQA Workshop Shared Task.
+Adapted fromt the SQuAD v1.1 official evaluation script.
+Usage:
+    python official_eval.py dataset_file.jsonl.gz prediction_file.json
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from pathlib import Path
+from urllib.parse import urlparse
+import argparse
+import string
+import re
+import json
+import gzip
+import sys
+import os
+from collections import Counter
+
+
+def cached_path(url_or_filename, cache_dir=None):
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    """
+    if cache_dir is None:
+        cache_dir = os.path.dirname(url_or_filename)
+    if isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+
+    url_or_filename = os.path.expanduser(url_or_filename)
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
+        # URL, so get it from the cache (downloading if necessary)
+        return get_from_cache(url_or_filename, cache_dir)
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        return url_or_filename
+    elif parsed.scheme == '':
+        # File, but it doesn't exist.
+        raise FileNotFoundError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def read_predictions(prediction_file):
+    with open(prediction_file) as f:
+        predictions = json.load(f)
+    return predictions
+
+
+def read_answers(gold_file):
+    answers = {}
+    with gzip.open(gold_file, 'rb') as f:
+        for i, line in enumerate(f):
+            example = json.loads(line)
+            if i == 0 and 'header' in example:
+                continue
+            for qa in example['qas']:
+                answers[qa['qid']] = qa['answers']
+    return answers
+
+
+def evaluate(answers, predictions, skip_no_answer=False):
+    f1 = exact_match = total = 0
+    for qid, ground_truths in answers.items():
+        if qid not in predictions:
+            if not skip_no_answer:
+                message = 'Unanswered question %s will receive score 0.' % qid
+                print(message)
+                total += 1
+            continue
+        total += 1
+        prediction = predictions[qid]
+        exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
+        f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+
+    return {'exact_match': exact_match, 'f1': f1}
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Evaluation for MRQA Workshop Shared Task')
+    parser.add_argument('dataset_file', type=str, help='Dataset File')
+    parser.add_argument('prediction_file', type=str, help='Prediction File')
+    parser.add_argument('--skip-no-answer', action='store_true')
+    args = parser.parse_args()
+
+    answers = read_answers(cached_path(args.dataset_file))
+    predictions = read_predictions(cached_path(args.prediction_file))
+    metrics = evaluate(answers, predictions, args.skip_no_answer)
+
+    print(json.dumps(metrics))
diff --git a/src_utils/util_squad_eval.py b/src_utils/util_squad_eval.py
new file mode 100644
index 0000000..059d404
--- /dev/null
+++ b/src_utils/util_squad_eval.py
@@ -0,0 +1,42 @@
+import re
+import collections
+import string
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+        return re.sub(regex, ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def get_tokens(s):
+    if not s:
+        return []
+    return normalize_answer(s).split()
+
+
+def compute_f1(a_gold, a_pred):
+    gold_toks = get_tokens(a_gold)
+    pred_toks = get_tokens(a_pred)
+    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+    num_same = sum(common.values())
+    if len(gold_toks) == 0 or len(pred_toks) == 0:
+        return int(gold_toks == pred_toks)
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
diff --git a/src_utils/utils.py b/src_utils/utils.py
new file mode 100644
index 0000000..dd92c6e
--- /dev/null
+++ b/src_utils/utils.py
@@ -0,0 +1,230 @@
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import collections
+import json
+import logging
+import math
+import os
+import random
+import time
+import re
+import string
+from io import open
+import gzip
+import datetime
+import csv
+
+from IPython import embed
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from transformers import BertTokenizer, DebertaTokenizer, DebertaTokenizerFast
+from transformers import AdamW
+from model import BertForQuestionAnswering
+from transformers import get_scheduler
+from datasets import load_dataset
+
+from pytorch_pretrained_bert.tokenization import BasicTokenizer  # used in evaluation
+from pytorch_pretrained_bert.tokenization import whitespace_tokenize
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+def make_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length,
+                    do_lower_case , verbose_logging, version_2_with_negative):
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+    _PrelimPrediction = collections.namedtuple(
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+        prelim_predictions = []
+        score_null = 1000000
+        min_null_feature_index = 0
+        null_start_logit = 0
+        null_end_logit = 0
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            if version_2_with_negative:
+                feature_null_score = result.start_logits[0] + result.end_logits[0]
+                if feature_null_score < score_null:
+                    score_null = feature_null_score
+                    min_null_feature_index = feature_index
+                    null_start_logit = result.start_logits[0]
+                    null_end_logit = result.end_logits[0]
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+                    # if start_index not in feature.token_to_orig_map:
+                    #     continue
+                    # if end_index not in feature.token_to_orig_map:
+                    #     continue
+                    if not feature.token_is_max_context.get(start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(feature_index=feature_index,
+                                          start_index=start_index,
+                                          end_index=end_index,
+                                          start_logit=result.start_logits[start_index],
+                                          end_logit=result.end_logits[end_index]))
+        if version_2_with_negative:
+            prelim_predictions.append(
+                _PrelimPrediction(feature_index=min_null_feature_index,
+                                  start_index=0,
+                                  end_index=0,
+                                  start_logit=null_start_logit,
+                                  end_logit=null_end_logit))
+        prelim_predictions = sorted(prelim_predictions,
+                                    key=lambda x: (x.start_logit + x.end_logit),
+                                    reverse=True)
+
+
+        _NbestPrediction = collections.namedtuple("NbestPrediction",
+                                                  ["text", "start_logit", "end_logit", "start_index", "end_index"])
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+
+            feature = features[pred.feature_index]
+            if pred.start_index > 0:
+                try:
+                    orig_doc_start_char = feature.token_to_orig_map[pred.start_index-feature.doc_token_offset][0]
+                except IndexError:
+                    print('start index', pred.start_index)
+                    print('doc_token_offset', feature.doc_token_offset)
+                    print('feature map', feature.token_to_orig_map)
+                    print('len feature map', len(feature.token_to_orig_map))
+                    print('token_is_max_context', feature.token_is_max_context)
+                    exit(0)
+
+                # getting the end index of the answer span (in character)
+                if pred.end_index-feature.doc_token_offset >= len(feature.token_to_orig_map):
+                    # if the end index is in the [PAD] area, then make it the last token in context
+                    orig_doc_end_char = feature.token_to_orig_map[-1][1]
+                else:
+                    orig_doc_end_char = feature.token_to_orig_map[pred.end_index-feature.doc_token_offset][1]
+
+                ans_text = example.paragraph_text[orig_doc_start_char:orig_doc_end_char]
+
+                ans_text = ans_text.replace(" ##", "")
+                ans_text = ans_text.replace("##", "")
+                ans_text = ans_text.strip()
+                final_text = ans_text
+
+                if final_text in seen_predictions:
+                    continue
+                seen_predictions[final_text] = True
+            else:
+                final_text = ""
+                seen_predictions[final_text] = True
+
+            nbest.append(
+                _NbestPrediction(text=final_text,
+                                 start_logit=pred.start_logit,
+                                 end_logit=pred.end_logit,
+                                 start_index=pred.start_index,
+                                 end_index=pred.end_index))
+
+        if version_2_with_negative:
+            if "" not in seen_predictions:
+                nbest.append(
+                    _NbestPrediction(text="",
+                                     start_logit=null_start_logit,
+                                     end_logit=null_end_logit,
+                                     start_index=pred.start_index,
+                                     end_index=pred.end_index))
+            if len(nbest) == 1:
+                nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, start_index=pred.start_index, end_index=pred.end_index))
+
+        if not nbest:
+            nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, start_index=pred.start_index, end_index=pred.end_index))
+        assert len(nbest) >= 1
+
+        total_scores = []
+        best_non_null_entry = None
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+            if not best_non_null_entry:
+                if entry.text:
+                    best_non_null_entry = entry
+
+        probs = _compute_softmax(total_scores)
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+        if not version_2_with_negative:
+            all_predictions[example.qas_id] = nbest_json[0]["text"]
+        else:
+            score_diff = score_null - best_non_null_entry.start_logit - (
+                best_non_null_entry.end_logit)
+            scores_diff_json[example.qas_id] = score_diff
+            # all_predictions[example.qas_id] = best_non_null_entry.text
+            all_predictions[example.qas_id] = (best_non_null_entry.text, best_non_null_entry.start_index, best_non_null_entry.end_index)
+        all_nbest_json[example.qas_id] = nbest_json
+
+    return all_predictions, all_nbest_json, scores_diff_json