440 lines
18 KiB
Python
440 lines
18 KiB
Python
from tqdm import tqdm
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import collections
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import logging
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import os
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import json
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from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
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from .utils import DataProcessor, InputExample, InputFeatures
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from ...file_utils import is_tf_available
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if is_tf_available():
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import tensorflow as tf
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logger = logging.getLogger(__name__)
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def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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doc_stride, max_query_length, is_training,
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cls_token_at_end=False,
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cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
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sequence_a_segment_id=0, sequence_b_segment_id=1,
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cls_token_segment_id=0, pad_token_segment_id=0,
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mask_padding_with_zero=True,
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sequence_a_is_doc=False):
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"""Loads a data file into a list of `InputBatch`s."""
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# Defining helper methods
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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unique_id = 1000000000
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features = []
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for (example_index, example) in enumerate(tqdm(examples)):
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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tok_to_orig_index = []
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orig_to_tok_index = []
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all_doc_tokens = []
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for (i, token) in enumerate(example.doc_tokens):
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orig_to_tok_index.append(len(all_doc_tokens))
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sub_tokens = tokenizer.tokenize(token)
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for sub_token in sub_tokens:
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tok_to_orig_index.append(i)
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all_doc_tokens.append(sub_token)
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tok_start_position = None
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tok_end_position = None
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if is_training and example.is_impossible:
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tok_start_position = -1
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tok_end_position = -1
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if is_training and not example.is_impossible:
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tok_start_position = orig_to_tok_index[example.start_position]
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if example.end_position < len(example.doc_tokens) - 1:
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tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
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else:
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tok_end_position = len(all_doc_tokens) - 1
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(tok_start_position, tok_end_position) = _improve_answer_span(
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all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
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example.orig_answer_text)
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# The -3 accounts for [CLS], [SEP] and [SEP]
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max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
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# We can have documents that are longer than the maximum sequence length.
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# To deal with this we do a sliding window approach, where we take chunks
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# of the up to our max length with a stride of `doc_stride`.
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_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
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"DocSpan", ["start", "length"])
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doc_spans = []
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start_offset = 0
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while start_offset < len(all_doc_tokens):
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length = len(all_doc_tokens) - start_offset
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if length > max_tokens_for_doc:
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length = max_tokens_for_doc
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doc_spans.append(_DocSpan(start=start_offset, length=length))
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if start_offset + length == len(all_doc_tokens):
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break
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start_offset += min(length, doc_stride)
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for (doc_span_index, doc_span) in enumerate(doc_spans):
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tokens = []
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token_to_orig_map = {}
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token_is_max_context = {}
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segment_ids = []
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# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
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# Original TF implem also keep the classification token (set to 0) (not sure why...)
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p_mask = []
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# CLS token at the beginning
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if not cls_token_at_end:
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tokens.append(cls_token)
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segment_ids.append(cls_token_segment_id)
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p_mask.append(0)
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cls_index = 0
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# XLNet: P SEP Q SEP CLS
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# Others: CLS Q SEP P SEP
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if not sequence_a_is_doc:
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# Query
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tokens += query_tokens
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segment_ids += [sequence_a_segment_id] * len(query_tokens)
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p_mask += [1] * len(query_tokens)
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(1)
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# Paragraph
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for i in range(doc_span.length):
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split_token_index = doc_span.start + i
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token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
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is_max_context = _check_is_max_context(doc_spans, doc_span_index,
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split_token_index)
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token_is_max_context[len(tokens)] = is_max_context
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tokens.append(all_doc_tokens[split_token_index])
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if not sequence_a_is_doc:
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segment_ids.append(sequence_b_segment_id)
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else:
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(0)
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paragraph_len = doc_span.length
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if sequence_a_is_doc:
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(1)
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tokens += query_tokens
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segment_ids += [sequence_b_segment_id] * len(query_tokens)
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p_mask += [1] * len(query_tokens)
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_b_segment_id)
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p_mask.append(1)
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# CLS token at the end
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if cls_token_at_end:
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tokens.append(cls_token)
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segment_ids.append(cls_token_segment_id)
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p_mask.append(0)
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cls_index = len(tokens) - 1 # Index of classification token
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(pad_token)
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input_mask.append(0 if mask_padding_with_zero else 1)
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segment_ids.append(pad_token_segment_id)
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p_mask.append(1)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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span_is_impossible = example.is_impossible
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start_position = None
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end_position = None
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if is_training and not span_is_impossible:
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# For training, if our document chunk does not contain an annotation
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# we throw it out, since there is nothing to predict.
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doc_start = doc_span.start
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doc_end = doc_span.start + doc_span.length - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and
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tok_end_position <= doc_end):
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out_of_span = True
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if out_of_span:
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start_position = 0
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end_position = 0
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span_is_impossible = True
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else:
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if sequence_a_is_doc:
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doc_offset = 0
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else:
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doc_offset = len(query_tokens) + 2
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start_position = tok_start_position - doc_start + doc_offset
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end_position = tok_end_position - doc_start + doc_offset
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if is_training and span_is_impossible:
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start_position = cls_index
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end_position = cls_index
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if example_index < 20:
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logger.info("*** Example ***")
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logger.info("unique_id: %s" % (unique_id))
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logger.info("example_index: %s" % (example_index))
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logger.info("doc_span_index: %s" % (doc_span_index))
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logger.info("tokens: %s" % " ".join(tokens))
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logger.info("token_to_orig_map: %s" % " ".join([
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"%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
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logger.info("token_is_max_context: %s" % " ".join([
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"%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
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]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info(
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"input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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if is_training and span_is_impossible:
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logger.info("impossible example")
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if is_training and not span_is_impossible:
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answer_text = " ".join(tokens[start_position:(end_position + 1)])
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logger.info("start_position: %d" % (start_position))
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logger.info("end_position: %d" % (end_position))
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logger.info(
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"answer: %s" % (answer_text))
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features.append(
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SquadFeatures(
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unique_id=unique_id,
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example_index=example_index,
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doc_span_index=doc_span_index,
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tokens=tokens,
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token_to_orig_map=token_to_orig_map,
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token_is_max_context=token_is_max_context,
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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cls_index=cls_index,
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p_mask=p_mask,
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paragraph_len=paragraph_len,
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start_position=start_position,
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end_position=end_position,
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is_impossible=span_is_impossible))
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unique_id += 1
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return features
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def read_squad_examples(input_file, is_training, version_2_with_negative):
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"""Read a SQuAD json file into a list of SquadExample."""
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with open(input_file, "r", encoding='utf-8') as reader:
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input_data = json.load(reader)["data"]
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def is_whitespace(c):
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if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
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return True
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return False
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examples = []
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for entry in input_data:
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for paragraph in entry["paragraphs"]:
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paragraph_text = paragraph["context"]
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doc_tokens = []
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char_to_word_offset = []
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prev_is_whitespace = True
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for c in paragraph_text:
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if is_whitespace(c):
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prev_is_whitespace = True
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else:
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if prev_is_whitespace:
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doc_tokens.append(c)
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else:
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doc_tokens[-1] += c
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prev_is_whitespace = False
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char_to_word_offset.append(len(doc_tokens) - 1)
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question_text = qa["question"]
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start_position = None
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end_position = None
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orig_answer_text = None
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is_impossible = False
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if is_training:
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if version_2_with_negative:
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is_impossible = qa["is_impossible"]
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if (len(qa["answers"]) != 1) and (not is_impossible):
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raise ValueError(
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"For training, each question should have exactly 1 answer.")
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if not is_impossible:
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answer = qa["answers"][0]
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orig_answer_text = answer["text"]
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answer_offset = answer["answer_start"]
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answer_length = len(orig_answer_text)
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start_position = char_to_word_offset[answer_offset]
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end_position = char_to_word_offset[answer_offset + answer_length - 1]
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# Only add answers where the text can be exactly recovered from the
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# document. If this CAN'T happen it's likely due to weird Unicode
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# stuff so we will just skip the example.
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#
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# Note that this means for training mode, every example is NOT
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# guaranteed to be preserved.
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actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
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cleaned_answer_text = " ".join(
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whitespace_tokenize(orig_answer_text))
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if actual_text.find(cleaned_answer_text) == -1:
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logger.warning("Could not find answer: '%s' vs. '%s'",
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actual_text, cleaned_answer_text)
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continue
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else:
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start_position = -1
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end_position = -1
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orig_answer_text = ""
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example = SquadExample(
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qas_id=qas_id,
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question_text=question_text,
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doc_tokens=doc_tokens,
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orig_answer_text=orig_answer_text,
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start_position=start_position,
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end_position=end_position,
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is_impossible=is_impossible)
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examples.append(example)
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return examples
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class SquadExample(object):
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"""
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A single training/test example for the Squad dataset.
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For examples without an answer, the start and end position are -1.
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"""
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def __init__(self,
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qas_id,
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question_text,
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doc_tokens,
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orig_answer_text=None,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.qas_id = qas_id
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self.question_text = question_text
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self.doc_tokens = doc_tokens
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self.orig_answer_text = orig_answer_text
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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s = ""
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s += "qas_id: %s" % (self.qas_id)
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s += ", question_text: %s" % (
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self.question_text)
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s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
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if self.start_position:
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s += ", start_position: %d" % (self.start_position)
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if self.end_position:
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s += ", end_position: %d" % (self.end_position)
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if self.is_impossible:
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s += ", is_impossible: %r" % (self.is_impossible)
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return s
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class SquadFeatures(object):
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"""A single set of features of data."""
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def __init__(self,
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unique_id,
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example_index,
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doc_span_index,
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tokens,
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token_to_orig_map,
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token_is_max_context,
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input_ids,
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input_mask,
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segment_ids,
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cls_index,
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p_mask,
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paragraph_len,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.unique_id = unique_id
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self.example_index = example_index
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self.doc_span_index = doc_span_index
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self.tokens = tokens
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self.token_to_orig_map = token_to_orig_map
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self.token_is_max_context = token_is_max_context
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.cls_index = cls_index
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self.p_mask = p_mask
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self.paragraph_len = paragraph_len
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def __eq__(self, other):
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return self.cls_index == other.cls_index and \
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self.doc_span_index == other.doc_span_index and \
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self.end_position == other.end_position and \
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self.example_index == other.example_index and \
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self.input_ids == other.input_ids and \
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self.input_mask == other.input_mask and \
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self.is_impossible == other.is_impossible and \
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self.p_mask == other.p_mask and \
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self.paragraph_len == other.paragraph_len and \
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self.segment_ids == other.segment_ids and \
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self.start_position == other.start_position and \
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self.token_is_max_context == other.token_is_max_context and \
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self.token_to_orig_map == other.token_to_orig_map and \
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self.tokens == other.tokens and \
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self.unique_id == other.unique_id |