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Commit a5a8a617 authored by LysandreJik's avatar LysandreJik
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Works for BERT

parent a7dafe2f
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Showing with 433 additions and 76 deletions
transformers/data/processors/squad.py
View file @ a5a8a617
...@@ -3,6 +3,7 @@ import collections ...@@ -3,6 +3,7 @@ import collections
import logging import logging
import os import os
import json import json
import numpy as np
from ...tokenization_bert import BasicTokenizer, whitespace_tokenize from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
from .utils import DataProcessor, InputExample, InputFeatures from .utils import DataProcessor, InputExample, InputFeatures
...@@ -13,19 +14,7 @@ if is_tf_available(): ...@@ -13,19 +14,7 @@ if is_tf_available():
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
cls_token_at_end=False,
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
sequence_a_segment_id=0, sequence_b_segment_id=1,
cls_token_segment_id=0, pad_token_segment_id=0,
mask_padding_with_zero=True,
sequence_a_is_doc=False):
"""Loads a data file into a list of `InputBatch`s."""
# Defining helper methods
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text): orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer.""" """Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
...@@ -37,7 +26,8 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -37,7 +26,8 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
return (new_start, new_end) return (new_start, new_end)
return (input_start, input_end) return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token.""" """Check if this is the 'max context' doc span for the token."""
best_score = None best_score = None
best_span_index = None best_span_index = None
...@@ -56,25 +46,221 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -56,25 +46,221 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
return cur_span_index == best_span_index return cur_span_index == best_span_index
def _new_check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# if len(doc_spans) == 1:
# return True
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span["start"] + doc_span["length"] - 1
if position < doc_span["start"]:
continue
if position > end:
continue
num_left_context = position - doc_span["start"]
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
cls_token_at_end=True,
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
sequence_a_segment_id=0, sequence_b_segment_id=1,
cls_token_segment_id=0, pad_token_segment_id=0,
mask_padding_with_zero=True,
sequence_a_is_doc=False):
"""Loads a data file into a list of `InputBatch`s."""
# Defining helper methods
unique_id = 1000000000 unique_id = 1000000000
features = [] features = []
new_features = []
for (example_index, example) in enumerate(tqdm(examples)): for (example_index, example) in enumerate(tqdm(examples)):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length: doc_tokens = []
query_tokens = query_tokens[0:max_query_length] char_to_word_offset = []
prev_is_whitespace = True
# Split on whitespace so that different tokens may be attributed to their original position.
for c in example.context_text:
if _is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
if is_training:
# Get start and end position
answer_length = len(example.answer_text)
start_position = char_to_word_offset[example.start_position]
end_position = char_to_word_offset[example.start_position + answer_length - 1]
# If the answer cannot be found in the text, then skip this example.
actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
continue
tok_to_orig_index = [] tok_to_orig_index = []
orig_to_tok_index = [] orig_to_tok_index = []
all_doc_tokens = [] all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens): for (i, token) in enumerate(doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens)) orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token) sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens: for sub_token in sub_tokens:
tok_to_orig_index.append(i) tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token) all_doc_tokens.append(sub_token)
spans = []
truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence
sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
encoded_dict = tokenizer.encode_plus(
truncated_query,
all_doc_tokens,
max_length=max_seq_length,
padding_strategy='right',
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
return_overflowing_tokens=True,
truncation_strategy='only_second'
)
ids = encoded_dict['input_ids']
print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
non_padded_ids = ids[:ids.index(tokenizer.pad_token_id)] if tokenizer.pad_token_id in ids else ids
paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[0 + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = 0
encoded_dict["length"] = paragraph_len
spans.append(encoded_dict)
print("YESSIR", len(spans) * doc_stride < len(all_doc_tokens), "overflowing_tokens" in encoded_dict)
while len(spans) * doc_stride < len(all_doc_tokens) and "overflowing_tokens" in encoded_dict:
overflowing_tokens = encoded_dict['overflowing_tokens']
print("OVERFLOW", len(overflowing_tokens))
encoded_dict = tokenizer.encode_plus(
truncated_query,
overflowing_tokens,
max_length=max_seq_length,
return_overflowing_tokens=True,
padding_strategy='right',
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
truncation_strategy='only_second'
)
ids = encoded_dict['input_ids']
print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
# Length of the document without the query
paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[len(spans) * doc_stride + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = len(spans) * doc_stride
encoded_dict["length"] = paragraph_len
# split_token_index = doc_span.start + i
# token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
# is_max_context = _check_is_max_context(doc_spans, doc_span_index,
# split_token_index)
# token_is_max_context[len(tokens)] = is_max_context
# tokens.append(all_doc_tokens[split_token_index])
spans.append(encoded_dict)
for doc_span_index in range(len(spans)):
for j in range(spans[doc_span_index]["paragraph_len"]):
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
index = spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
print("new span", len(spans))
for span in spans:
# Identify the position of the CLS token
cls_index = span['input_ids'].index(tokenizer.cls_token_id)
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implem also keep the classification token (set to 0) (not sure why...)
p_mask = np.array(span['token_type_ids'])
# Convert all SEP indices to '0' before inversion
p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 0
# Limit positive values to one
p_mask = 1 - np.minimum(p_mask, 1)
# Set the CLS index to '0'
p_mask[cls_index] = 0
print("new features length", len(new_features))
new_features.append(NewSquadFeatures(
span['input_ids'],
span['attention_mask'],
span['token_type_ids'],
cls_index,
p_mask.tolist(),
example_index=example_index,
unique_id=unique_id,
paragraph_len=span['paragraph_len'],
token_is_max_context=span["token_is_max_context"],
tokens=span["tokens"],
token_to_orig_map=span["token_to_orig_map"]
))
unique_id += 1
# tokenize ...
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_start_position = None tok_start_position = None
tok_end_position = None tok_end_position = None
if is_training and example.is_impossible: if is_training and example.is_impossible:
...@@ -82,7 +268,7 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -82,7 +268,7 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
tok_end_position = -1 tok_end_position = -1
if is_training and not example.is_impossible: if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position] tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1: if example.end_position < len(doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else: else:
tok_end_position = len(all_doc_tokens) - 1 tok_end_position = len(all_doc_tokens) - 1
...@@ -101,14 +287,19 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -101,14 +287,19 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_spans = [] doc_spans = []
start_offset = 0 start_offset = 0
while start_offset < len(all_doc_tokens): while start_offset < len(all_doc_tokens):
print("OLD DOC CREATION BEGIN", start_offset, len(all_doc_tokens))
length = len(all_doc_tokens) - start_offset length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc: if length > max_tokens_for_doc:
length = max_tokens_for_doc length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length)) doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens): if start_offset + length == len(all_doc_tokens):
print("Done with this doc span, breaking out.", start_offset, length)
break break
print("CHOOSING OFFSET", length, doc_stride)
start_offset += min(length, doc_stride) start_offset += min(length, doc_stride)
print("OLD DOC CREATION END", start_offset)
print("old span", len(doc_spans))
for (doc_span_index, doc_span) in enumerate(doc_spans): for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = [] tokens = []
token_to_orig_map = {} token_to_orig_map = {}
...@@ -183,18 +374,20 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -183,18 +374,20 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
# tokens are attended to. # tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length. # Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length: while len(input_ids) < max_seq_length:
input_ids.append(pad_token) input_ids.append(pad_token)
input_mask.append(0 if mask_padding_with_zero else 1) input_mask.append(0 if mask_padding_with_zero else 1)
segment_ids.append(pad_token_segment_id) segment_ids.append(pad_token_segment_id)
p_mask.append(1) p_mask.append(1)
print("[OLD] Ids computed; position of the first padding", input_ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in input_ids else None)
assert len(input_ids) == max_seq_length assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length assert len(segment_ids) == max_seq_length
span_is_impossible = example.is_impossible span_is_impossible = example.is_impossible if hasattr(example, "is_impossible") else False
start_position = None start_position = None
end_position = None end_position = None
if is_training and not span_is_impossible: if is_training and not span_is_impossible:
...@@ -222,31 +415,32 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -222,31 +415,32 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
start_position = cls_index start_position = cls_index
end_position = cls_index end_position = cls_index
if example_index < 20: # if example_index < 20:
logger.info("*** Example ***") # logger.info("*** Example ***")
logger.info("unique_id: %s" % (unique_id)) # logger.info("unique_id: %s" % (unique_id))
logger.info("example_index: %s" % (example_index)) # logger.info("example_index: %s" % (example_index))
logger.info("doc_span_index: %s" % (doc_span_index)) # logger.info("doc_span_index: %s" % (doc_span_index))
logger.info("tokens: %s" % " ".join(tokens)) # logger.info("tokens: %s" % str(tokens))
logger.info("token_to_orig_map: %s" % " ".join([ # logger.info("token_to_orig_map: %s" % " ".join([
"%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()])) # "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
logger.info("token_is_max_context: %s" % " ".join([ # logger.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in token_is_max_context.items() # "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
])) # ]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) # logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info( # logger.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask])) # "input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info( # logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids])) # "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and span_is_impossible: # if is_training and span_is_impossible:
logger.info("impossible example") # logger.info("impossible example")
if is_training and not span_is_impossible: # if is_training and not span_is_impossible:
answer_text = " ".join(tokens[start_position:(end_position + 1)]) # answer_text = " ".join(tokens[start_position:(end_position + 1)])
logger.info("start_position: %d" % (start_position)) # logger.info("start_position: %d" % (start_position))
logger.info("end_position: %d" % (end_position)) # logger.info("end_position: %d" % (end_position))
logger.info( # logger.info(
"answer: %s" % (answer_text)) # "answer: %s" % (answer_text))
print("features length", len(features))
features.append( features.append(
SquadFeatures( SquadFeatures(
unique_id=unique_id, unique_id=unique_id,
...@@ -266,7 +460,48 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -266,7 +460,48 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
is_impossible=span_is_impossible)) is_impossible=span_is_impossible))
unique_id += 1 unique_id += 1
return features assert len(features) == len(new_features)
assert len(features) == len(new_features)
for i in range(len(features)):
print(i)
feature, new_feature = features[i], new_features[i]
input_ids = feature.input_ids
input_mask = feature.input_mask
segment_ids = feature.segment_ids
cls_index = feature.cls_index
p_mask = feature.p_mask
example_index = feature.example_index
paragraph_len = feature.paragraph_len
token_is_max_context = feature.token_is_max_context
tokens = feature.tokens
token_to_orig_map = feature.token_to_orig_map
new_input_ids = new_feature.input_ids
new_input_mask = new_feature.attention_mask
new_segment_ids = new_feature.token_type_ids
new_cls_index = new_feature.cls_index
new_p_mask = new_feature.p_mask
new_example_index = new_feature.example_index
new_paragraph_len = new_feature.paragraph_len
new_token_is_max_context = new_feature.token_is_max_context
new_tokens = new_feature.tokens
new_token_to_orig_map = new_feature.token_to_orig_map
assert input_ids == new_input_ids
assert input_mask == new_input_mask
assert segment_ids == new_segment_ids
assert cls_index == new_cls_index
assert p_mask == new_p_mask
assert example_index == new_example_index
assert paragraph_len == new_paragraph_len
assert token_is_max_context == new_token_is_max_context
assert tokens == new_tokens
assert token_to_orig_map == new_token_to_orig_map
return new_features
def read_squad_examples(input_file, is_training, version_2_with_negative): def read_squad_examples(input_file, is_training, version_2_with_negative):
...@@ -347,6 +582,124 @@ def read_squad_examples(input_file, is_training, version_2_with_negative): ...@@ -347,6 +582,124 @@ def read_squad_examples(input_file, is_training, version_2_with_negative):
return examples return examples
class SquadV1Processor(DataProcessor):
"""Processor for the SQuAD data set."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return NewSquadExample(
tensor_dict['id'].numpy(),
tensor_dict['question'].numpy().decode('utf-8'),
tensor_dict['context'].numpy().decode('utf-8'),
tensor_dict['answers']['text'].numpy().decode('utf-8'),
tensor_dict['answers']['answers_start'].numpy().decode('utf-8'),
tensor_dict['title'].numpy().decode('utf-8')
)
def get_train_examples(self, data_dir):
"""See base class."""
with open(os.path.join(data_dir, "train-v1.1.json"), "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "train")
def get_dev_examples(self, data_dir):
"""See base class."""
with open(os.path.join(data_dir, "dev-v1.1.json"), "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, input_data, set_type):
"""Creates examples for the training and dev sets."""
is_training = set_type == "train"
examples = []
for entry in input_data:
title = entry['title']
for paragraph in entry["paragraphs"]:
context_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
answer_text = None
if is_training:
if (len(qa["answers"]) != 1):
raise ValueError(
"For training, each question should have exactly 1 answer.")
answer = qa["answers"][0]
answer_text = answer['text']
start_position = answer['answer_start']
example = NewSquadExample(
qas_id=qas_id,
question_text=question_text,
context_text=context_text,
answer_text=answer_text,
start_position=start_position,
title=title
)
examples.append(example)
return examples
class NewSquadExample(object):
"""
A single training/test example for the Squad dataset, as loaded from disk.
"""
def __init__(self,
qas_id,
question_text,
context_text,
answer_text,
start_position,
title):
self.qas_id = qas_id
self.question_text = question_text
self.context_text = context_text
self.answer_text = answer_text
self.start_position = start_position
self.title = title
class NewSquadFeatures(object):
"""
Single squad example features to be fed to a model.
Those features are model-specific.
"""
def __init__(self,
input_ids,
attention_mask,
token_type_ids,
cls_index,
p_mask,
example_index,
unique_id,
paragraph_len,
token_is_max_context,
tokens,
token_to_orig_map
):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.cls_index = cls_index
self.p_mask = p_mask
self.example_index = example_index
self.unique_id = unique_id
self.paragraph_len = paragraph_len
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
class SquadExample(object): class SquadExample(object):
""" """
A single training/test example for the Squad dataset. A single training/test example for the Squad dataset.
...@@ -423,18 +776,22 @@ class SquadFeatures(object): ...@@ -423,18 +776,22 @@ class SquadFeatures(object):
self.is_impossible = is_impossible self.is_impossible = is_impossible
def __eq__(self, other): def __eq__(self, other):
return self.cls_index == other.cls_index and \ print(self.example_index == other.example_index)
self.doc_span_index == other.doc_span_index and \ print(self.input_ids == other.input_ids)
self.end_position == other.end_position and \ print(self.input_mask == other.attention_mask)
self.example_index == other.example_index and \ print(self.p_mask == other.p_mask)
print(self.paragraph_len == other.paragraph_len)
print(self.segment_ids == other.token_type_ids)
print(self.token_is_max_context == other.token_is_max_context)
print(self.token_to_orig_map == other.token_to_orig_map)
print(self.tokens == other.tokens)
return self.example_index == other.example_index and \
self.input_ids == other.input_ids and \ self.input_ids == other.input_ids and \
self.input_mask == other.input_mask and \ self.input_mask == other.attention_mask and \
self.is_impossible == other.is_impossible and \
self.p_mask == other.p_mask and \ self.p_mask == other.p_mask and \
self.paragraph_len == other.paragraph_len and \ self.paragraph_len == other.paragraph_len and \
self.segment_ids == other.segment_ids and \ self.segment_ids == other.token_type_ids and \
self.start_position == other.start_position and \
self.token_is_max_context == other.token_is_max_context and \ self.token_is_max_context == other.token_is_max_context and \
self.token_to_orig_map == other.token_to_orig_map and \ self.token_to_orig_map == other.token_to_orig_map and \
self.tokens == other.tokens and \ self.tokens == other.tokens
self.unique_id == other.unique_id \ No newline at end of file
\ No newline at end of file
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