official/nlp/data/squad_lib.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Library to process data for SQuAD 1.1 and SQuAD 2.0."""
# pylint: disable=g-bad-import-order
import collections
import copy
import json
import math
import os
import six
from absl import logging
import tensorflow as tf, tf_keras
from official.nlp.tools import tokenization
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
Attributes:
qas_id: ID of the question-answer pair.
question_text: Original text for the question.
doc_tokens: The list of tokens in the context obtained by splitting on
whitespace only.
orig_answer_text: Original text for the answer.
start_position: Starting index of the answer in `doc_tokens`.
end_position: Ending index of the answer in `doc_tokens`.
is_impossible: Whether the question is impossible to answer given the
context. Only used in SQuAD 2.0.
"""
def __init__(self,
qas_id,
question_text,
doc_tokens,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.doc_tokens = doc_tokens
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" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tokens,
token_to_orig_map,
token_is_max_context,
input_ids,
input_mask,
segment_ids,
paragraph_mask=None,
class_index=None,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.token_is_max_context = token_is_max_context
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
self.paragraph_mask = paragraph_mask
self.class_index = class_index
class FeatureWriter(object):
"""Writes InputFeature to TF example file."""
def __init__(self, filename, is_training):
self.filename = filename
self.is_training = is_training
self.num_features = 0
tf.io.gfile.makedirs(os.path.dirname(filename))
self._writer = tf.io.TFRecordWriter(filename)
def process_feature(self, feature):
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
self.num_features += 1
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
features = collections.OrderedDict()
features["unique_ids"] = create_int_feature([feature.unique_id])
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
if feature.paragraph_mask is not None:
features["paragraph_mask"] = create_int_feature(feature.paragraph_mask)
if feature.class_index is not None:
features["class_index"] = create_int_feature([feature.class_index])
if self.is_training:
features["start_positions"] = create_int_feature([feature.start_position])
features["end_positions"] = create_int_feature([feature.end_position])
impossible = 0
if feature.is_impossible:
impossible = 1
features["is_impossible"] = create_int_feature([impossible])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
self._writer.write(tf_example.SerializeToString())
def close(self):
self._writer.close()
def read_squad_examples(input_file, is_training,
version_2_with_negative,
translated_input_folder=None):
"""Read a SQuAD json file into a list of SquadExample."""
with tf.io.gfile.GFile(input_file, "r") as reader:
input_data = json.load(reader)["data"]
if translated_input_folder is not None:
translated_files = tf.io.gfile.glob(
os.path.join(translated_input_folder, "*.json"))
for file in translated_files:
with tf.io.gfile.GFile(file, "r") as reader:
input_data.extend(json.load(reader)["data"])
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
for c in paragraph_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)
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
end_position = None
orig_answer_text = None
is_impossible = False
if is_training:
if version_2_with_negative:
is_impossible = qa["is_impossible"]
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
answer_offset = answer["answer_start"]
answer_length = len(orig_answer_text)
start_position = char_to_word_offset[answer_offset]
end_position = char_to_word_offset[answer_offset + answer_length -
1]
# Only add answers where the text can be exactly recovered from the
# document. If this CAN'T happen it's likely due to weird Unicode
# stuff so we will just skip the example.
#
# Note that this means for training mode, every example is NOT
# guaranteed to be preserved.
actual_text = " ".join(doc_tokens[start_position:(end_position +
1)])
cleaned_answer_text = " ".join(
tokenization.whitespace_tokenize(orig_answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logging.warning("Could not find answer: '%s' vs. '%s'",
actual_text, cleaned_answer_text)
continue
else:
start_position = -1
end_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
doc_tokens=doc_tokens,
orig_answer_text=orig_answer_text,
start_position=start_position,
end_position=end_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
output_fn,
xlnet_format=False,
batch_size=None):
"""Loads a data file into a list of `InputBatch`s."""
base_id = 1000000000
unique_id = base_id
feature = None
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
# Paragraph mask used in XLNet.
# 1 represents paragraph and class tokens.
# 0 represents query and other special tokens.
paragraph_mask = []
# pylint: disable=cell-var-from-loop
def process_query(seg_q):
for token in query_tokens:
tokens.append(token)
segment_ids.append(seg_q)
paragraph_mask.append(0)
tokens.append("[SEP]")
segment_ids.append(seg_q)
paragraph_mask.append(0)
def process_paragraph(seg_p):
for i in range(doc_span.length):
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])
segment_ids.append(seg_p)
paragraph_mask.append(1)
tokens.append("[SEP]")
segment_ids.append(seg_p)
paragraph_mask.append(0)
def process_class(seg_class):
class_index = len(segment_ids)
tokens.append("[CLS]")
segment_ids.append(seg_class)
paragraph_mask.append(1)
return class_index
if xlnet_format:
seg_p, seg_q, seg_class, seg_pad = 0, 1, 2, 3
process_paragraph(seg_p)
process_query(seg_q)
class_index = process_class(seg_class)
else:
seg_p, seg_q, seg_class, seg_pad = 1, 0, 0, 0
class_index = process_class(seg_class)
process_query(seg_q)
process_paragraph(seg_p)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(seg_pad)
paragraph_mask.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(paragraph_mask) == max_seq_length
start_position = 0
end_position = 0
span_contains_answer = False
if is_training and not example.is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
span_contains_answer = (tok_start_position >= doc_start and
tok_end_position <= doc_end)
if span_contains_answer:
doc_offset = 0 if xlnet_format else len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if example_index < 20:
logging.info("*** Example ***")
logging.info("unique_id: %s", (unique_id))
logging.info("example_index: %s", (example_index))
logging.info("doc_span_index: %s", (doc_span_index))
logging.info("tokens: %s",
" ".join([tokenization.printable_text(x) for x in tokens]))
logging.info(
"token_to_orig_map: %s", " ".join([
"%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)
]))
logging.info(
"token_is_max_context: %s", " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
logging.info("paragraph_mask: %s", " ".join(
[str(x) for x in paragraph_mask]))
logging.info("class_index: %d", class_index)
if is_training:
if span_contains_answer:
answer_text = " ".join(tokens[start_position:(end_position + 1)])
logging.info("start_position: %d", (start_position))
logging.info("end_position: %d", (end_position))
logging.info("answer: %s", tokenization.printable_text(answer_text))
else:
logging.info("document span doesn't contain answer")
feature = InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
paragraph_mask=paragraph_mask,
class_index=class_index,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_position=start_position,
end_position=end_position,
is_impossible=not span_contains_answer)
# Run callback
if is_training:
output_fn(feature)
else:
output_fn(feature, is_padding=False)
unique_id += 1
if not is_training and feature:
assert batch_size
num_padding = 0
num_examples = unique_id - base_id
if unique_id % batch_size != 0:
num_padding = batch_size - (num_examples % batch_size)
logging.info("Adding padding examples to make sure no partial batch.")
logging.info("Adds %d padding examples for inference.", num_padding)
dummy_feature = copy.deepcopy(feature)
for _ in range(num_padding):
dummy_feature.unique_id = unique_id
# Run callback
output_fn(feature, is_padding=True)
unique_id += 1
return unique_id - base_id
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
# Answer: 1895
#
# The original whitespace-tokenized answer will be "(1895-1943).". However
# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
# the exact answer, 1895.
#
# However, this is not always possible. Consider the following:
#
# Question: What country is the top exporter of electronics?
# Context: The Japanese electronics industry is the lagest in the world.
# Answer: Japan
#
# In this case, the annotator chose "Japan" as a character sub-span of
# the word "Japanese". Since our WordPiece tokenizer does not split
# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
# in SQuAD, but does happen.
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
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 write_predictions(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
verbose=False):
"""Write final predictions to the json file and log-odds of null if needed."""
logging.info("Writing predictions to: %s", (output_prediction_file))
logging.info("Writing nbest to: %s", (output_nbest_file))
all_predictions, all_nbest_json, scores_diff_json = (
postprocess_output(
all_examples=all_examples,
all_features=all_features,
all_results=all_results,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
do_lower_case=do_lower_case,
version_2_with_negative=version_2_with_negative,
null_score_diff_threshold=null_score_diff_threshold,
verbose=verbose))
write_to_json_files(all_predictions, output_prediction_file)
write_to_json_files(all_nbest_json, output_nbest_file)
if version_2_with_negative:
write_to_json_files(scores_diff_json, output_null_log_odds_file)
def postprocess_output(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
xlnet_format=False,
verbose=False):
"""Postprocess model output, to form predicton results."""
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( # pylint: disable=invalid-name
"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 = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
if feature.unique_id not in unique_id_to_result:
logging.info("Skip eval example %s, not in pred.", feature.unique_id)
continue
result = unique_id_to_result[feature.unique_id]
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
if xlnet_format:
feature_null_score = result.class_logits
else:
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, start_logit,
end_index, end_logit) in _get_best_indexes_and_logits(
result=result,
n_best_size=n_best_size,
xlnet_format=xlnet_format):
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
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=start_logit,
end_logit=end_logit))
if version_2_with_negative and not xlnet_format:
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( # pylint: disable=invalid-name
"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 or xlnet_format: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(
tok_text, orig_text, do_lower_case, verbose=verbose)
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 we didn't include the empty option in the n-best, include it
if version_2_with_negative and not xlnet_format:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
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:
# pytype: disable=attribute-error
# predict "" iff the null score - the score of best non-null > threshold
if best_non_null_entry is not None:
if xlnet_format:
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
all_predictions[example.qas_id] = best_non_null_entry.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
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
else:
logging.warning("best_non_null_entry is None")
scores_diff_json[example.qas_id] = score_null
all_predictions[example.qas_id] = ""
# pytype: enable=attribute-error
all_nbest_json[example.qas_id] = nbest_json
return all_predictions, all_nbest_json, scores_diff_json
def write_to_json_files(json_records, json_file):
with tf.io.gfile.GFile(json_file, "w") as writer:
writer.write(json.dumps(json_records, indent=4) + "\n")
def get_final_text(pred_text, orig_text, do_lower_case, verbose=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose:
logging.info("Unable to find text: '%s' in '%s'", pred_text, orig_text)
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose:
logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose:
logging.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose:
logging.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indexes_and_logits(result,
n_best_size,
xlnet_format=False):
"""Generates the n-best indexes and logits from a list."""
if xlnet_format:
for i in range(n_best_size):
for j in range(n_best_size):
j_index = i * n_best_size + j
yield (result.start_indexes[i], result.start_logits[i],
result.end_indexes[j_index], result.end_logits[j_index])
else:
start_index_and_score = sorted(enumerate(result.start_logits),
key=lambda x: x[1], reverse=True)
end_index_and_score = sorted(enumerate(result.end_logits),
key=lambda x: x[1], reverse=True)
for i in range(len(start_index_and_score)):
if i >= n_best_size:
break
for j in range(len(end_index_and_score)):
if j >= n_best_size:
break
yield (start_index_and_score[i][0], start_index_and_score[i][1],
end_index_and_score[j][0], end_index_and_score[j][1])
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 generate_tf_record_from_json_file(input_file_path,
vocab_file_path,
output_path,
translated_input_folder=None,
max_seq_length=384,
do_lower_case=True,
max_query_length=64,
doc_stride=128,
version_2_with_negative=False,
xlnet_format=False):
"""Generates and saves training data into a tf record file."""
train_examples = read_squad_examples(
input_file=input_file_path,
is_training=True,
version_2_with_negative=version_2_with_negative,
translated_input_folder=translated_input_folder)
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file_path, do_lower_case=do_lower_case)
train_writer = FeatureWriter(filename=output_path, is_training=True)
number_of_examples = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
xlnet_format=xlnet_format)
train_writer.close()
meta_data = {
"task_type": "bert_squad",
"train_data_size": number_of_examples,
"max_seq_length": max_seq_length,
"max_query_length": max_query_length,
"doc_stride": doc_stride,
"version_2_with_negative": version_2_with_negative,
}
return meta_data