official/nlp/data/squad_lib_sp.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.
"""Run ALBERT on SQuAD 1.1 and SQuAD 2.0 using sentence piece tokenization.
The file is forked from:
https://github.com/google-research/ALBERT/blob/master/run_squad_sp.py
"""
import collections
import copy
import json
import math
import os
from absl import logging
import numpy as np
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.
"""
def __init__(self,
qas_id,
question_text,
paragraph_text,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
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" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
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,
tok_start_to_orig_index,
tok_end_to_orig_index,
token_is_max_context,
tokens,
input_ids,
input_mask,
segment_ids,
paragraph_len,
class_index=None,
paragraph_mask=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.tok_start_to_orig_index = tok_start_to_orig_index
self.tok_end_to_orig_index = tok_end_to_orig_index
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.input_ids = input_ids
self.input_mask = input_mask
self.paragraph_mask = paragraph_mask
self.segment_ids = segment_ids
self.paragraph_len = paragraph_len
self.class_index = class_index
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
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."""
del version_2_with_negative
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"])
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
orig_answer_text = None
is_impossible = False
if is_training:
is_impossible = qa.get("is_impossible", False)
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"]
start_position = answer["answer_start"]
else:
start_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,
is_impossible=is_impossible)
examples.append(example)
return examples
def _convert_index(index, pos, m=None, is_start=True):
"""Converts index."""
if index[pos] is not None:
return index[pos]
n = len(index)
rear = pos
while rear < n - 1 and index[rear] is None:
rear += 1
front = pos
while front > 0 and index[front] is None:
front -= 1
assert index[front] is not None or index[rear] is not None
if index[front] is None:
if index[rear] >= 1: # pytype: disable=unsupported-operands
if is_start:
return 0
else:
return index[rear] - 1
return index[rear]
if index[rear] is None:
if m is not None and index[front] < m - 1:
if is_start:
return index[front] + 1
else:
return m - 1
return index[front]
if is_start:
if index[rear] > index[front] + 1:
return index[front] + 1
else:
return index[rear]
else:
if index[rear] > index[front] + 1:
return index[rear] - 1
else:
return index[front]
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
output_fn,
do_lower_case,
xlnet_format=False,
batch_size=None):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
base_id = 1000000000
unique_id = base_id
max_n, max_m = 1024, 1024
f = np.zeros((max_n, max_m), dtype=np.float32)
for (example_index, example) in enumerate(examples):
if example_index % 100 == 0:
logging.info("Converting %d/%d pos %d neg %d", example_index,
len(examples), cnt_pos, cnt_neg)
query_tokens = tokenization.encode_ids(
tokenizer.sp_model,
tokenization.preprocess_text(
example.question_text, lower=do_lower_case))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = tokenization.encode_pieces(
tokenizer.sp_model,
tokenization.preprocess_text(
example.paragraph_text, lower=do_lower_case))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
new_token = token.replace(tokenization.SPIECE_UNDERLINE, " ")
chartok_to_tok_index.extend([i] * len(new_token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(new_token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = "".join(para_tokens).replace(tokenization.SPIECE_UNDERLINE,
" ")
n, m = len(paragraph_text), len(tok_cat_text)
if n > max_n or m > max_m:
max_n = max(n, max_n)
max_m = max(m, max_m)
f = np.zeros((max_n, max_m), dtype=np.float32)
g = {}
# pylint: disable=cell-var-from-loop
def _lcs_match(max_dist, n=n, m=m):
"""Longest-common-substring algorithm."""
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(n):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (tokenization.preprocess_text(
paragraph_text[i], lower=do_lower_case,
remove_space=False) == tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
# pylint: enable=cell-var-from-loop
max_dist = abs(n - m) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[n - 1, m - 1] > 0.8 * n:
break
max_dist *= 2
orig_to_chartok_index = [None] * n
chartok_to_orig_index = [None] * m
i, j = n - 1, m - 1
while i >= 0 and j >= 0:
if (i, j) not in g:
break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index) or
f[n - 1, m - 1] < 0.8 * n):
logging.info("MISMATCH DETECTED!")
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(
chartok_to_orig_index, start_chartok_pos, n, is_start=True)
end_orig_pos = _convert_index(
chartok_to_orig_index, end_chartok_pos, n, is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = 0
tok_end_position = 0
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(
orig_to_chartok_index, start_position, is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(
orig_to_chartok_index, end_position, is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
# 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_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 = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
# 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(tokenizer.sp_model.PieceToId("[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
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_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(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(seg_p)
paragraph_mask.append(0)
return len(tokens)
def process_class(seg_class):
class_index = len(segment_ids)
tokens.append(tokenizer.sp_model.PieceToId("[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
paragraph_len = 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)
paragraph_len = process_paragraph(seg_p)
input_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
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_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
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
else:
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 is_training and span_is_impossible:
start_position = class_index
end_position = class_index
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("tok_start_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_start_to_orig_index]))
logging.info("tok_end_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_end_to_orig_index]))
logging.info(
"token_is_max_context: %s", " ".join(
["%d:%s" % (x, y) for (x, y) in token_is_max_context.items()]))
logging.info(
"input_pieces: %s",
" ".join([tokenizer.sp_model.IdToPiece(x) for x in tokens]))
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 and span_is_impossible:
logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [
tokenizer.sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
logging.info("start_position: %d", (start_position))
logging.info("end_position: %d", (end_position))
logging.info("answer: %s", (tokenization.printable_text(answer_text)))
# With multi processing, the example_index is actually the index
# within the current process therefore we use example_index=None
# to avoid being used in the future.
# The current code does not use example_index of training data.
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
paragraph_mask=paragraph_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
class_index=class_index,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible)
# Run callback
if is_training:
output_fn(feature)
else:
output_fn(feature, is_padding=False)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 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)
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
logging.info("Total number of instances: %d = pos %d neg %d",
cnt_pos + cnt_neg, cnt_pos, cnt_neg)
return unique_id - base_id
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."""
del do_lower_case, verbose
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]
doc_offset = 0 if xlnet_format else feature.tokens.index("[SEP]") + 1
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 - doc_offset >= len(feature.tok_start_to_orig_index):
continue
if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
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 - doc_offset,
end_index=end_index - doc_offset,
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=-1,
end_index=-1,
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_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos:end_orig_pos + 1].strip()
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:
assert 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:
# predict "" iff the null score - the score of best non-null > threshold
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
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_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
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 generate_tf_record_from_json_file(input_file_path,
sp_model_file,
output_path,
translated_input_folder=None,
max_seq_length=384,
do_lower_case=True,
max_query_length=64,
doc_stride=128,
xlnet_format=False,
version_2_with_negative=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.FullSentencePieceTokenizer(
sp_model_file=sp_model_file)
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,
do_lower_case=do_lower_case)
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