wehub-resource-sync
397 lines
17 KiB
Python
397 lines
17 KiB
Python
# Copyright 2020 The HuggingFace Team. 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.
|
|
|
|
import json
|
|
import os, collections
|
|
import pickle
|
|
import logging
|
|
|
|
import numpy as np
|
|
import six
|
|
|
|
|
|
|
|
def whitespace_tokenize(text):
|
|
"""Runs basic whitespace cleaning and splitting on a piece of text."""
|
|
text = text.strip()
|
|
if not text:
|
|
return []
|
|
tokens = text.split()
|
|
return tokens
|
|
|
|
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,
|
|
doc_tokens,
|
|
orig_answer_text=None,
|
|
start_position=None,
|
|
end_position=None,
|
|
is_impossible=False,
|
|
answers=[]):
|
|
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
|
|
self.answers = answers
|
|
|
|
def __str__(self):
|
|
return self.__repr__()
|
|
|
|
def __repr__(self):
|
|
s = ""
|
|
s += "qas_id: %s" % (str(self.qas_id))
|
|
s += ", question_text: %s" % (
|
|
str(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)
|
|
if self.orig_answer_text:
|
|
s += ", ori_answer_text: %s" % (self.orig_answer_text)
|
|
s += ", answer_text: %s" % (' '.join(self.doc_tokens[self.start_position: self.end_position + 1]))
|
|
return s
|
|
|
|
class SquadFeature(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,
|
|
p_mask,
|
|
doc_offset,
|
|
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.p_mask = p_mask
|
|
self.doc_offset = doc_offset
|
|
self.token_to_orig_map = token_to_orig_map
|
|
self.token_is_max_context = token_is_max_context
|
|
self.input_ids = input_ids
|
|
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):
|
|
"""Read a SQuAD json file into a list of SquadExample."""
|
|
with open(input_file, "r") as reader:
|
|
input_data = 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 = qa.get("is_impossible", False)
|
|
answers = []
|
|
if is_training:
|
|
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(
|
|
whitespace_tokenize(orig_answer_text))
|
|
if actual_text.find(cleaned_answer_text) == -1:
|
|
print("Could not find answer: '%s' vs. '%s'",
|
|
actual_text, cleaned_answer_text)
|
|
continue
|
|
else:
|
|
start_position = -1
|
|
end_position = -1
|
|
orig_answer_text = ""
|
|
elif not is_impossible:
|
|
answers = qa["answers"]
|
|
|
|
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,
|
|
answers=answers)
|
|
examples.append(example)
|
|
return examples
|
|
|
|
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 electornics?
|
|
# 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 squad_convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride,
|
|
max_query_length, is_training,
|
|
cls_token='[CLS]', sep_token='[SEP]', additional_seq=True):
|
|
features = []
|
|
unique_id = 1000000000
|
|
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)
|
|
|
|
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 = []
|
|
p_mask = []
|
|
token_to_orig_map = {}
|
|
token_is_max_context = {}
|
|
tokens.append(cls_token)
|
|
p_mask.append(0)
|
|
for token in query_tokens:
|
|
tokens.append(token)
|
|
p_mask.append(1)
|
|
tokens.append(sep_token)
|
|
p_mask.append(1)
|
|
if additional_seq:
|
|
tokens.append(sep_token)
|
|
p_mask.append(1)
|
|
doc_offset = len(tokens)
|
|
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])
|
|
p_mask.append(0)
|
|
tokens.append(sep_token)
|
|
p_mask.append(1)
|
|
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
assert len(p_mask) == len(input_ids)
|
|
start_position = None
|
|
end_position = None
|
|
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
|
|
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:
|
|
start_position = 0
|
|
end_position = 0
|
|
else:
|
|
start_position = tok_start_position - doc_start + doc_offset
|
|
end_position = tok_end_position - doc_start + doc_offset
|
|
|
|
if is_training and example.is_impossible:
|
|
start_position = 0
|
|
end_position = 0
|
|
|
|
if example_index < 10:
|
|
print("*** Example ***")
|
|
print("unique_id: %s" % (unique_id))
|
|
print("example_index: %s" % (example_index))
|
|
print("doc_span_index: %s" % (doc_span_index))
|
|
print("tokens: %s" % " ".join(
|
|
[x for x in tokens]))
|
|
print("token_to_orig_map: %s" % " ".join(
|
|
["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
|
|
print("token_is_max_context: %s" % " ".join([
|
|
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
|
|
]))
|
|
print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
|
|
if is_training and example.is_impossible:
|
|
print("impossible example")
|
|
if is_training and not example.is_impossible:
|
|
answer_text = " ".join(tokens[start_position:(end_position + 1)])
|
|
print("start_position: %d" % (start_position))
|
|
print("end_position: %d" % (end_position))
|
|
print("answer: %s" % (answer_text))
|
|
|
|
feature = SquadFeature(
|
|
unique_id=unique_id,
|
|
example_index=example_index,
|
|
doc_span_index=doc_span_index,
|
|
tokens=tokens,
|
|
token_to_orig_map=token_to_orig_map,
|
|
token_is_max_context=token_is_max_context,
|
|
input_ids=input_ids,
|
|
p_mask=p_mask,
|
|
doc_offset=doc_offset,
|
|
start_position=start_position,
|
|
end_position=end_position,
|
|
is_impossible=example.is_impossible)
|
|
features.append(feature)
|
|
unique_id += 1
|
|
return features
|