878 lines
32 KiB
Python
878 lines
32 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Library to process data for SQuAD 1.1 and SQuAD 2.0."""
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# pylint: disable=g-bad-import-order
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import copy
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import json
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import math
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import six
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from absl import logging
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import tensorflow as tf
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import tokenization
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# pylint: enable=g-bad-import-order
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class SquadExample(object):
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"""A single training/test example for simple sequence classification.
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For examples without an answer, the start and end position are -1.
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"""
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def __init__(self,
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qas_id,
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question_text,
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doc_tokens,
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orig_answer_text=None,
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start_position=None,
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end_position=None,
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is_impossible=False):
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self.qas_id = qas_id
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self.question_text = question_text
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self.doc_tokens = doc_tokens
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self.orig_answer_text = orig_answer_text
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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s = ""
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s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
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s += ", question_text: %s" % (
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tokenization.printable_text(self.question_text))
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s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
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if self.start_position:
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s += ", start_position: %d" % (self.start_position)
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if self.start_position:
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s += ", end_position: %d" % (self.end_position)
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if self.start_position:
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s += ", is_impossible: %r" % (self.is_impossible)
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return s
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self,
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unique_id,
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example_index,
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doc_span_index,
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tokens,
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token_to_orig_map,
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token_is_max_context,
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input_ids,
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input_mask,
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segment_ids,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.unique_id = unique_id
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self.example_index = example_index
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self.doc_span_index = doc_span_index
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self.tokens = tokens
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self.token_to_orig_map = token_to_orig_map
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self.token_is_max_context = token_is_max_context
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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class FeatureWriter(object):
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"""Writes InputFeature to TF example file."""
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def __init__(self, filename, is_training):
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self.filename = filename
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self.is_training = is_training
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self.num_features = 0
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self._writer = tf.io.TFRecordWriter(filename)
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def process_feature(self, feature):
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"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
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self.num_features += 1
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def create_int_feature(values):
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feature = tf.train.Feature(
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int64_list=tf.train.Int64List(value=list(values)))
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return feature
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features = collections.OrderedDict()
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features["unique_ids"] = create_int_feature([feature.unique_id])
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features["input_ids"] = create_int_feature(feature.input_ids)
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features["input_mask"] = create_int_feature(feature.input_mask)
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features["segment_ids"] = create_int_feature(feature.segment_ids)
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if self.is_training:
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features["start_positions"] = create_int_feature([feature.start_position])
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features["end_positions"] = create_int_feature([feature.end_position])
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impossible = 0
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if feature.is_impossible:
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impossible = 1
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features["is_impossible"] = create_int_feature([impossible])
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tf_example = tf.train.Example(features=tf.train.Features(feature=features))
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self._writer.write(tf_example.SerializeToString())
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def close(self):
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self._writer.close()
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def read_squad_examples(input_file, is_training, version_2_with_negative, input_data=None):
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"""Read a SQuAD json file into a list of SquadExample."""
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if input_data is None:
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with tf.io.gfile.GFile(input_file, "r") as reader:
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input_data = json.load(reader)["data"]
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def is_whitespace(c):
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if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
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return True
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return False
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examples = []
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for entry in input_data:
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for paragraph in entry["paragraphs"]:
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paragraph_text = paragraph["context"]
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doc_tokens = []
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char_to_word_offset = []
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prev_is_whitespace = True
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for c in paragraph_text:
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if is_whitespace(c):
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prev_is_whitespace = True
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else:
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if prev_is_whitespace:
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doc_tokens.append(c)
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else:
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doc_tokens[-1] += c
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prev_is_whitespace = False
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char_to_word_offset.append(len(doc_tokens) - 1)
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question_text = qa["question"]
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start_position = None
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end_position = None
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orig_answer_text = None
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is_impossible = False
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if is_training:
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if version_2_with_negative:
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is_impossible = qa["is_impossible"]
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if (len(qa["answers"]) != 1) and (not is_impossible):
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raise ValueError(
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"For training, each question should have exactly 1 answer.")
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if not is_impossible:
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answer = qa["answers"][0]
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orig_answer_text = answer["text"]
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answer_offset = answer["answer_start"]
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answer_length = len(orig_answer_text)
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start_position = char_to_word_offset[answer_offset]
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end_position = char_to_word_offset[answer_offset + answer_length -
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1]
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# Only add answers where the text can be exactly recovered from the
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# document. If this CAN'T happen it's likely due to weird Unicode
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# stuff so we will just skip the example.
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#
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# Note that this means for training mode, every example is NOT
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# guaranteed to be preserved.
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actual_text = " ".join(
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doc_tokens[start_position:(end_position + 1)])
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cleaned_answer_text = " ".join(
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tokenization.whitespace_tokenize(orig_answer_text))
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if actual_text.find(cleaned_answer_text) == -1:
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logging.warning("Could not find answer: '%s' vs. '%s'",
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actual_text, cleaned_answer_text)
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continue
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else:
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start_position = -1
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end_position = -1
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orig_answer_text = ""
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example = SquadExample(
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qas_id=qas_id,
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question_text=question_text,
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doc_tokens=doc_tokens,
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orig_answer_text=orig_answer_text,
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start_position=start_position,
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end_position=end_position,
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is_impossible=is_impossible)
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examples.append(example)
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return examples
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def convert_examples_to_features(examples,
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tokenizer,
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max_seq_length,
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doc_stride,
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max_query_length,
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is_training,
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output_fn,
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batch_size=None):
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"""Loads a data file into a list of `InputBatch`s."""
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base_id = 1000000000
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unique_id = base_id
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feature = None
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for (example_index, example) in enumerate(examples):
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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tok_to_orig_index = []
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orig_to_tok_index = []
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all_doc_tokens = []
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for (i, token) in enumerate(example.doc_tokens):
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orig_to_tok_index.append(len(all_doc_tokens))
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sub_tokens = tokenizer.tokenize(token)
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for sub_token in sub_tokens:
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tok_to_orig_index.append(i)
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all_doc_tokens.append(sub_token)
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tok_start_position = None
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tok_end_position = None
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if is_training and example.is_impossible:
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tok_start_position = -1
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tok_end_position = -1
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if is_training and not example.is_impossible:
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tok_start_position = orig_to_tok_index[example.start_position]
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if example.end_position < len(example.doc_tokens) - 1:
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tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
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else:
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tok_end_position = len(all_doc_tokens) - 1
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(tok_start_position, tok_end_position) = _improve_answer_span(
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all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
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example.orig_answer_text)
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# The -3 accounts for [CLS], [SEP] and [SEP]
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max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
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# We can have documents that are longer than the maximum sequence length.
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# To deal with this we do a sliding window approach, where we take chunks
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# of the up to our max length with a stride of `doc_stride`.
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_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
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"DocSpan", ["start", "length"])
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doc_spans = []
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start_offset = 0
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while start_offset < len(all_doc_tokens):
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length = len(all_doc_tokens) - start_offset
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if length > max_tokens_for_doc:
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length = max_tokens_for_doc
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doc_spans.append(_DocSpan(start=start_offset, length=length))
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if start_offset + length == len(all_doc_tokens):
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break
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start_offset += min(length, doc_stride)
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for (doc_span_index, doc_span) in enumerate(doc_spans):
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tokens = []
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token_to_orig_map = {}
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token_is_max_context = {}
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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in query_tokens:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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for i in range(doc_span.length):
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split_token_index = doc_span.start + i
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token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
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is_max_context = _check_is_max_context(doc_spans, doc_span_index,
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split_token_index)
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token_is_max_context[len(tokens)] = is_max_context
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tokens.append(all_doc_tokens[split_token_index])
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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start_position = None
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end_position = None
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if is_training and not example.is_impossible:
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# For training, if our document chunk does not contain an annotation
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# we throw it out, since there is nothing to predict.
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doc_start = doc_span.start
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doc_end = doc_span.start + doc_span.length - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and
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tok_end_position <= doc_end):
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out_of_span = True
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if out_of_span:
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start_position = 0
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end_position = 0
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else:
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doc_offset = len(query_tokens) + 2
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start_position = tok_start_position - doc_start + doc_offset
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end_position = tok_end_position - doc_start + doc_offset
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if is_training and example.is_impossible:
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start_position = 0
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end_position = 0
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if example_index < 2:
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logging.info("*** Example ***")
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logging.info("unique_id: %s", (unique_id))
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logging.info("example_index: %s", (example_index))
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logging.info("doc_span_index: %s", (doc_span_index))
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logging.info("tokens: %s",
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" ".join([tokenization.printable_text(x) for x in tokens]))
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logging.info(
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"token_to_orig_map: %s", " ".join([
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"%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)
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]))
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logging.info(
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"token_is_max_context: %s", " ".join([
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"%d:%s" % (x, y)
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for (x, y) in six.iteritems(token_is_max_context)
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]))
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logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
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logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
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logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
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if is_training and example.is_impossible:
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logging.info("impossible example")
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if is_training and not example.is_impossible:
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answer_text = " ".join(tokens[start_position:(end_position + 1)])
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logging.info("start_position: %d", (start_position))
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logging.info("end_position: %d", (end_position))
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logging.info("answer: %s", tokenization.printable_text(answer_text))
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feature = InputFeatures(
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unique_id=unique_id,
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example_index=example_index,
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doc_span_index=doc_span_index,
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tokens=tokens,
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token_to_orig_map=token_to_orig_map,
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token_is_max_context=token_is_max_context,
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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start_position=start_position,
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end_position=end_position,
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is_impossible=example.is_impossible)
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# Run callback
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if is_training:
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output_fn(feature)
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else:
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output_fn(feature, is_padding=False)
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unique_id += 1
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if not is_training and feature:
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assert batch_size
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num_padding = 0
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num_examples = unique_id - base_id
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if unique_id % batch_size != 0:
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num_padding = batch_size - (num_examples % batch_size)
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logging.info("Adding padding examples to make sure no partial batch.")
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logging.info("Adds %d padding examples for inference.", num_padding)
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dummy_feature = copy.deepcopy(feature)
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for _ in range(num_padding):
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dummy_feature.unique_id = unique_id
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# Run callback
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output_fn(feature, is_padding=True)
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unique_id += 1
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return unique_id - base_id
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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# The SQuAD annotations are character based. We first project them to
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# whitespace-tokenized words. But then after WordPiece tokenization, we can
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# often find a "better match". For example:
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#
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# Question: What year was John Smith born?
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# Context: The leader was John Smith (1895-1943).
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# Answer: 1895
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#
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# The original whitespace-tokenized answer will be "(1895-1943).". However
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# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
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# the exact answer, 1895.
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#
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# However, this is not always possible. Consider the following:
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#
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# Question: What country is the top exporter of electornics?
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# Context: The Japanese electronics industry is the lagest in the world.
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# Answer: Japan
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#
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# In this case, the annotator chose "Japan" as a character sub-span of
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# the word "Japanese". Since our WordPiece tokenizer does not split
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# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
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# in SQuAD, but does happen.
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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# Because of the sliding window approach taken to scoring documents, a single
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# token can appear in multiple documents. E.g.
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# Doc: the man went to the store and bought a gallon of milk
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# Span A: the man went to the
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# Span B: to the store and bought
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# Span C: and bought a gallon of
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# ...
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#
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# Now the word 'bought' will have two scores from spans B and C. We only
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# want to consider the score with "maximum context", which we define as
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# the *minimum* of its left and right context (the *sum* of left and
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# right context will always be the same, of course).
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#
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# In the example the maximum context for 'bought' would be span C since
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# it has 1 left context and 3 right context, while span B has 4 left context
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# and 0 right context.
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
|
|
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
|
|
|
|
|
|
RawResult = collections.namedtuple("RawResult",
|
|
["unique_id", "start_logits", "end_logits"])
|
|
|
|
|
|
def get_predictions(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,
|
|
verbose=False):
|
|
|
|
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):
|
|
result = unique_id_to_result[feature.unique_id]
|
|
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
|
|
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
|
|
# if we could have irrelevant answers, get the min score of irrelevant
|
|
if version_2_with_negative:
|
|
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 in start_indexes:
|
|
for end_index in end_indexes:
|
|
# 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=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index]))
|
|
|
|
if version_2_with_negative:
|
|
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: # 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 inlude the empty option in the n-best, inlcude it
|
|
if version_2_with_negative:
|
|
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
|
|
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
|
|
# pytype: enable=attribute-error
|
|
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
return all_predictions, all_nbest_json, scores_diff_json
|
|
|
|
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 = get_predictions(
|
|
all_examples, all_features, all_results, n_best_size,
|
|
max_answer_length, do_lower_case, version_2_with_negative,
|
|
null_score_diff_threshold, verbose)
|
|
|
|
with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with tf.io.gfile.GFile(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
if version_2_with_negative:
|
|
with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, 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-charcter 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(logits, n_best_size):
|
|
"""Get the n-best logits from a list."""
|
|
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
|
|
|
|
best_indexes = []
|
|
for i in range(len(index_and_score)): # pylint: disable=consider-using-enumerate
|
|
if i >= n_best_size:
|
|
break
|
|
best_indexes.append(index_and_score[i][0])
|
|
return best_indexes
|
|
|
|
|
|
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,
|
|
max_seq_length=384,
|
|
do_lower_case=True,
|
|
max_query_length=64,
|
|
doc_stride=128,
|
|
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)
|
|
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)
|
|
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
|