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# Copyright 2019 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. | |
# ============================================================================== | |
"""BERT library to process data for classification task.""" | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import collections | |
import csv | |
import os | |
from absl import logging | |
import tensorflow as tf | |
import tokenization | |
class InputExample(object): | |
"""A single training/test example for simple sequence classification.""" | |
def __init__(self, guid, text_a, text_b=None, label=None): | |
"""Constructs a InputExample. | |
Args: | |
guid: Unique id for the example. | |
text_a: string. The untokenized text of the first sequence. For single | |
sequence tasks, only this sequence must be specified. | |
text_b: (Optional) string. The untokenized text of the second sequence. | |
Only must be specified for sequence pair tasks. | |
label: (Optional) string. The label of the example. This should be | |
specified for train and dev examples, but not for test examples. | |
""" | |
self.guid = guid | |
self.text_a = text_a | |
self.text_b = text_b | |
self.label = label | |
class InputFeatures(object): | |
"""A single set of features of data.""" | |
def __init__(self, | |
input_ids, | |
input_mask, | |
segment_ids, | |
label_id, | |
is_real_example=True): | |
self.input_ids = input_ids | |
self.input_mask = input_mask | |
self.segment_ids = segment_ids | |
self.label_id = label_id | |
self.is_real_example = is_real_example | |
class DataProcessor(object): | |
"""Base class for data converters for sequence classification data sets.""" | |
def get_train_examples(self, data_dir): | |
"""Gets a collection of `InputExample`s for the train set.""" | |
raise NotImplementedError() | |
def get_dev_examples(self, data_dir): | |
"""Gets a collection of `InputExample`s for the dev set.""" | |
raise NotImplementedError() | |
def get_test_examples(self, data_dir): | |
"""Gets a collection of `InputExample`s for prediction.""" | |
raise NotImplementedError() | |
def get_labels(self): | |
"""Gets the list of labels for this data set.""" | |
raise NotImplementedError() | |
@staticmethod | |
def get_processor_name(): | |
"""Gets the string identifier of the processor.""" | |
raise NotImplementedError() | |
@classmethod | |
def _read_tsv(cls, input_file, quotechar=None): | |
"""Reads a tab separated value file.""" | |
with tf.io.gfile.GFile(input_file, "r") as f: | |
reader = csv.reader(f, delimiter="\t", quotechar=quotechar) | |
lines = [] | |
for line in reader: | |
lines.append(line) | |
return lines | |
class XnliProcessor(DataProcessor): | |
"""Processor for the XNLI data set.""" | |
def __init__(self): | |
self.language = "zh" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
lines = self._read_tsv( | |
os.path.join(data_dir, "multinli", | |
"multinli.train.%s.tsv" % self.language)) | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "train-%d" % (i) | |
text_a = tokenization.convert_to_unicode(line[0]) | |
text_b = tokenization.convert_to_unicode(line[1]) | |
label = tokenization.convert_to_unicode(line[2]) | |
if label == tokenization.convert_to_unicode("contradictory"): | |
label = tokenization.convert_to_unicode("contradiction") | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv")) | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "dev-%d" % (i) | |
language = tokenization.convert_to_unicode(line[0]) | |
if language != tokenization.convert_to_unicode(self.language): | |
continue | |
text_a = tokenization.convert_to_unicode(line[6]) | |
text_b = tokenization.convert_to_unicode(line[7]) | |
label = tokenization.convert_to_unicode(line[1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
def get_labels(self): | |
"""See base class.""" | |
return ["contradiction", "entailment", "neutral"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "XNLI" | |
class MnliProcessor(DataProcessor): | |
"""Processor for the MultiNLI data set (GLUE version).""" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), | |
"dev_matched") | |
def get_test_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test") | |
def get_labels(self): | |
"""See base class.""" | |
return ["contradiction", "entailment", "neutral"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "MNLI" | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) | |
text_a = tokenization.convert_to_unicode(line[8]) | |
text_b = tokenization.convert_to_unicode(line[9]) | |
if set_type == "test": | |
label = "contradiction" | |
else: | |
label = tokenization.convert_to_unicode(line[-1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
class MrpcProcessor(DataProcessor): | |
"""Processor for the MRPC data set (GLUE version).""" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | |
def get_test_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") | |
def get_labels(self): | |
"""See base class.""" | |
return ["0", "1"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "MRPC" | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "%s-%s" % (set_type, i) | |
text_a = tokenization.convert_to_unicode(line[3]) | |
text_b = tokenization.convert_to_unicode(line[4]) | |
if set_type == "test": | |
label = "0" | |
else: | |
label = tokenization.convert_to_unicode(line[0]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
class ColaProcessor(DataProcessor): | |
"""Processor for the CoLA data set (GLUE version).""" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | |
def get_test_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") | |
def get_labels(self): | |
"""See base class.""" | |
return ["0", "1"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "COLA" | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
for (i, line) in enumerate(lines): | |
# Only the test set has a header | |
if set_type == "test" and i == 0: | |
continue | |
guid = "%s-%s" % (set_type, i) | |
if set_type == "test": | |
text_a = tokenization.convert_to_unicode(line[1]) | |
label = "0" | |
else: | |
text_a = tokenization.convert_to_unicode(line[3]) | |
label = tokenization.convert_to_unicode(line[1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) | |
return examples | |
class SstProcessor(DataProcessor): | |
"""Processor for the SST-2 data set (GLUE version).""" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | |
def get_test_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") | |
def get_labels(self): | |
"""See base class.""" | |
return ["0", "1"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "SST-2" | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "%s-%s" % (set_type, i) | |
if set_type == "test": | |
text_a = tokenization.convert_to_unicode(line[1]) | |
label = "0" | |
else: | |
text_a = tokenization.convert_to_unicode(line[0]) | |
label = tokenization.convert_to_unicode(line[1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) | |
return examples | |
class QnliProcessor(DataProcessor): | |
"""Processor for the QNLI data set (GLUE version).""" | |
def get_train_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | |
def get_dev_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched") | |
def get_test_examples(self, data_dir): | |
"""See base class.""" | |
return self._create_examples( | |
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") | |
def get_labels(self): | |
"""See base class.""" | |
return ["entailment", "not_entailment"] | |
@staticmethod | |
def get_processor_name(): | |
"""See base class.""" | |
return "QNLI" | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "%s-%s" % (set_type, 1) | |
if set_type == "test": | |
text_a = tokenization.convert_to_unicode(line[1]) | |
text_b = tokenization.convert_to_unicode(line[2]) | |
label = "entailment" | |
else: | |
text_a = tokenization.convert_to_unicode(line[1]) | |
text_b = tokenization.convert_to_unicode(line[2]) | |
label = tokenization.convert_to_unicode(line[-1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
def convert_single_example(ex_index, example, label_list, max_seq_length, | |
tokenizer): | |
"""Converts a single `InputExample` into a single `InputFeatures`.""" | |
label_map = {} | |
for (i, label) in enumerate(label_list): | |
label_map[label] = i | |
tokens_a = tokenizer.tokenize(example.text_a) | |
tokens_b = None | |
if example.text_b: | |
tokens_b = tokenizer.tokenize(example.text_b) | |
if tokens_b: | |
# Modifies `tokens_a` and `tokens_b` in place so that the total | |
# length is less than the specified length. | |
# Account for [CLS], [SEP], [SEP] with "- 3" | |
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) | |
else: | |
# Account for [CLS] and [SEP] with "- 2" | |
if len(tokens_a) > max_seq_length - 2: | |
tokens_a = tokens_a[0:(max_seq_length - 2)] | |
# The convention in BERT is: | |
# (a) For sequence pairs: | |
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] | |
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 | |
# (b) For single sequences: | |
# tokens: [CLS] the dog is hairy . [SEP] | |
# type_ids: 0 0 0 0 0 0 0 | |
# | |
# Where "type_ids" are used to indicate whether this is the first | |
# sequence or the second sequence. The embedding vectors for `type=0` and | |
# `type=1` were learned during pre-training and are added to the wordpiece | |
# embedding vector (and position vector). This is not *strictly* necessary | |
# since the [SEP] token unambiguously separates the sequences, but it makes | |
# it easier for the model to learn the concept of sequences. | |
# | |
# For classification tasks, the first vector (corresponding to [CLS]) is | |
# used as the "sentence vector". Note that this only makes sense because | |
# the entire model is fine-tuned. | |
tokens = [] | |
segment_ids = [] | |
tokens.append("[CLS]") | |
segment_ids.append(0) | |
for token in tokens_a: | |
tokens.append(token) | |
segment_ids.append(0) | |
tokens.append("[SEP]") | |
segment_ids.append(0) | |
if tokens_b: | |
for token in tokens_b: | |
tokens.append(token) | |
segment_ids.append(1) | |
tokens.append("[SEP]") | |
segment_ids.append(1) | |
input_ids = tokenizer.convert_tokens_to_ids(tokens) | |
# The mask has 1 for real tokens and 0 for padding tokens. Only real | |
# tokens are attended to. | |
input_mask = [1] * len(input_ids) | |
# Zero-pad up to the sequence length. | |
while len(input_ids) < max_seq_length: | |
input_ids.append(0) | |
input_mask.append(0) | |
segment_ids.append(0) | |
assert len(input_ids) == max_seq_length | |
assert len(input_mask) == max_seq_length | |
assert len(segment_ids) == max_seq_length | |
label_id = label_map[example.label] | |
if ex_index < 5: | |
logging.info("*** Example ***") | |
logging.info("guid: %s", (example.guid)) | |
logging.info("tokens: %s", | |
" ".join([tokenization.printable_text(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("label: %s (id = %d)", example.label, label_id) | |
feature = InputFeatures( | |
input_ids=input_ids, | |
input_mask=input_mask, | |
segment_ids=segment_ids, | |
label_id=label_id, | |
is_real_example=True) | |
return feature | |
def file_based_convert_examples_to_features(examples, label_list, | |
max_seq_length, tokenizer, | |
output_file): | |
"""Convert a set of `InputExample`s to a TFRecord file.""" | |
writer = tf.io.TFRecordWriter(output_file) | |
for (ex_index, example) in enumerate(examples): | |
if ex_index % 10000 == 0: | |
logging.info("Writing example %d of %d", ex_index, len(examples)) | |
feature = convert_single_example(ex_index, example, label_list, | |
max_seq_length, tokenizer) | |
def create_int_feature(values): | |
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) | |
return f | |
features = collections.OrderedDict() | |
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) | |
features["label_ids"] = create_int_feature([feature.label_id]) | |
features["is_real_example"] = create_int_feature( | |
[int(feature.is_real_example)]) | |
tf_example = tf.train.Example(features=tf.train.Features(feature=features)) | |
writer.write(tf_example.SerializeToString()) | |
writer.close() | |
def _truncate_seq_pair(tokens_a, tokens_b, max_length): | |
"""Truncates a sequence pair in place to the maximum length.""" | |
# This is a simple heuristic which will always truncate the longer sequence | |
# one token at a time. This makes more sense than truncating an equal percent | |
# of tokens from each, since if one sequence is very short then each token | |
# that's truncated likely contains more information than a longer sequence. | |
while True: | |
total_length = len(tokens_a) + len(tokens_b) | |
if total_length <= max_length: | |
break | |
if len(tokens_a) > len(tokens_b): | |
tokens_a.pop() | |
else: | |
tokens_b.pop() | |
def generate_tf_record_from_data_file(processor, | |
data_dir, | |
vocab_file, | |
train_data_output_path=None, | |
eval_data_output_path=None, | |
max_seq_length=128, | |
do_lower_case=True): | |
"""Generates and saves training data into a tf record file. | |
Arguments: | |
processor: Input processor object to be used for generating data. Subclass | |
of `DataProcessor`. | |
data_dir: Directory that contains train/eval data to process. Data files | |
should be in from "dev.tsv", "test.tsv", or "train.tsv". | |
vocab_file: Text file with words to be used for training/evaluation. | |
train_data_output_path: Output to which processed tf record for training | |
will be saved. | |
eval_data_output_path: Output to which processed tf record for evaluation | |
will be saved. | |
max_seq_length: Maximum sequence length of the to be generated | |
training/eval data. | |
do_lower_case: Whether to lower case input text. | |
Returns: | |
A dictionary containing input meta data. | |
""" | |
assert train_data_output_path or eval_data_output_path | |
label_list = processor.get_labels() | |
tokenizer = tokenization.FullTokenizer( | |
vocab_file=vocab_file, do_lower_case=do_lower_case) | |
assert train_data_output_path | |
train_input_data_examples = processor.get_train_examples(data_dir) | |
file_based_convert_examples_to_features(train_input_data_examples, label_list, | |
max_seq_length, tokenizer, | |
train_data_output_path) | |
num_training_data = len(train_input_data_examples) | |
if eval_data_output_path: | |
eval_input_data_examples = processor.get_dev_examples(data_dir) | |
file_based_convert_examples_to_features(eval_input_data_examples, | |
label_list, max_seq_length, | |
tokenizer, eval_data_output_path) | |
meta_data = { | |
"task_type": "bert_classification", | |
"processor_type": processor.get_processor_name(), | |
"num_labels": len(processor.get_labels()), | |
"train_data_size": num_training_data, | |
"max_seq_length": max_seq_length, | |
} | |
if eval_data_output_path: | |
meta_data["eval_data_size"] = len(eval_input_data_examples) | |
return meta_data |
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