701 lines
25 KiB
Python
701 lines
25 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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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"""BERT finetuning runner."""
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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 csv
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import os
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import modeling
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import optimization
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import tokenization
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import tensorflow as tf
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flags = tf.flags
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FLAGS = flags.FLAGS
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## Required parameters
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flags.DEFINE_string(
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"data_dir", None,
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"The input data dir. Should contain the .tsv files (or other data files) "
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"for the task.")
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flags.DEFINE_string(
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"bert_config_file", None,
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"The config json file corresponding to the pre-trained BERT model. "
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"This specifies the model architecture.")
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flags.DEFINE_string("task_name", None, "The name of the task to train.")
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flags.DEFINE_string("vocab_file", None,
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"The vocabulary file that the BERT model was trained on.")
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flags.DEFINE_string(
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"output_dir", None,
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"The output directory where the model checkpoints will be written.")
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## Other parameters
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flags.DEFINE_string(
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"init_checkpoint", None,
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"Initial checkpoint (usually from a pre-trained BERT model).")
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flags.DEFINE_bool(
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"do_lower_case", True,
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"Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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flags.DEFINE_integer(
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"max_seq_length", 128,
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"The maximum total input sequence length after WordPiece tokenization. "
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"Sequences longer than this will be truncated, and sequences shorter "
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"than this will be padded.")
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flags.DEFINE_bool("do_train", False, "Whether to run training.")
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flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
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flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
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flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
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flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
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flags.DEFINE_float("num_train_epochs", 3.0,
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"Total number of training epochs to perform.")
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flags.DEFINE_float(
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"warmup_proportion", 0.1,
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"Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10% of training.")
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flags.DEFINE_integer("save_checkpoints_steps", 1000,
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"How often to save the model checkpoint.")
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flags.DEFINE_integer("iterations_per_loop", 1000,
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"How many steps to make in each estimator call.")
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flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
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tf.flags.DEFINE_string(
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"tpu_name", None,
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"The Cloud TPU to use for training. This should be either the name "
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"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
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"url.")
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tf.flags.DEFINE_string(
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"tpu_zone", None,
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"[Optional] GCE zone where the Cloud TPU is located in. If not "
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"specified, we will attempt to automatically detect the GCE project from "
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"metadata.")
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tf.flags.DEFINE_string(
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"gcp_project", None,
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"[Optional] Project name for the Cloud TPU-enabled project. If not "
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"specified, we will attempt to automatically detect the GCE project from "
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"metadata.")
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tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
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flags.DEFINE_integer(
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"num_tpu_cores", 8,
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"Only used if `use_tpu` is True. Total number of TPU cores to use.")
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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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def __init__(self, guid, text_a, text_b=None, label=None):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.label = label
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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, input_ids, input_mask, segment_ids, label_id):
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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.label_id = label_id
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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raise NotImplementedError()
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def get_labels(self):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with tf.gfile.Open(input_file, "r") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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lines.append(line)
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return lines
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class MnliProcessor(DataProcessor):
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"""Processor for the MultiNLI data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
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"dev_matched")
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
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text_a = tokenization.convert_to_unicode(line[8])
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text_b = tokenization.convert_to_unicode(line[9])
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label = tokenization.convert_to_unicode(line[-1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class MrpcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization.convert_to_unicode(line[3])
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text_b = tokenization.convert_to_unicode(line[4])
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label = tokenization.convert_to_unicode(line[0])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class ColaProcessor(DataProcessor):
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"""Processor for the CoLA data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization.convert_to_unicode(line[3])
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label = tokenization.convert_to_unicode(line[1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
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return examples
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def convert_examples_to_features(examples, label_list, max_seq_length,
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tokenizer):
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"""Loads a data file into a list of `InputBatch`s."""
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label_map = {}
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for (i, label) in enumerate(label_list):
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label_map[label] = i
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features = []
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for (ex_index, example) in enumerate(examples):
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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if example.text_b:
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3"
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_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[0:(max_seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambigiously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens = []
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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 tokens_a:
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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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if tokens_b:
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for token in tokens_b:
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tokens.append(token)
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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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label_id = label_map[example.label]
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if ex_index < 5:
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tf.logging.info("*** Example ***")
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tf.logging.info("guid: %s" % (example.guid))
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tf.logging.info("tokens: %s" % " ".join(
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[tokenization.printable_text(x) for x in tokens]))
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tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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tf.logging.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
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features.append(
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InputFeatures(
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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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label_id=label_id))
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return features
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def _truncate_seq_pair(tokens_a, tokens_b, max_length):
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"""Truncates a sequence pair in place to the maximum length."""
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# This is a simple heuristic which will always truncate the longer sequence
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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tokens_b.pop()
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def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
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labels, num_labels, use_one_hot_embeddings):
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"""Creates a classification model."""
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model = modeling.BertModel(
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config=bert_config,
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is_training=is_training,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=segment_ids,
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use_one_hot_embeddings=use_one_hot_embeddings)
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# In the demo, we are doing a simple classification task on the entire
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# segment.
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#
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# If you want to use the token-level output, use model.get_sequence_output()
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# instead.
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output_layer = model.get_pooled_output()
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hidden_size = output_layer.shape[-1].value
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output_weights = tf.get_variable(
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"output_weights", [num_labels, hidden_size],
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initializer=tf.truncated_normal_initializer(stddev=0.02))
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output_bias = tf.get_variable(
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"output_bias", [num_labels], initializer=tf.zeros_initializer())
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with tf.variable_scope("loss"):
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if is_training:
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# I.e., 0.1 dropout
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output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
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logits = tf.matmul(output_layer, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
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per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
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loss = tf.reduce_mean(per_example_loss)
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return (loss, per_example_loss, logits)
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def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
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num_train_steps, num_warmup_steps, use_tpu,
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use_one_hot_embeddings):
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"""Returns `model_fn` closure for TPUEstimator."""
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def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""The `model_fn` for TPUEstimator."""
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tf.logging.info("*** Features ***")
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for name in sorted(features.keys()):
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tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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segment_ids = features["segment_ids"]
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label_ids = features["label_ids"]
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is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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(total_loss, per_example_loss, logits) = create_model(
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bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
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num_labels, use_one_hot_embeddings)
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tvars = tf.trainable_variables()
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scaffold_fn = None
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if init_checkpoint:
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(assignment_map,
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initialized_variable_names) = modeling.get_assigment_map_from_checkpoint(
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tvars, init_checkpoint)
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if use_tpu:
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def tpu_scaffold():
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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return tf.train.Scaffold()
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scaffold_fn = tpu_scaffold
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else:
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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tf.logging.info("**** Trainable Variables ****")
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for var in tvars:
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init_string = ""
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if var.name in initialized_variable_names:
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init_string = ", *INIT_FROM_CKPT*"
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tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
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init_string)
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output_spec = None
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if mode == tf.estimator.ModeKeys.TRAIN:
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train_op = optimization.create_optimizer(
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total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
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output_spec = tf.contrib.tpu.TPUEstimatorSpec(
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mode=mode,
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loss=total_loss,
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train_op=train_op,
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scaffold_fn=scaffold_fn)
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elif mode == tf.estimator.ModeKeys.EVAL:
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def metric_fn(per_example_loss, label_ids, logits):
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predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
|
|
accuracy = tf.metrics.accuracy(label_ids, predictions)
|
|
loss = tf.metrics.mean(per_example_loss)
|
|
return {
|
|
"eval_accuracy": accuracy,
|
|
"eval_loss": loss,
|
|
}
|
|
|
|
eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
|
|
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
|
|
mode=mode,
|
|
loss=total_loss,
|
|
eval_metrics=eval_metrics,
|
|
scaffold_fn=scaffold_fn)
|
|
else:
|
|
raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
|
|
|
|
return output_spec
|
|
|
|
return model_fn
|
|
|
|
|
|
def input_fn_builder(features, seq_length, is_training, drop_remainder):
|
|
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
|
|
|
|
all_input_ids = []
|
|
all_input_mask = []
|
|
all_segment_ids = []
|
|
all_label_ids = []
|
|
|
|
for feature in features:
|
|
all_input_ids.append(feature.input_ids)
|
|
all_input_mask.append(feature.input_mask)
|
|
all_segment_ids.append(feature.segment_ids)
|
|
all_label_ids.append(feature.label_id)
|
|
|
|
def input_fn(params):
|
|
"""The actual input function."""
|
|
batch_size = params["batch_size"]
|
|
|
|
num_examples = len(features)
|
|
|
|
# This is for demo purposes and does NOT scale to large data sets. We do
|
|
# not use Dataset.from_generator() because that uses tf.py_func which is
|
|
# not TPU compatible. The right way to load data is with TFRecordReader.
|
|
d = tf.data.Dataset.from_tensor_slices({
|
|
"input_ids":
|
|
tf.constant(
|
|
all_input_ids, shape=[num_examples, seq_length],
|
|
dtype=tf.int32),
|
|
"input_mask":
|
|
tf.constant(
|
|
all_input_mask,
|
|
shape=[num_examples, seq_length],
|
|
dtype=tf.int32),
|
|
"segment_ids":
|
|
tf.constant(
|
|
all_segment_ids,
|
|
shape=[num_examples, seq_length],
|
|
dtype=tf.int32),
|
|
"label_ids":
|
|
tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
|
|
})
|
|
|
|
if is_training:
|
|
d = d.repeat()
|
|
d = d.shuffle(buffer_size=100)
|
|
|
|
d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
|
|
return d
|
|
|
|
return input_fn
|
|
|
|
|
|
def main(_):
|
|
tf.logging.set_verbosity(tf.logging.INFO)
|
|
|
|
processors = {
|
|
"cola": ColaProcessor,
|
|
"mnli": MnliProcessor,
|
|
"mrpc": MrpcProcessor,
|
|
}
|
|
|
|
if not FLAGS.do_train and not FLAGS.do_eval:
|
|
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
|
|
|
|
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
|
|
|
|
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
|
|
raise ValueError(
|
|
"Cannot use sequence length %d because the BERT model "
|
|
"was only trained up to sequence length %d" %
|
|
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
|
|
|
|
tf.gfile.MakeDirs(FLAGS.output_dir)
|
|
|
|
task_name = FLAGS.task_name.lower()
|
|
|
|
if task_name not in processors:
|
|
raise ValueError("Task not found: %s" % (task_name))
|
|
|
|
processor = processors[task_name]()
|
|
|
|
label_list = processor.get_labels()
|
|
|
|
tokenizer = tokenization.FullTokenizer(
|
|
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
|
|
|
|
tpu_cluster_resolver = None
|
|
if FLAGS.use_tpu and FLAGS.tpu_name:
|
|
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
|
|
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
|
|
|
|
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
|
|
run_config = tf.contrib.tpu.RunConfig(
|
|
cluster=tpu_cluster_resolver,
|
|
master=FLAGS.master,
|
|
model_dir=FLAGS.output_dir,
|
|
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
|
|
tpu_config=tf.contrib.tpu.TPUConfig(
|
|
iterations_per_loop=FLAGS.iterations_per_loop,
|
|
num_shards=FLAGS.num_tpu_cores,
|
|
per_host_input_for_training=is_per_host))
|
|
|
|
train_examples = None
|
|
num_train_steps = None
|
|
num_warmup_steps = None
|
|
if FLAGS.do_train:
|
|
train_examples = processor.get_train_examples(FLAGS.data_dir)
|
|
num_train_steps = int(
|
|
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
|
|
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
|
|
|
|
model_fn = model_fn_builder(
|
|
bert_config=bert_config,
|
|
num_labels=len(label_list),
|
|
init_checkpoint=FLAGS.init_checkpoint,
|
|
learning_rate=FLAGS.learning_rate,
|
|
num_train_steps=num_train_steps,
|
|
num_warmup_steps=num_warmup_steps,
|
|
use_tpu=FLAGS.use_tpu,
|
|
use_one_hot_embeddings=FLAGS.use_tpu)
|
|
|
|
# If TPU is not available, this will fall back to normal Estimator on CPU
|
|
# or GPU.
|
|
estimator = tf.contrib.tpu.TPUEstimator(
|
|
use_tpu=FLAGS.use_tpu,
|
|
model_fn=model_fn,
|
|
config=run_config,
|
|
train_batch_size=FLAGS.train_batch_size,
|
|
eval_batch_size=FLAGS.eval_batch_size)
|
|
|
|
if FLAGS.do_train:
|
|
train_features = convert_examples_to_features(
|
|
train_examples, label_list, FLAGS.max_seq_length, tokenizer)
|
|
tf.logging.info("***** Running training *****")
|
|
tf.logging.info(" Num examples = %d", len(train_examples))
|
|
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
|
|
tf.logging.info(" Num steps = %d", num_train_steps)
|
|
train_input_fn = input_fn_builder(
|
|
features=train_features,
|
|
seq_length=FLAGS.max_seq_length,
|
|
is_training=True,
|
|
drop_remainder=True)
|
|
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
|
|
|
|
if FLAGS.do_eval:
|
|
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
|
|
eval_features = convert_examples_to_features(
|
|
eval_examples, label_list, FLAGS.max_seq_length, tokenizer)
|
|
|
|
tf.logging.info("***** Running evaluation *****")
|
|
tf.logging.info(" Num examples = %d", len(eval_examples))
|
|
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
|
|
|
|
# This tells the estimator to run through the entire set.
|
|
eval_steps = None
|
|
# However, if running eval on the TPU, you will need to specify the
|
|
# number of steps.
|
|
if FLAGS.use_tpu:
|
|
# Eval will be slightly WRONG on the TPU because it will truncate
|
|
# the last batch.
|
|
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
|
|
|
|
eval_drop_remainder = True if FLAGS.use_tpu else False
|
|
eval_input_fn = input_fn_builder(
|
|
features=eval_features,
|
|
seq_length=FLAGS.max_seq_length,
|
|
is_training=False,
|
|
drop_remainder=eval_drop_remainder)
|
|
|
|
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
|
|
|
|
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
|
|
with tf.gfile.GFile(output_eval_file, "w") as writer:
|
|
tf.logging.info("***** Eval results *****")
|
|
for key in sorted(result.keys()):
|
|
tf.logging.info(" %s = %s", key, str(result[key]))
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
flags.mark_flag_as_required("data_dir")
|
|
flags.mark_flag_as_required("task_name")
|
|
flags.mark_flag_as_required("vocab_file")
|
|
flags.mark_flag_as_required("bert_config_file")
|
|
flags.mark_flag_as_required("output_dir")
|
|
tf.app.run()
|