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- # coding=utf-8
- # Copyright 2018 The Google AI Language Team Authors.
- #
- # 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 finetuning runner."""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import collections
- import csv
- import os
- import modeling
- import optimization
- import tokenization
- import tensorflow as tf
- import pdb
-
- flags = tf.flags
-
- FLAGS = flags.FLAGS
-
- ## Required parameters
- flags.DEFINE_string(
- "data_dir", None,
- "The input data dir. Should contain the .tsv files (or other data files) "
- "for the task.")
-
- flags.DEFINE_string(
- "bert_config_file", None,
- "The config json file corresponding to the pre-trained BERT model. "
- "This specifies the model architecture.")
-
- flags.DEFINE_string("task_name", None, "The name of the task to train.")
-
- flags.DEFINE_string("vocab_file", None,
- "The vocabulary file that the BERT model was trained on.")
-
- flags.DEFINE_string(
- "output_dir", None,
- "The output directory where the model checkpoints will be written.")
-
- ## Other parameters
-
- flags.DEFINE_string(
- "init_checkpoint", None,
- "Initial checkpoint (usually from a pre-trained BERT model).")
-
- flags.DEFINE_bool(
- "do_lower_case", True,
- "Whether to lower case the input text. Should be True for uncased "
- "models and False for cased models.")
-
- flags.DEFINE_integer(
- "max_seq_length", 128,
- "The maximum total input sequence length after WordPiece tokenization. "
- "Sequences longer than this will be truncated, and sequences shorter "
- "than this will be padded.")
-
- flags.DEFINE_bool("do_train", False, "Whether to run training.")
-
- flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
-
- flags.DEFINE_bool(
- "do_predict", False,
- "Whether to run the model in inference mode on the test set.")
-
- flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
-
- flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
-
- flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
-
- flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
-
- flags.DEFINE_float("num_train_epochs", 3.0,
- "Total number of training epochs to perform.")
-
- flags.DEFINE_float(
- "warmup_proportion", 0.1,
- "Proportion of training to perform linear learning rate warmup for. "
- "E.g., 0.1 = 10% of training.")
-
- flags.DEFINE_integer("save_checkpoints_steps", 1000,
- "How often to save the model checkpoint.")
-
- flags.DEFINE_integer("iterations_per_loop", 1000,
- "How many steps to make in each estimator call.")
-
- flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
-
- tf.flags.DEFINE_string(
- "tpu_name", None,
- "The Cloud TPU to use for training. This should be either the name "
- "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
- "url.")
-
- tf.flags.DEFINE_string(
- "tpu_zone", None,
- "[Optional] GCE zone where the Cloud TPU is located in. If not "
- "specified, we will attempt to automatically detect the GCE project from "
- "metadata.")
-
- tf.flags.DEFINE_string(
- "gcp_project", None,
- "[Optional] Project name for the Cloud TPU-enabled project. If not "
- "specified, we will attempt to automatically detect the GCE project from "
- "metadata.")
-
- tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
-
- flags.DEFINE_integer(
- "num_tpu_cores", 8,
- "Only used if `use_tpu` is True. Total number of TPU cores to use.")
-
-
- class InputExample(object):
- """A single training/test example for simple sequence classification."""
- #guid=guid, text_a=text_a, text_b=text_b, text_c=text_c, label=label, score_c=score_c, score_t=score_t
- def __init__(self, guid, text_a, text_b=None, text_c=None, label=None, score_c=None, score_t=None, text_k = 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.text_c = text_c
- self.label = label
- self.score_c = score_c
- self.score_t = score_t
- self.text_k = text_k
-
- class InputFeatures(object):
- """A single set of features of data."""
-
- def __init__(self, input_ids, input_mask, segment_ids, _input_ids, _input_mask, _segment_ids, label_id, score_c, score_t, input_ids_, input_mask_, segment_ids_):
- self.input_ids = input_ids
- self.input_mask = input_mask
- self.segment_ids = segment_ids
- self._input_ids = _input_ids
- self._input_mask = _input_mask
- self._segment_ids = _segment_ids
- self.label_id = label_id
- self.score_c = score_c
- self.score_t = score_t
- self.input_ids_ = input_ids_
- self.input_mask_ = input_mask_
- self.segment_ids_ = segment_ids_
-
- 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()
-
- @classmethod
- def _read_tsv(cls, input_file, quotechar=None):
- """Reads a tab separated value file."""
- with tf.gfile.Open(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"]
-
-
- 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"]
-
- 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"]
-
- 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[1])
- text_b = tokenization.convert_to_unicode(line[2])
- 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 MedRKGProcessor(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", "2", "3", "4"]
- #score(0>01>2>3>4),
- #doc,
- #content fragment(relevance),
- #topic fragment(importance),
- #relevance score(3>2>1),
- #importance score(3>2>1),
- #knowledge fact,
- #knowledge fact index,
- #doc id;
- 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)
- #label title+abstract kg_str kg_idx rel_score rel_sentence imp_score imp_fragments keywords id bk_location
- text_a = tokenization.convert_to_unicode(line[5])#content
- text_c = tokenization.convert_to_unicode(line[7])#topic
- text_b = tokenization.convert_to_unicode(line[2])#knowledge triple
- score_c = tokenization.convert_to_unicode(str((3-int(line[4]))))# mapping 3-1 to [0,1]
- score_t = tokenization.convert_to_unicode(str((3-int(line[6]))))# mapping 3-1 to [0,2]
- text_k = tokenization.convert_to_unicode(line[1]) # title+abs
- #print (text_c)
- #pdb.set_trace()
- if set_type == "test":
- label = "0"
- else:
- label = tokenization.convert_to_unicode(line[0])# mapping 1-5 to [0,4]
- #label = tokenization.convert_to_unicode(line[0])
- examples.append(
- InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c=text_c, label=label, score_c=score_c, score_t=score_t, text_k = text_k))
- 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"]
-
- 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
-
-
- 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
-
- #print ("example.text_c",example.text_c)
- #pdb.set_trace()
- tokens_a = tokenizer.tokenize(example.text_a)
- tokens_c = tokenizer.tokenize(example.text_c)
- tokens_k = tokenizer.tokenize(example.text_k)
-
- 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)
- _truncate_seq_pair(tokens_c, tokens_b, max_seq_length - 3)
- _truncate_seq_pair(tokens_k, tokens_b, max_seq_length*2 - 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)]
- if len(tokens_c) > max_seq_length - 2:
- tokens_c = tokens_c[0:(max_seq_length - 2)]
- if len(tokens_k) > max_seq_length*2 - 2:
- tokens_k = tokens_k[0:(max_seq_length*2 - 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 as the "sentence vector". Note that this only makes sense because
- # the entire model is fine-tuned.
- _tokens = []
- _segment_ids = []
- tokens = []
- segment_ids = []
- 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)
-
- _tokens.append("[CLS]")
- _segment_ids.append(0)
- for token in tokens_c:
- _tokens.append(token)
- _segment_ids.append(0)
- _tokens.append("[SEP]")
- _segment_ids.append(0)
-
- tokens_.append("[CLS]")
- segment_ids_.append(0)
- for token in tokens_k:
- 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)
- _tokens.append(token)
- tokens_.append(token)
- segment_ids.append(1)
- _segment_ids.append(1)
- segment_ids_.append(1)
- tokens.append("[SEP]")
- segment_ids.append(1)
- _tokens.append("[SEP]")
- _segment_ids.append(1)
- tokens_.append("[SEP]")
- segment_ids_.append(1)
-
- input_ids = tokenizer.convert_tokens_to_ids(tokens)
- _input_ids = tokenizer.convert_tokens_to_ids(_tokens)
- 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)
- _input_mask = [1] * len(_input_ids)
- 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)
- while len(_input_ids) < max_seq_length:
- _input_ids.append(0)
- _input_mask.append(0)
- _segment_ids.append(0)
- while len(input_ids_) < max_seq_length*2:
- 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
- assert len(_input_ids) == max_seq_length
- assert len(_input_mask) == max_seq_length
- assert len(_segment_ids) == max_seq_length
- assert len(input_ids_) == max_seq_length*2
- assert len(input_mask_) == max_seq_length*2
- assert len(segment_ids_) == max_seq_length*2
-
- label_id = label_map[example.label]
- score_c = int(example.score_c)
- score_t = int(example.score_t)
- if ex_index < 5:
- tf.logging.info("*** Example ***")
- tf.logging.info("guid: %s" % (example.guid))
- tf.logging.info("tokens: %s" % " ".join(
- [tokenization.printable_text(x) for x in tokens]))
- tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
- tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
- tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
- tf.logging.info("_input_ids: %s" % " ".join([str(x) for x in _input_ids]))
- tf.logging.info("_input_mask: %s" % " ".join([str(x) for x in _input_mask]))
- tf.logging.info("_segment_ids: %s" % " ".join([str(x) for x in _segment_ids]))
- tf.logging.info("input_ids_: %s" % " ".join([str(x) for x in input_ids_]))
- tf.logging.info("input_mask_: %s" % " ".join([str(x) for x in input_mask_]))
- tf.logging.info("segment_ids_: %s" % " ".join([str(x) for x in segment_ids_]))
- tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
- tf.logging.info("score_c: %s (id = %d)" % (example.score_c, score_c))
- tf.logging.info("score_t: %s (id = %d)" % (example.score_t, label_id))
- feature = InputFeatures(
- input_ids=input_ids,
- input_mask=input_mask,
- segment_ids=segment_ids,
- _input_ids=_input_ids,
- _input_mask=_input_mask,
- _segment_ids=_segment_ids,
- label_id=label_id,
- score_c = score_c,
- score_t = score_t,
- input_ids_=input_ids_,
- input_mask_=input_mask_,
- segment_ids_=segment_ids_
- )
- 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.python_io.TFRecordWriter(output_file)
-
- for (ex_index, example) in enumerate(examples):
- if ex_index % 10000 == 0:
- tf.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["_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["score_c"] = create_int_feature([feature.score_c])
- features["score_t"] = create_int_feature([feature.score_t])
- 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_)
-
- tf_example = tf.train.Example(features=tf.train.Features(feature=features))
- writer.write(tf_example.SerializeToString())
-
-
- def file_based_input_fn_builder(input_file, seq_length, is_training,
- drop_remainder):
- """Creates an `input_fn` closure to be passed to TPUEstimator."""
-
- name_to_features = {
- "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
- "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "_input_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "_input_mask": tf.FixedLenFeature([seq_length], tf.int64),
- "_segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "label_ids": tf.FixedLenFeature([], tf.int64),
- "score_c": tf.FixedLenFeature([], tf.int64),
- "score_t": tf.FixedLenFeature([], tf.int64),
- "input_ids_": tf.FixedLenFeature([seq_length*2], tf.int64),
- "input_mask_": tf.FixedLenFeature([seq_length*2], tf.int64),
- "segment_ids_": tf.FixedLenFeature([seq_length*2], tf.int64),
- }
-
- def _decode_record(record, name_to_features):
- """Decodes a record to a TensorFlow example."""
- example = tf.parse_single_example(record, name_to_features)
-
- # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
- # So cast all int64 to int32.
- for name in list(example.keys()):
- t = example[name]
- if t.dtype == tf.int64:
- t = tf.to_int32(t)
- example[name] = t
-
- return example
-
- def input_fn(params):
- """The actual input function."""
- batch_size = params["batch_size"]
-
- # For training, we want a lot of parallel reading and shuffling.
- # For eval, we want no shuffling and parallel reading doesn't matter.
- d = tf.data.TFRecordDataset(input_file)
- if is_training:
- d = d.repeat()
- d = d.shuffle(buffer_size=100)
-
- d = d.apply(
- tf.contrib.data.map_and_batch(
- lambda record: _decode_record(record, name_to_features),
- batch_size=batch_size,
- drop_remainder=drop_remainder))
-
- return d
-
- return input_fn
-
-
- 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 create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
- _input_ids, _input_mask, _segment_ids,
- labels, score_c, score_t, input_ids_, input_mask_, segment_ids_, num_labels, use_one_hot_embeddings):
- """Creates a classification model."""
- model = modeling.BertModel(
- config=bert_config,
- is_training=is_training,
- input_ids=input_ids,
- input_mask=input_mask,
- token_type_ids=segment_ids,
- _input_ids=_input_ids,
- _input_mask=_input_mask,
- _token_type_ids=_segment_ids,
- input_ids_ = input_ids_,
- input_mask_ = input_mask_,
- token_type_ids_ = segment_ids_,
- use_one_hot_embeddings=use_one_hot_embeddings)
-
- # In the demo, we are doing a simple classification task on the entire
- # segment.
- #
- # If you want to use the token-level output, use model.get_sequence_output()
- # instead.
-
- #output_layer content for relevance
- #_output_layer topic for importance
- output_layer_c, _output_layer, output_layer = model.get_pooled_output()
-
-
-
- hidden_size = output_layer.shape[-1].value
-
- output_weights = tf.get_variable(
- "output_weights", [num_labels, hidden_size],
- initializer=tf.truncated_normal_initializer(stddev=0.02))
-
- output_bias = tf.get_variable(
- "output_bias", [num_labels], initializer=tf.zeros_initializer())
-
- c_output_weights = tf.get_variable(
- "output_weights_c", [2, hidden_size],
- initializer=tf.truncated_normal_initializer(stddev=0.02))
-
- c_output_bias = tf.get_variable(
- "output_bias_c", [2], initializer=tf.zeros_initializer())
-
- _output_weights = tf.get_variable(
- "output_weights_t", [3, hidden_size],
- initializer=tf.truncated_normal_initializer(stddev=0.02))
-
- _output_bias = tf.get_variable(
- "output_bias_t", [3], initializer=tf.zeros_initializer())
-
-
- with tf.variable_scope("loss"):
- if is_training:
- # I.e., 0.1 dropout
- output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
- output_layer_c = tf.nn.dropout(output_layer_c, keep_prob=0.9)
- _output_layer = tf.nn.dropout(_output_layer, keep_prob=0.9)
-
- #matching loss
- logits = tf.matmul(output_layer, output_weights, transpose_b=True)
- logits = tf.nn.bias_add(logits, output_bias)
- probabilities = tf.nn.softmax(logits, axis=-1)
- log_probs = tf.nn.log_softmax(logits, axis=-1)
-
- one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
-
- per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
- loss = tf.reduce_mean(per_example_loss)
-
- #### content loss
- logits_c = tf.matmul(output_layer_c, c_output_weights, transpose_b=True)
- logits_c = tf.nn.bias_add(logits_c, c_output_bias)
- probabilities_c = tf.nn.softmax(logits_c, axis=-1)
- log_probs_c = tf.nn.log_softmax(logits_c, axis=-1)
-
- one_hot_labels_c = tf.one_hot(score_c, depth=2, dtype=tf.float32)
-
- per_example_loss_c = -tf.reduce_sum(one_hot_labels_c * log_probs_c, axis=-1)
- loss_c = tf.reduce_mean(per_example_loss_c)
-
- #### topic loss
- logits_t = tf.matmul(_output_layer, _output_weights, transpose_b=True)
- logits_t = tf.nn.bias_add(logits_t, _output_bias)
- probabilities_t = tf.nn.softmax(logits_t, axis=-1)
- log_probs_t = tf.nn.log_softmax(logits_t, axis=-1)
-
- one_hot_labels_t = tf.one_hot(score_t, depth=3, dtype=tf.float32)
-
- per_example_loss_t = -tf.reduce_sum(one_hot_labels_t * log_probs_t, axis=-1)
- loss_t = tf.reduce_mean(per_example_loss_t)
-
- return (loss+loss_c+loss_t, per_example_loss, logits, probabilities)
-
-
- def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
- num_train_steps, num_warmup_steps, use_tpu,
- use_one_hot_embeddings):
- """Returns `model_fn` closure for TPUEstimator."""
-
- def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
- """The `model_fn` for TPUEstimator."""
-
- tf.logging.info("*** Features ***")
- for name in sorted(features.keys()):
- tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
-
- input_ids = features["input_ids"]
- input_mask = features["input_mask"]
- segment_ids = features["segment_ids"]
- _input_ids = features["_input_ids"]
- _input_mask = features["_input_mask"]
- _segment_ids = features["_segment_ids"]
- label_ids = features["label_ids"]
- score_c = features["score_c"]
- score_t = features["score_t"]
- input_ids_ = features["input_ids_"]
- input_mask_ = features["input_mask_"]
- segment_ids_ = features["segment_ids_"]
-
- is_training = (mode == tf.estimator.ModeKeys.TRAIN)
-
- (total_loss, per_example_loss, logits, probabilities) = create_model(
- bert_config, is_training, input_ids, input_mask, segment_ids,
- _input_ids, _input_mask, _segment_ids, label_ids, score_c, score_t, input_ids_, input_mask_, segment_ids_,
- num_labels, use_one_hot_embeddings)
-
- tvars = tf.trainable_variables()
-
- scaffold_fn = None
- if init_checkpoint:
- (assignment_map, initialized_variable_names
- ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
- if use_tpu:
-
- def tpu_scaffold():
- tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
- return tf.train.Scaffold()
-
- scaffold_fn = tpu_scaffold
- else:
- tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
-
- tf.logging.info("**** Trainable Variables ****")
- for var in tvars:
- init_string = ""
- if var.name in initialized_variable_names:
- init_string = ", *INIT_FROM_CKPT*"
- tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
- init_string)
-
- output_spec = None
- if mode == tf.estimator.ModeKeys.TRAIN:
-
- train_op = optimization.create_optimizer(
- total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
-
- output_spec = tf.contrib.tpu.TPUEstimatorSpec(
- mode=mode,
- loss=total_loss,
- train_op=train_op,
- scaffold_fn=scaffold_fn)
- elif mode == tf.estimator.ModeKeys.EVAL:
-
- def metric_fn(per_example_loss, label_ids, logits):
- 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:
- output_spec = tf.contrib.tpu.TPUEstimatorSpec(
- mode=mode, predictions=probabilities, scaffold_fn=scaffold_fn)
- return output_spec
-
- return model_fn
-
-
- # This function is not used by this file but is still used by the Colab and
- # people who depend on it.
- 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
-
-
- # This function is not used by this file but is still used by the Colab and
- # people who depend on it.
- def convert_examples_to_features(examples, label_list, max_seq_length,
- tokenizer):
- """Convert a set of `InputExample`s to a list of `InputFeatures`."""
-
- features = []
- for (ex_index, example) in enumerate(examples):
- if ex_index % 10000 == 0:
- tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
-
- feature = convert_single_example(ex_index, example, label_list,
- max_seq_length, tokenizer)
-
- features.append(feature)
- return features
-
-
- def main(_):
- tf.logging.set_verbosity(tf.logging.INFO)
-
- processors = {
- "cola": ColaProcessor,
- "mnli": MnliProcessor,
- "mrpc": MrpcProcessor,
- "xnli": XnliProcessor,
- "medrkg": MedRKGProcessor,
- }
-
- if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
- raise ValueError(
- "At least one of `do_train`, `do_eval` or `do_predict' 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,
- predict_batch_size=FLAGS.predict_batch_size)
-
- if FLAGS.do_train:
- train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
- file_based_convert_examples_to_features(
- train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
- 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 = file_based_input_fn_builder(
- input_file=train_file,
- 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_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
- file_based_convert_examples_to_features(
- eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
-
- 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 = file_based_input_fn_builder(
- input_file=eval_file,
- 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 FLAGS.do_predict:
- predict_examples = processor.get_test_examples(FLAGS.data_dir)
- predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
- file_based_convert_examples_to_features(predict_examples, label_list,
- FLAGS.max_seq_length, tokenizer,
- predict_file)
-
- tf.logging.info("***** Running prediction*****")
- tf.logging.info(" Num examples = %d", len(predict_examples))
- tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
-
- if FLAGS.use_tpu:
- # Warning: According to tpu_estimator.py Prediction on TPU is an
- # experimental feature and hence not supported here
- raise ValueError("Prediction in TPU not supported")
-
- predict_drop_remainder = True if FLAGS.use_tpu else False
- predict_input_fn = file_based_input_fn_builder(
- input_file=predict_file,
- seq_length=FLAGS.max_seq_length,
- is_training=False,
- drop_remainder=predict_drop_remainder)
-
- result = estimator.predict(input_fn=predict_input_fn)
-
- output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
- with tf.gfile.GFile(output_predict_file, "w") as writer:
- tf.logging.info("***** Predict results *****")
- for prediction in result:
- output_line = "\t".join(
- str(class_probability) for class_probability in prediction) + "\n"
- writer.write(output_line)
-
-
- 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()
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