mirror of
https://github.com/huggingface/transformers.git
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446 lines
18 KiB
Python
446 lines
18 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_pytorch
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# import optimization
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import tokenization_pytorch
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import torch
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import logging
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logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
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datefmt = '%m/%d/%Y %H:%M:%S',
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level = logging.INFO)
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logger = logging.getLogger(__name__)
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import argparse
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--data_dir",
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default = None,
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type = str,
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required = True,
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help = "The input data dir. Should contain the .tsv files (or other data files) for the task.")
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parser.add_argument("--bert_config_file",
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default = None,
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type = str,
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required = True,
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help = "The config json file corresponding to the pre-trained BERT model. \n"
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"This specifies the model architecture.")
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parser.add_argument("--task_name",
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default = None,
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type = str,
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required = True,
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help = "The name of the task to train.")
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parser.add_argument("--vocab_file",
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default = None,
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type = str,
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required = True,
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help = "The vocabulary file that the BERT model was trained on.")
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parser.add_argument("--output_dir",
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default = None,
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type = str,
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required = True,
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help = "The output directory where the model checkpoints will be written.")
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## Other parameters
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parser.add_argument("--init_checkpoint",
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default = None,
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type = str,
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help = "Initial checkpoint (usually from a pre-trained BERT model).")
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parser.add_argument("--do_lower_case",
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default = True,
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type = bool,
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help = "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
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parser.add_argument("--max_seq_length",
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default = 128,
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type = int,
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help = "The maximum total input sequence length after WordPiece tokenization. \n"
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"Sequences longer than this will be truncated, and sequences shorter \n"
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"than this will be padded.")
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parser.add_argument("--do_train",
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default = False,
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type = bool,
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help = "Whether to run training.")
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parser.add_argument("--do_eval",
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default = False,
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type = bool,
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help = "Whether to run eval on the dev set.")
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parser.add_argument("--train_batch_size",
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default = 32,
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type = int,
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help = "Total batch size for training.")
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parser.add_argument("--eval_batch_size",
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default = 8,
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type = int,
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help = "Total batch size for eval.")
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parser.add_argument("--learning_rate",
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default = 5e-5,
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type = float,
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help = "The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs",
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default = 3.0,
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type = float,
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help = "Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion",
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default = 0.1,
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type = float,
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help = "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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parser.add_argument("--save_checkpoints_steps",
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default = 1000,
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type = int,
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help = "How often to save the model checkpoint.")
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parser.add_argument("--iterations_per_loop",
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default = 1000,
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type = int,
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help = "How many steps to make in each estimator call.")
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### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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parser.add_argument("--use_tpu",
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default = False,
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type = bool,
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help = "Whether to use TPU or GPU/CPU.")
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parser.add_argument("--tpu_name",
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default = None,
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type = str,
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help = "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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parser.add_argument("--tpu_zone",
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default = None,
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type = str,
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help = "[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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parser.add_argument("--gcp_project",
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default = None,
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type = str,
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help = "[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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parser.add_argument("--master",
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default = None,
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type = str,
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help = "[Optional] TensorFlow master URL.")
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parser.add_argument("--num_tpu_cores",
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default = 8,
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type = int,
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help = "Only used if `use_tpu` is True. Total number of TPU cores to use.")
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### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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args = parser.parse_args()
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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 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_pytorch.convert_to_unicode(line[0]))
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text_a = tokenization_pytorch.convert_to_unicode(line[8])
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text_b = tokenization_pytorch.convert_to_unicode(line[9])
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label = tokenization_pytorch.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 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_pytorch.convert_to_unicode(line[3])
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label = tokenization_pytorch.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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logger.info("*** Example ***")
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logger.info("guid: %s" % (example.guid))
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logger.info("tokens: %s" % " ".join(
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[tokenization_pytorch.printable_text(x) for x in tokens]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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logger.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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raise NotImplementedError()
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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,
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use_one_hot_embeddings):
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raise NotImplementedError()
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### ATTENTION - I removed the `use_tpu` argument
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def input_fn_builder(features, seq_length, is_training, drop_remainder):
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"""Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
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all_input_ids = []
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all_input_mask = []
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all_segment_ids = []
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all_label_ids = []
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for feature in features:
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all_input_ids.append(feature.input_ids)
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all_input_mask.append(feature.input_mask)
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all_segment_ids.append(feature.segment_ids)
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all_label_ids.append(feature.label_id)
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def input_fn(params):
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"""The actual input function."""
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batch_size = params["batch_size"]
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num_examples = len(features)
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# This is for demo purposes and does NOT scale to large data sets. We do
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# not use Dataset.from_generator() because that uses tf.py_func which is
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# not TPU compatible. The right way to load data is with TFRecordReader.
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d = tf.data.Dataset.from_tensor_slices({
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"input_ids":
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torch.Tensor(all_input_ids, size=[num_examples, seq_length],
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dtype=torch.int32, requires_grad=False),
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"input_mask":
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torch.Tensor(all_input_mask, size=[num_examples, seq_length],
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dtype=torch.int32, requires_grad=False),
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"segment_ids":
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torch.Tensor(all_segment_ids, size=[num_examples, seq_length],
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dtype=torch.int32, requires_grad=False),
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"label_ids":
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torch.Tensor(all_label_ids, size=[num_examples],
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dtype=torch.int32, requires_grad=False)
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})
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if is_training:
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d = d.repeat()
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d = d.shuffle(buffer_size=100)
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d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
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return d
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return input_fn
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