run_tf_multiple_choice.py
a0ebe5d8b65emaster

run_tf_multiple_choice.py
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			#!/usr/bin/env python
			# coding=utf-8
			# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
			# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
			#
			# Licensed under the Apache License, Version 2.0 (the "License");
			# you may not use this file except in compliance with the License.
			# You may obtain a copy of the License at
			#
			# http://www.apache.org/licenses/LICENSE-2.0
			#
			# Unless required by applicable law or agreed to in writing, software
			# distributed under the License is distributed on an "AS IS" BASIS,
			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
			# See the License for the specific language governing permissions and
			# limitations under the License.
			""" Finetuning the library models for multiple choice (Bert, Roberta, XLNet)."""


			import logging
			import os
			from dataclasses import dataclass, field
			from typing import Dict, Optional

			import numpy as np

			from transformers import (
			AutoConfig,
			AutoTokenizer,
			EvalPrediction,
			HfArgumentParser,
			TFAutoModelForMultipleChoice,
			TFTrainer,
			TFTrainingArguments,
			set_seed,
			)
			from transformers.utils import logging as hf_logging
			from utils_multiple_choice import Split, TFMultipleChoiceDataset, processors


			hf_logging.set_verbosity_info()
			hf_logging.enable_default_handler()
			hf_logging.enable_explicit_format()


			logger = logging.getLogger(__name__)


			def simple_accuracy(preds, labels):
			return (preds == labels).mean()


			@dataclass
			class ModelArguments:
			"""
			Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
			"""

			model_name_or_path: str = field(
			metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
			)
			config_name: Optional[str] = field(
			default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
			)
			tokenizer_name: Optional[str] = field(
			default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
			)
			cache_dir: Optional[str] = field(
			default=None,
			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
			)


			@dataclass
			class DataTrainingArguments:
			"""
			Arguments pertaining to what data we are going to input our model for training and eval.
			"""

			task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
			data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
			max_seq_length: int = field(
			default=128,
			metadata={
			"help": "The maximum total input sequence length after tokenization. Sequences longer "
			"than this will be truncated, sequences shorter will be padded."
			},
			)
			overwrite_cache: bool = field(
			default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
			)


			def main():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
			model_args, data_args, training_args = parser.parse_args_into_dataclasses()

			if (
			os.path.exists(training_args.output_dir)
			and os.listdir(training_args.output_dir)
			and training_args.do_train
			and not training_args.overwrite_output_dir
			):
			raise ValueError(
			f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
			)

			# Setup logging
			logging.basicConfig(
			format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
			datefmt="%m/%d/%Y %H:%M:%S",
			level=logging.INFO,
			)
			logger.warning(
			"device: %s, n_replicas: %s, 16-bits training: %s",
			training_args.device,
			training_args.n_replicas,
			training_args.fp16,
			)
			logger.info("Training/evaluation parameters %s", training_args)

			# Set seed
			set_seed(training_args.seed)

			try:
			processor = processors[data_args.task_name]()
			label_list = processor.get_labels()
			num_labels = len(label_list)
			except KeyError:
			raise ValueError("Task not found: %s" % (data_args.task_name))

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			config = AutoConfig.from_pretrained(
			model_args.config_name if model_args.config_name else model_args.model_name_or_path,
			num_labels=num_labels,
			finetuning_task=data_args.task_name,
			cache_dir=model_args.cache_dir,
			)
			tokenizer = AutoTokenizer.from_pretrained(
			model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
			cache_dir=model_args.cache_dir,
			)
			with training_args.strategy.scope():
			model = TFAutoModelForMultipleChoice.from_pretrained(
			model_args.model_name_or_path,
			from_pt=bool(".bin" in model_args.model_name_or_path),
			config=config,
			cache_dir=model_args.cache_dir,
			)
			# Get datasets
			train_dataset = (
			TFMultipleChoiceDataset(
			data_dir=data_args.data_dir,
			tokenizer=tokenizer,
			task=data_args.task_name,
			max_seq_length=data_args.max_seq_length,
			overwrite_cache=data_args.overwrite_cache,
			mode=Split.train,
			)
			if training_args.do_train
			else None
			)
			eval_dataset = (
			TFMultipleChoiceDataset(
			data_dir=data_args.data_dir,
			tokenizer=tokenizer,
			task=data_args.task_name,
			max_seq_length=data_args.max_seq_length,
			overwrite_cache=data_args.overwrite_cache,
			mode=Split.dev,
			)
			if training_args.do_eval
			else None
			)

			def compute_metrics(p: EvalPrediction) -> Dict:
			preds = np.argmax(p.predictions, axis=1)
			return {"acc": simple_accuracy(preds, p.label_ids)}

			# Initialize our Trainer
			trainer = TFTrainer(
			model=model,
			args=training_args,
			train_dataset=train_dataset.get_dataset() if train_dataset else None,
			eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
			compute_metrics=compute_metrics,
			)

			# Training
			if training_args.do_train:
			trainer.train()
			trainer.save_model()
			tokenizer.save_pretrained(training_args.output_dir)
			# Evaluation
			results = {}
			if training_args.do_eval:
			logger.info("* Evaluate *")

			result = trainer.evaluate()

			output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
			with open(output_eval_file, "w") as writer:
			logger.info("*** Eval results ***")
			for key, value in result.items():
			logger.info(" %s = %s", key, value)
			writer.write("%s = %s\n" % (key, value))

			results.update(result)

			return results


			if __name__ == "__main__":
			main()

R11484/source_code/transformers/examples/multiple-choice/run_tf_multiple_choice.pya0ebe5d8b65emaster

run_tf_multiple_choice.pyView Options

R11484/source_code/transformers/examples/multiple-choice/run_tf_multiple_choice.py
a0ebe5d8b65emaster

run_tf_multiple_choice.py
View Options