init
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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"""Finetuning the library models for multiple choice (Bert, Roberta, XLNet)."""
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import logging
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import os
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from dataclasses import dataclass, field
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from typing import Optional
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import numpy as np
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from utils_multiple_choice import MultipleChoiceDataset, Split, processors
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import transformers
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from transformers import (
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AutoConfig,
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AutoModelForMultipleChoice,
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AutoTokenizer,
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DataCollatorWithPadding,
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EvalPrediction,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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set_seed,
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)
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from transformers.trainer_utils import is_main_process
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logger = logging.getLogger(__name__)
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def simple_accuracy(preds, labels):
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return (preds == labels).mean()
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
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data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
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max_seq_length: int = field(
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default=128,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
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" --overwrite_output_dir to overcome."
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)
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# Setup logging
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logging.basicConfig(
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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 if training_args.local_rank in [-1, 0] else logging.WARN,
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)
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logger.warning(
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"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
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training_args.local_rank,
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training_args.device,
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training_args.n_gpu,
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bool(training_args.local_rank != -1),
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training_args.fp16,
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)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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logger.info("Training/evaluation parameters %s", training_args)
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# Set seed
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set_seed(training_args.seed)
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try:
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processor = processors[data_args.task_name]()
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label_list = processor.get_labels()
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num_labels = len(label_list)
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except KeyError:
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raise ValueError("Task not found: %s" % (data_args.task_name))
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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num_labels=num_labels,
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finetuning_task=data_args.task_name,
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cache_dir=model_args.cache_dir,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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)
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model = AutoModelForMultipleChoice.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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)
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# Get datasets
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train_dataset = (
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MultipleChoiceDataset(
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data_dir=data_args.data_dir,
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tokenizer=tokenizer,
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task=data_args.task_name,
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max_seq_length=data_args.max_seq_length,
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overwrite_cache=data_args.overwrite_cache,
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mode=Split.train,
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)
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if training_args.do_train
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else None
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)
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eval_dataset = (
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MultipleChoiceDataset(
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data_dir=data_args.data_dir,
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tokenizer=tokenizer,
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task=data_args.task_name,
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max_seq_length=data_args.max_seq_length,
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overwrite_cache=data_args.overwrite_cache,
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mode=Split.dev,
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)
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if training_args.do_eval
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else None
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)
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def compute_metrics(p: EvalPrediction) -> dict:
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preds = np.argmax(p.predictions, axis=1)
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return {"acc": simple_accuracy(preds, p.label_ids)}
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# Data collator
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data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
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# Initialize our Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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compute_metrics=compute_metrics,
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data_collator=data_collator,
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)
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# Training
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if training_args.do_train:
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trainer.train(
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model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
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)
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trainer.save_model()
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# For convenience, we also re-save the tokenizer to the same directory,
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# so that you can share your model easily on huggingface.co/models =)
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if trainer.is_world_master():
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tokenizer.save_pretrained(training_args.output_dir)
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# Evaluation
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results = {}
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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result = trainer.evaluate()
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output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
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if trainer.is_world_master():
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results *****")
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for key, value in result.items():
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logger.info(" %s = %s", key, value)
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writer.write("{} = {}\n".format(key, value))
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results.update(result)
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return results
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,483 @@
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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"""Multiple choice fine-tuning: utilities to work with multiple choice tasks of reading comprehension"""
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import csv
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import glob
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import json
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import logging
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import os
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from dataclasses import dataclass
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from enum import Enum
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from typing import Optional
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import tqdm
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from filelock import FileLock
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from transformers import PreTrainedTokenizer, is_torch_available
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logger = logging.getLogger(__name__)
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@dataclass(frozen=True)
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class InputExample:
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"""
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A single training/test example for multiple choice
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Args:
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example_id: Unique id for the example.
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question: string. The untokenized text of the second sequence (question).
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contexts: list of str. The untokenized text of the first sequence (context of corresponding question).
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endings: list of str. multiple choice's options. Its length must be equal to contexts' length.
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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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example_id: str
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question: str
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contexts: list[str]
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endings: list[str]
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label: Optional[str]
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@dataclass(frozen=True)
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class InputFeatures:
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"""
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A single set of features of data.
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Property names are the same names as the corresponding inputs to a model.
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"""
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example_id: str
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input_ids: list[list[int]]
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attention_mask: Optional[list[list[int]]]
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token_type_ids: Optional[list[list[int]]]
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label: Optional[int]
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class Split(Enum):
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train = "train"
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dev = "dev"
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test = "test"
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if is_torch_available():
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import torch
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from torch.utils.data import Dataset
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class MultipleChoiceDataset(Dataset):
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features: list[InputFeatures]
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def __init__(
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self,
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data_dir: str,
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tokenizer: PreTrainedTokenizer,
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task: str,
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max_seq_length: Optional[int] = None,
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overwrite_cache=False,
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mode: Split = Split.train,
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):
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processor = processors[task]()
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cached_features_file = os.path.join(
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data_dir,
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"cached_{}_{}_{}_{}".format(
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mode.value,
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tokenizer.__class__.__name__,
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str(max_seq_length),
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task,
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),
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)
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# Make sure only the first process in distributed training processes the dataset,
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# and the others will use the cache.
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lock_path = cached_features_file + ".lock"
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with FileLock(lock_path):
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if os.path.exists(cached_features_file) and not overwrite_cache:
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logger.info(f"Loading features from cached file {cached_features_file}")
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self.features = torch.load(cached_features_file, weights_only=True)
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else:
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logger.info(f"Creating features from dataset file at {data_dir}")
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label_list = processor.get_labels()
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if mode == Split.dev:
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examples = processor.get_dev_examples(data_dir)
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elif mode == Split.test:
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examples = processor.get_test_examples(data_dir)
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else:
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examples = processor.get_train_examples(data_dir)
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logger.info("Training examples: %s", len(examples))
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self.features = convert_examples_to_features(
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examples,
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label_list,
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max_seq_length,
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tokenizer,
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)
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logger.info("Saving features into cached file %s", cached_features_file)
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torch.save(self.features, cached_features_file)
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def __len__(self):
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return len(self.features)
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def __getitem__(self, i) -> InputFeatures:
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return self.features[i]
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class DataProcessor:
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"""Base class for data converters for multiple choice 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_test_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the test 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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class RaceProcessor(DataProcessor):
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"""Processor for the RACE data set."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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logger.info(f"LOOKING AT {data_dir} train")
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high = os.path.join(data_dir, "train/high")
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middle = os.path.join(data_dir, "train/middle")
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high = self._read_txt(high)
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middle = self._read_txt(middle)
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return self._create_examples(high + middle, "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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logger.info(f"LOOKING AT {data_dir} dev")
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high = os.path.join(data_dir, "dev/high")
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middle = os.path.join(data_dir, "dev/middle")
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high = self._read_txt(high)
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middle = self._read_txt(middle)
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return self._create_examples(high + middle, "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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logger.info(f"LOOKING AT {data_dir} test")
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high = os.path.join(data_dir, "test/high")
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middle = os.path.join(data_dir, "test/middle")
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high = self._read_txt(high)
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middle = self._read_txt(middle)
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return self._create_examples(high + middle, "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1", "2", "3"]
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def _read_txt(self, input_dir):
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lines = []
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files = glob.glob(input_dir + "/*txt")
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for file in tqdm.tqdm(files, desc="read files"):
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with open(file, encoding="utf-8") as fin:
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data_raw = json.load(fin)
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data_raw["race_id"] = file
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lines.append(data_raw)
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return lines
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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 _, data_raw in enumerate(lines):
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race_id = "{}-{}".format(set_type, data_raw["race_id"])
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article = data_raw["article"]
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for i in range(len(data_raw["answers"])):
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truth = str(ord(data_raw["answers"][i]) - ord("A"))
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question = data_raw["questions"][i]
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options = data_raw["options"][i]
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examples.append(
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InputExample(
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example_id=race_id,
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question=question,
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contexts=[article, article, article, article], # this is not efficient but convenient
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endings=[options[0], options[1], options[2], options[3]],
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label=truth,
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)
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)
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return examples
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class SynonymProcessor(DataProcessor):
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"""Processor for the Synonym data set."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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||||
logger.info(f"LOOKING AT {data_dir} train")
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return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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||||
logger.info(f"LOOKING AT {data_dir} dev")
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return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")
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def get_test_examples(self, data_dir):
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||||
"""See base class."""
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||||
logger.info(f"LOOKING AT {data_dir} dev")
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||||
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||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")
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|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3", "4"]
|
||||
|
||||
def _read_csv(self, input_file):
|
||||
with open(input_file, encoding="utf-8") as f:
|
||||
return list(csv.reader(f))
|
||||
|
||||
def _create_examples(self, lines: list[list[str]], type: str):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
|
||||
examples = [
|
||||
InputExample(
|
||||
example_id=line[0],
|
||||
question="", # in the swag dataset, the
|
||||
# common beginning of each
|
||||
# choice is stored in "sent2".
|
||||
contexts=[line[1], line[1], line[1], line[1], line[1]],
|
||||
endings=[line[2], line[3], line[4], line[5], line[6]],
|
||||
label=line[7],
|
||||
)
|
||||
for line in lines # we skip the line with the column names
|
||||
]
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
class SwagProcessor(DataProcessor):
|
||||
"""Processor for the SWAG data set."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
raise ValueError(
|
||||
"For swag testing, the input file does not contain a label column. It can not be tested in current code "
|
||||
"setting!"
|
||||
)
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3"]
|
||||
|
||||
def _read_csv(self, input_file):
|
||||
with open(input_file, encoding="utf-8") as f:
|
||||
return list(csv.reader(f))
|
||||
|
||||
def _create_examples(self, lines: list[list[str]], type: str):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
if type == "train" and lines[0][-1] != "label":
|
||||
raise ValueError("For training, the input file must contain a label column.")
|
||||
|
||||
examples = [
|
||||
InputExample(
|
||||
example_id=line[2],
|
||||
question=line[5], # in the swag dataset, the
|
||||
# common beginning of each
|
||||
# choice is stored in "sent2".
|
||||
contexts=[line[4], line[4], line[4], line[4]],
|
||||
endings=[line[7], line[8], line[9], line[10]],
|
||||
label=line[11],
|
||||
)
|
||||
for line in lines[1:] # we skip the line with the column names
|
||||
]
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
class ArcProcessor(DataProcessor):
|
||||
"""Processor for the ARC data set (request from allennlp)."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
logger.info(f"LOOKING AT {data_dir} test")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3"]
|
||||
|
||||
def _read_json(self, input_file):
|
||||
with open(input_file, encoding="utf-8") as fin:
|
||||
lines = fin.readlines()
|
||||
return lines
|
||||
|
||||
def _create_examples(self, lines, type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
|
||||
# There are two types of labels. They should be normalized
|
||||
def normalize(truth):
|
||||
if truth in "ABCD":
|
||||
return ord(truth) - ord("A")
|
||||
elif truth in "1234":
|
||||
return int(truth) - 1
|
||||
else:
|
||||
logger.info("truth ERROR! %s", str(truth))
|
||||
return None
|
||||
|
||||
examples = []
|
||||
three_choice = 0
|
||||
four_choice = 0
|
||||
five_choice = 0
|
||||
other_choices = 0
|
||||
# we deleted example which has more than or less than four choices
|
||||
for line in tqdm.tqdm(lines, desc="read arc data"):
|
||||
data_raw = json.loads(line.strip("\n"))
|
||||
if len(data_raw["question"]["choices"]) == 3:
|
||||
three_choice += 1
|
||||
continue
|
||||
elif len(data_raw["question"]["choices"]) == 5:
|
||||
five_choice += 1
|
||||
continue
|
||||
elif len(data_raw["question"]["choices"]) != 4:
|
||||
other_choices += 1
|
||||
continue
|
||||
four_choice += 1
|
||||
truth = str(normalize(data_raw["answerKey"]))
|
||||
assert truth != "None"
|
||||
question_choices = data_raw["question"]
|
||||
question = question_choices["stem"]
|
||||
id = data_raw["id"]
|
||||
options = question_choices["choices"]
|
||||
if len(options) == 4:
|
||||
examples.append(
|
||||
InputExample(
|
||||
example_id=id,
|
||||
question=question,
|
||||
contexts=[
|
||||
options[0]["para"].replace("_", ""),
|
||||
options[1]["para"].replace("_", ""),
|
||||
options[2]["para"].replace("_", ""),
|
||||
options[3]["para"].replace("_", ""),
|
||||
],
|
||||
endings=[options[0]["text"], options[1]["text"], options[2]["text"], options[3]["text"]],
|
||||
label=truth,
|
||||
)
|
||||
)
|
||||
|
||||
if type == "train":
|
||||
assert len(examples) > 1
|
||||
assert examples[0].label is not None
|
||||
logger.info("len examples: %s}", str(len(examples)))
|
||||
logger.info("Three choices: %s", str(three_choice))
|
||||
logger.info("Five choices: %s", str(five_choice))
|
||||
logger.info("Other choices: %s", str(other_choices))
|
||||
logger.info("four choices: %s", str(four_choice))
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
def convert_examples_to_features(
|
||||
examples: list[InputExample],
|
||||
label_list: list[str],
|
||||
max_length: int,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
) -> list[InputFeatures]:
|
||||
"""
|
||||
Loads a data file into a list of `InputFeatures`
|
||||
"""
|
||||
|
||||
label_map = {label: i for i, label in enumerate(label_list)}
|
||||
|
||||
features = []
|
||||
for ex_index, example in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
|
||||
if ex_index % 10000 == 0:
|
||||
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
|
||||
choices_inputs = []
|
||||
for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
|
||||
text_a = context
|
||||
if example.question.find("_") != -1:
|
||||
# this is for cloze question
|
||||
text_b = example.question.replace("_", ending)
|
||||
else:
|
||||
text_b = example.question + " " + ending
|
||||
|
||||
inputs = tokenizer(
|
||||
text_a,
|
||||
text_b,
|
||||
add_special_tokens=True,
|
||||
max_length=max_length,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
return_overflowing_tokens=True,
|
||||
)
|
||||
if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
|
||||
logger.info(
|
||||
"Attention! you are cropping tokens (swag task is ok). "
|
||||
"If you are training ARC and RACE and you are popping question + options, "
|
||||
"you need to try to use a bigger max seq length!"
|
||||
)
|
||||
|
||||
choices_inputs.append(inputs)
|
||||
|
||||
label = label_map[example.label]
|
||||
|
||||
input_ids = [x["input_ids"] for x in choices_inputs]
|
||||
attention_mask = (
|
||||
[x["attention_mask"] for x in choices_inputs] if "attention_mask" in choices_inputs[0] else None
|
||||
)
|
||||
token_type_ids = (
|
||||
[x["token_type_ids"] for x in choices_inputs] if "token_type_ids" in choices_inputs[0] else None
|
||||
)
|
||||
|
||||
features.append(
|
||||
InputFeatures(
|
||||
example_id=example.example_id,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
label=label,
|
||||
)
|
||||
)
|
||||
|
||||
for f in features[:2]:
|
||||
logger.info("*** Example ***")
|
||||
logger.info("feature: %s" % f)
|
||||
|
||||
return features
|
||||
|
||||
|
||||
processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor, "syn": SynonymProcessor}
|
||||
MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4, "syn", 5}
|
||||
Reference in New Issue
Block a user