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parent
c852036b4a
commit
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@ -27,7 +27,7 @@ This is still a work-in-progress – in particular documentation is still sparse
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| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization) | CNN/Daily Mail | - | - | - | -
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| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization) | CNN/Daily Mail | - | - | - | -
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| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation) | WMT | - | - | - | -
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| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation) | WMT | - | - | - | -
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| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | -
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| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | -
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| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | - | - | - | -
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| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | - | -
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<br>
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<br>
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231
examples/adversarial/run_hans.py
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231
examples/adversarial/run_hans.py
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@ -0,0 +1,231 @@
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# coding=utf-8
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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 sequence classification on HANS."""
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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 Dict, List, Optional
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import numpy as np
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import torch
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from transformers import (
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AutoConfig,
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AutoModelForSequenceClassification,
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AutoTokenizer,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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default_data_collator,
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set_seed,
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)
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from utils_hans import HansDataset, InputFeatures, hans_processors
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logger = logging.getLogger(__name__)
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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, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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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(
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metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys())}
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)
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data_dir: str = field(
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metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
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)
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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": "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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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 hans_data_collator(features: List[InputFeatures]) -> Dict[str, torch.Tensor]:
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"""
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Data collator that removes the "pairID" key if present.
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"""
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batch = default_data_collator(features)
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_ = batch.pop("pairID", None)
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return batch
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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 --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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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 = hans_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 = AutoModelForSequenceClassification.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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HansDataset(
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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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)
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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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HansDataset(
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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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evaluate=True,
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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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# 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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data_collator=hans_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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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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output = trainer.predict(eval_dataset)
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preds = output.predictions
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preds = np.argmax(preds, axis=1)
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pair_ids = [ex.pairID for ex in eval_dataset]
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output_eval_file = os.path.join(training_args.output_dir, "hans_predictions.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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for pid, pred in zip(pair_ids, preds):
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writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
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trainer._log(output.metrics)
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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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@ -1,577 +0,0 @@
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# coding=utf-8
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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 sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
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from __future__ import absolute_import, division, print_function
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import argparse
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import glob
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import logging
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import os
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import random
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from transformers import (
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WEIGHTS_NAME,
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AdamW,
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AlbertConfig,
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AlbertForSequenceClassification,
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AlbertTokenizer,
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BertConfig,
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BertForSequenceClassification,
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BertTokenizer,
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DistilBertConfig,
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DistilBertForSequenceClassification,
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DistilBertTokenizer,
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RobertaConfig,
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RobertaForSequenceClassification,
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RobertaTokenizer,
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XLMConfig,
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XLMForSequenceClassification,
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XLMTokenizer,
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XLNetConfig,
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XLNetForSequenceClassification,
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XLNetTokenizer,
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default_data_collator,
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get_linear_schedule_with_warmup,
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)
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from utils_hans import HansDataset, hans_output_modes, hans_processors
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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MODEL_CLASSES = {
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"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
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"xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
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"xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
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"roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
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"distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
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"albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
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}
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def train(args, train_dataset, model, tokenizer):
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(
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train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=default_data_collator,
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)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": args.weight_decay,
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},
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{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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||||||
# multi-gpu training (should be after apex fp16 initialization)
|
|
||||||
if args.n_gpu > 1:
|
|
||||||
model = torch.nn.DataParallel(model)
|
|
||||||
|
|
||||||
# Distributed training (should be after apex fp16 initialization)
|
|
||||||
if args.local_rank != -1:
|
|
||||||
model = torch.nn.parallel.DistributedDataParallel(
|
|
||||||
model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
|
|
||||||
)
|
|
||||||
|
|
||||||
# Train!
|
|
||||||
logger.info("***** Running training *****")
|
|
||||||
logger.info(" Num examples = %d", len(train_dataset))
|
|
||||||
logger.info(" Num Epochs = %d", args.num_train_epochs)
|
|
||||||
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
|
|
||||||
logger.info(
|
|
||||||
" Total train batch size (w. parallel, distributed & accumulation) = %d",
|
|
||||||
args.train_batch_size
|
|
||||||
* args.gradient_accumulation_steps
|
|
||||||
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
|
|
||||||
)
|
|
||||||
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
|
|
||||||
logger.info(" Total optimization steps = %d", t_total)
|
|
||||||
|
|
||||||
global_step = 0
|
|
||||||
tr_loss, logging_loss = 0.0, 0.0
|
|
||||||
model.zero_grad()
|
|
||||||
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
|
|
||||||
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
|
|
||||||
for _ in train_iterator:
|
|
||||||
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
|
|
||||||
for step, batch in enumerate(epoch_iterator):
|
|
||||||
model.train()
|
|
||||||
inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
|
|
||||||
outputs = model(**inputs)
|
|
||||||
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
|
|
||||||
|
|
||||||
if args.n_gpu > 1:
|
|
||||||
loss = loss.mean() # mean() to average on multi-gpu parallel training
|
|
||||||
if args.gradient_accumulation_steps > 1:
|
|
||||||
loss = loss / args.gradient_accumulation_steps
|
|
||||||
|
|
||||||
if args.fp16:
|
|
||||||
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
|
||||||
scaled_loss.backward()
|
|
||||||
else:
|
|
||||||
loss.backward()
|
|
||||||
|
|
||||||
tr_loss += loss.item()
|
|
||||||
if (step + 1) % args.gradient_accumulation_steps == 0:
|
|
||||||
if args.fp16:
|
|
||||||
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
|
|
||||||
else:
|
|
||||||
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
|
|
||||||
|
|
||||||
optimizer.step()
|
|
||||||
scheduler.step() # Update learning rate schedule
|
|
||||||
model.zero_grad()
|
|
||||||
global_step += 1
|
|
||||||
|
|
||||||
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
|
|
||||||
logs = {}
|
|
||||||
if (
|
|
||||||
args.local_rank == -1 and args.evaluate_during_training
|
|
||||||
): # Only evaluate when single GPU otherwise metrics may not average well
|
|
||||||
results = evaluate(args, model, tokenizer)
|
|
||||||
for key, value in results.items():
|
|
||||||
eval_key = "eval_{}".format(key)
|
|
||||||
logs[eval_key] = value
|
|
||||||
|
|
||||||
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
|
|
||||||
learning_rate_scalar = scheduler.get_lr()[0]
|
|
||||||
logs["learning_rate"] = learning_rate_scalar
|
|
||||||
logs["loss"] = loss_scalar
|
|
||||||
logging_loss = tr_loss
|
|
||||||
|
|
||||||
for key, value in logs.items():
|
|
||||||
tb_writer.add_scalar(key, value, global_step)
|
|
||||||
# print(json.dumps({**logs, **{'step': global_step}}))
|
|
||||||
|
|
||||||
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
|
|
||||||
# Save model checkpoint
|
|
||||||
output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
|
|
||||||
if not os.path.exists(output_dir):
|
|
||||||
os.makedirs(output_dir)
|
|
||||||
model_to_save = (
|
|
||||||
model.module if hasattr(model, "module") else model
|
|
||||||
) # Take care of distributed/parallel training
|
|
||||||
model_to_save.save_pretrained(output_dir)
|
|
||||||
torch.save(args, os.path.join(output_dir, "training_args.bin"))
|
|
||||||
logger.info("Saving model checkpoint to %s", output_dir)
|
|
||||||
|
|
||||||
if args.max_steps > 0 and global_step > args.max_steps:
|
|
||||||
epoch_iterator.close()
|
|
||||||
break
|
|
||||||
if args.max_steps > 0 and global_step > args.max_steps:
|
|
||||||
train_iterator.close()
|
|
||||||
break
|
|
||||||
|
|
||||||
if args.local_rank in [-1, 0]:
|
|
||||||
tb_writer.close()
|
|
||||||
|
|
||||||
return global_step, tr_loss / global_step
|
|
||||||
|
|
||||||
|
|
||||||
def evaluate(args, model, tokenizer, label_list, prefix=""):
|
|
||||||
# Loop to handle MNLI double evaluation (matched, mis-matched)
|
|
||||||
eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
|
|
||||||
eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
|
|
||||||
|
|
||||||
results = {}
|
|
||||||
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
|
|
||||||
eval_dataset = HansDataset(
|
|
||||||
args.data_dir,
|
|
||||||
tokenizer,
|
|
||||||
args.task_name,
|
|
||||||
args.max_seq_length,
|
|
||||||
overwrite_cache=args.overwrite_cache,
|
|
||||||
evaluate=True,
|
|
||||||
)
|
|
||||||
|
|
||||||
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
|
|
||||||
os.makedirs(eval_output_dir)
|
|
||||||
|
|
||||||
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
|
|
||||||
# Note that DistributedSampler samples randomly
|
|
||||||
eval_sampler = SequentialSampler(eval_dataset)
|
|
||||||
eval_dataloader = DataLoader(
|
|
||||||
eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=default_data_collator,
|
|
||||||
)
|
|
||||||
|
|
||||||
# multi-gpu eval
|
|
||||||
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
|
|
||||||
model = torch.nn.DataParallel(model)
|
|
||||||
|
|
||||||
# Eval!
|
|
||||||
logger.info("***** Running evaluation {} *****".format(prefix))
|
|
||||||
logger.info(" Num examples = %d", len(eval_dataset))
|
|
||||||
logger.info(" Batch size = %d", args.eval_batch_size)
|
|
||||||
eval_loss = 0.0
|
|
||||||
nb_eval_steps = 0
|
|
||||||
preds = None
|
|
||||||
out_label_ids = None
|
|
||||||
for batch in tqdm(eval_dataloader, desc="Evaluating"):
|
|
||||||
model.eval()
|
|
||||||
inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
|
|
||||||
pair_ids = batch.pop("pairID", None)
|
|
||||||
with torch.no_grad():
|
|
||||||
outputs = model(**inputs)
|
|
||||||
tmp_eval_loss, logits = outputs[:2]
|
|
||||||
|
|
||||||
eval_loss += tmp_eval_loss.mean().item()
|
|
||||||
nb_eval_steps += 1
|
|
||||||
if preds is None:
|
|
||||||
preds = logits.detach().cpu().numpy()
|
|
||||||
out_label_ids = inputs["labels"].detach().cpu().numpy()
|
|
||||||
pair_ids = pair_ids.detach().cpu().numpy()
|
|
||||||
else:
|
|
||||||
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
|
|
||||||
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
|
|
||||||
pair_ids = np.append(pair_ids, pair_ids.detach().cpu().numpy(), axis=0)
|
|
||||||
|
|
||||||
eval_loss = eval_loss / nb_eval_steps
|
|
||||||
if args.output_mode == "classification":
|
|
||||||
preds = np.argmax(preds, axis=1)
|
|
||||||
elif args.output_mode == "regression":
|
|
||||||
preds = np.squeeze(preds)
|
|
||||||
|
|
||||||
output_eval_file = os.path.join(eval_output_dir, "hans_predictions.txt")
|
|
||||||
with open(output_eval_file, "w") as writer:
|
|
||||||
writer.write("pairID,gld_label\n")
|
|
||||||
for pid, pred in zip(pair_ids, preds):
|
|
||||||
writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
|
|
||||||
|
|
||||||
return results
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
parser = argparse.ArgumentParser()
|
|
||||||
|
|
||||||
# Required parameters
|
|
||||||
parser.add_argument(
|
|
||||||
"--data_dir",
|
|
||||||
default=None,
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--model_type",
|
|
||||||
default=None,
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--model_name_or_path",
|
|
||||||
default=None,
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Path to pretrained model or model identifier from huggingface.co/models",
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--task_name",
|
|
||||||
default=None,
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()),
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--output_dir",
|
|
||||||
default=None,
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="The output directory where the model predictions and checkpoints will be written.",
|
|
||||||
)
|
|
||||||
|
|
||||||
# Other parameters
|
|
||||||
parser.add_argument(
|
|
||||||
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--tokenizer_name",
|
|
||||||
default="",
|
|
||||||
type=str,
|
|
||||||
help="Pretrained tokenizer name or path if not the same as model_name",
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--cache_dir",
|
|
||||||
default="",
|
|
||||||
type=str,
|
|
||||||
help="Where do you want to store the pre-trained models downloaded from s3",
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--max_seq_length",
|
|
||||||
default=128,
|
|
||||||
type=int,
|
|
||||||
help="The maximum total input sequence length after tokenization. Sequences longer "
|
|
||||||
"than this will be truncated, sequences shorter will be padded.",
|
|
||||||
)
|
|
||||||
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
|
|
||||||
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
|
|
||||||
parser.add_argument(
|
|
||||||
"--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
|
|
||||||
)
|
|
||||||
|
|
||||||
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
|
|
||||||
parser.add_argument(
|
|
||||||
"--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--gradient_accumulation_steps",
|
|
||||||
type=int,
|
|
||||||
default=1,
|
|
||||||
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
|
||||||
)
|
|
||||||
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
|
||||||
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
|
|
||||||
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
|
|
||||||
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
||||||
parser.add_argument(
|
|
||||||
"--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--max_steps",
|
|
||||||
default=-1,
|
|
||||||
type=int,
|
|
||||||
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
|
|
||||||
)
|
|
||||||
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
|
|
||||||
|
|
||||||
parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
|
|
||||||
parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
|
|
||||||
parser.add_argument(
|
|
||||||
"--eval_all_checkpoints",
|
|
||||||
action="store_true",
|
|
||||||
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
|
|
||||||
)
|
|
||||||
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
|
|
||||||
parser.add_argument(
|
|
||||||
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
|
|
||||||
)
|
|
||||||
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
|
|
||||||
|
|
||||||
parser.add_argument(
|
|
||||||
"--fp16",
|
|
||||||
action="store_true",
|
|
||||||
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
"--fp16_opt_level",
|
|
||||||
type=str,
|
|
||||||
default="O1",
|
|
||||||
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
|
|
||||||
"See details at https://nvidia.github.io/apex/amp.html",
|
|
||||||
)
|
|
||||||
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
||||||
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
|
|
||||||
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
if (
|
|
||||||
os.path.exists(args.output_dir)
|
|
||||||
and os.listdir(args.output_dir)
|
|
||||||
and args.do_train
|
|
||||||
and not args.overwrite_output_dir
|
|
||||||
):
|
|
||||||
raise ValueError(
|
|
||||||
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
|
||||||
args.output_dir
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
# Setup distant debugging if needed
|
|
||||||
if args.server_ip and args.server_port:
|
|
||||||
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
|
||||||
import ptvsd
|
|
||||||
|
|
||||||
print("Waiting for debugger attach")
|
|
||||||
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
|
|
||||||
ptvsd.wait_for_attach()
|
|
||||||
|
|
||||||
# Setup CUDA, GPU & distributed training
|
|
||||||
if args.local_rank == -1 or args.no_cuda:
|
|
||||||
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
|
||||||
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
|
|
||||||
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
||||||
torch.cuda.set_device(args.local_rank)
|
|
||||||
device = torch.device("cuda", args.local_rank)
|
|
||||||
torch.distributed.init_process_group(backend="nccl")
|
|
||||||
args.n_gpu = 1
|
|
||||||
args.device = device
|
|
||||||
|
|
||||||
# Setup logging
|
|
||||||
logging.basicConfig(
|
|
||||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
||||||
datefmt="%m/%d/%Y %H:%M:%S",
|
|
||||||
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
|
|
||||||
)
|
|
||||||
logger.warning(
|
|
||||||
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
|
|
||||||
args.local_rank,
|
|
||||||
device,
|
|
||||||
args.n_gpu,
|
|
||||||
bool(args.local_rank != -1),
|
|
||||||
args.fp16,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Set seed
|
|
||||||
set_seed(args)
|
|
||||||
|
|
||||||
# Prepare GLUE task
|
|
||||||
args.task_name = args.task_name.lower()
|
|
||||||
if args.task_name not in hans_processors:
|
|
||||||
raise ValueError("Task not found: %s" % (args.task_name))
|
|
||||||
processor = hans_processors[args.task_name]()
|
|
||||||
args.output_mode = hans_output_modes[args.task_name]
|
|
||||||
label_list = processor.get_labels()
|
|
||||||
num_labels = len(label_list)
|
|
||||||
|
|
||||||
# Load pretrained model and tokenizer
|
|
||||||
if args.local_rank not in [-1, 0]:
|
|
||||||
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
|
||||||
|
|
||||||
args.model_type = args.model_type.lower()
|
|
||||||
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
|
|
||||||
config = config_class.from_pretrained(
|
|
||||||
args.config_name if args.config_name else args.model_name_or_path,
|
|
||||||
num_labels=num_labels,
|
|
||||||
finetuning_task=args.task_name,
|
|
||||||
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
||||||
)
|
|
||||||
tokenizer = tokenizer_class.from_pretrained(
|
|
||||||
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
|
|
||||||
do_lower_case=args.do_lower_case,
|
|
||||||
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
||||||
)
|
|
||||||
model = model_class.from_pretrained(
|
|
||||||
args.model_name_or_path,
|
|
||||||
from_tf=bool(".ckpt" in args.model_name_or_path),
|
|
||||||
config=config,
|
|
||||||
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
||||||
)
|
|
||||||
|
|
||||||
if args.local_rank == 0:
|
|
||||||
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
|
||||||
|
|
||||||
model.to(args.device)
|
|
||||||
|
|
||||||
logger.info("Training/evaluation parameters %s", args)
|
|
||||||
|
|
||||||
# Training
|
|
||||||
if args.do_train:
|
|
||||||
train_dataset = HansDataset(
|
|
||||||
args.data_dir, tokenizer, args.task_name, args.max_seq_length, overwrite_cache=args.overwrite_cache
|
|
||||||
)
|
|
||||||
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
||||||
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
||||||
|
|
||||||
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
|
|
||||||
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
|
||||||
# Create output directory if needed
|
|
||||||
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
|
|
||||||
os.makedirs(args.output_dir)
|
|
||||||
|
|
||||||
logger.info("Saving model checkpoint to %s", args.output_dir)
|
|
||||||
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
|
||||||
# They can then be reloaded using `from_pretrained()`
|
|
||||||
model_to_save = (
|
|
||||||
model.module if hasattr(model, "module") else model
|
|
||||||
) # Take care of distributed/parallel training
|
|
||||||
model_to_save.save_pretrained(args.output_dir)
|
|
||||||
tokenizer.save_pretrained(args.output_dir)
|
|
||||||
|
|
||||||
# Good practice: save your training arguments together with the trained model
|
|
||||||
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
|
||||||
|
|
||||||
# Load a trained model and vocabulary that you have fine-tuned
|
|
||||||
model = model_class.from_pretrained(args.output_dir)
|
|
||||||
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
|
|
||||||
model.to(args.device)
|
|
||||||
|
|
||||||
# Evaluation
|
|
||||||
results = {}
|
|
||||||
if args.do_eval and args.local_rank in [-1, 0]:
|
|
||||||
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
|
||||||
checkpoints = [args.output_dir]
|
|
||||||
if args.eval_all_checkpoints:
|
|
||||||
checkpoints = list(
|
|
||||||
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
|
|
||||||
)
|
|
||||||
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
|
|
||||||
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
|
||||||
for checkpoint in checkpoints:
|
|
||||||
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
|
||||||
prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
|
|
||||||
|
|
||||||
model = model_class.from_pretrained(checkpoint)
|
|
||||||
model.to(args.device)
|
|
||||||
result = evaluate(args, model, tokenizer, label_list, prefix=prefix)
|
|
||||||
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
|
|
||||||
results.update(result)
|
|
||||||
|
|
||||||
return results
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
@ -22,15 +22,7 @@ from typing import List, Optional, Union
|
|||||||
import tqdm
|
import tqdm
|
||||||
from filelock import FileLock
|
from filelock import FileLock
|
||||||
|
|
||||||
from transformers import (
|
from transformers import DataProcessor, PreTrainedTokenizer, is_tf_available, is_torch_available
|
||||||
DataProcessor,
|
|
||||||
PreTrainedTokenizer,
|
|
||||||
RobertaTokenizer,
|
|
||||||
RobertaTokenizerFast,
|
|
||||||
XLMRobertaTokenizer,
|
|
||||||
is_tf_available,
|
|
||||||
is_torch_available,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
@ -106,7 +98,6 @@ if is_torch_available():
|
|||||||
evaluate: bool = False,
|
evaluate: bool = False,
|
||||||
):
|
):
|
||||||
processor = hans_processors[task]()
|
processor = hans_processors[task]()
|
||||||
output_mode = hans_output_modes[task]
|
|
||||||
|
|
||||||
cached_features_file = os.path.join(
|
cached_features_file = os.path.join(
|
||||||
data_dir,
|
data_dir,
|
||||||
@ -127,22 +118,12 @@ if is_torch_available():
|
|||||||
logger.info(f"Creating features from dataset file at {data_dir}")
|
logger.info(f"Creating features from dataset file at {data_dir}")
|
||||||
label_list = processor.get_labels()
|
label_list = processor.get_labels()
|
||||||
|
|
||||||
if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
|
|
||||||
RobertaTokenizer,
|
|
||||||
RobertaTokenizerFast,
|
|
||||||
XLMRobertaTokenizer,
|
|
||||||
):
|
|
||||||
# HACK(label indices are swapped in RoBERTa pretrained model)
|
|
||||||
label_list[1], label_list[2] = label_list[2], label_list[1]
|
|
||||||
examples = (
|
examples = (
|
||||||
processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
|
processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
|
||||||
)
|
)
|
||||||
|
|
||||||
logger.info("Training examples: %s", len(examples))
|
logger.info("Training examples: %s", len(examples))
|
||||||
# TODO clean up all this to leverage built-in features of tokenizers
|
self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
|
||||||
self.features = hans_convert_examples_to_features(
|
|
||||||
examples, label_list, max_seq_length, tokenizer, output_mode
|
|
||||||
)
|
|
||||||
logger.info("Saving features into cached file %s", cached_features_file)
|
logger.info("Saving features into cached file %s", cached_features_file)
|
||||||
torch.save(self.features, cached_features_file)
|
torch.save(self.features, cached_features_file)
|
||||||
|
|
||||||
@ -174,21 +155,10 @@ if is_tf_available():
|
|||||||
evaluate: bool = False,
|
evaluate: bool = False,
|
||||||
):
|
):
|
||||||
processor = hans_processors[task]()
|
processor = hans_processors[task]()
|
||||||
output_mode = hans_output_modes[task]
|
|
||||||
label_list = processor.get_labels()
|
label_list = processor.get_labels()
|
||||||
|
|
||||||
if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
|
|
||||||
RobertaTokenizer,
|
|
||||||
RobertaTokenizerFast,
|
|
||||||
XLMRobertaTokenizer,
|
|
||||||
):
|
|
||||||
# HACK(label indices are swapped in RoBERTa pretrained model)
|
|
||||||
label_list[1], label_list[2] = label_list[2], label_list[1]
|
|
||||||
|
|
||||||
examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
|
examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
|
||||||
self.features = hans_convert_examples_to_features(
|
self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
|
||||||
examples, label_list, max_seq_length, tokenizer, output_mode
|
|
||||||
)
|
|
||||||
|
|
||||||
def gen():
|
def gen():
|
||||||
for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
|
for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
|
||||||
@ -240,15 +210,6 @@ if is_tf_available():
|
|||||||
class HansProcessor(DataProcessor):
|
class HansProcessor(DataProcessor):
|
||||||
"""Processor for the HANS data set."""
|
"""Processor for the HANS data set."""
|
||||||
|
|
||||||
def get_example_from_tensor_dict(self, tensor_dict):
|
|
||||||
"""See base class."""
|
|
||||||
return InputExample(
|
|
||||||
tensor_dict["idx"].numpy(),
|
|
||||||
tensor_dict["premise"].numpy().decode("utf-8"),
|
|
||||||
tensor_dict["hypothesis"].numpy().decode("utf-8"),
|
|
||||||
str(tensor_dict["label"].numpy()),
|
|
||||||
)
|
|
||||||
|
|
||||||
def get_train_examples(self, data_dir):
|
def get_train_examples(self, data_dir):
|
||||||
"""See base class."""
|
"""See base class."""
|
||||||
return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
|
||||||
@ -277,11 +238,7 @@ class HansProcessor(DataProcessor):
|
|||||||
|
|
||||||
|
|
||||||
def hans_convert_examples_to_features(
|
def hans_convert_examples_to_features(
|
||||||
examples: List[InputExample],
|
examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,
|
||||||
label_list: List[str],
|
|
||||||
max_length: int,
|
|
||||||
tokenizer: PreTrainedTokenizer,
|
|
||||||
output_mode: str,
|
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
Loads a data file into a list of ``InputFeatures``
|
Loads a data file into a list of ``InputFeatures``
|
||||||
@ -313,19 +270,8 @@ def hans_convert_examples_to_features(
|
|||||||
pad_to_max_length=True,
|
pad_to_max_length=True,
|
||||||
return_overflowing_tokens=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 poping question + options,"
|
|
||||||
"you need to try to use a bigger max seq length!"
|
|
||||||
)
|
|
||||||
|
|
||||||
if output_mode == "classification":
|
|
||||||
label = label_map[example.label] if example.label in label_map else 0
|
label = label_map[example.label] if example.label in label_map else 0
|
||||||
elif output_mode == "regression":
|
|
||||||
label = float(example.label)
|
|
||||||
else:
|
|
||||||
raise KeyError(output_mode)
|
|
||||||
|
|
||||||
pairID = int(example.pairID)
|
pairID = int(example.pairID)
|
||||||
|
|
||||||
@ -346,7 +292,3 @@ hans_tasks_num_labels = {
|
|||||||
hans_processors = {
|
hans_processors = {
|
||||||
"hans": HansProcessor,
|
"hans": HansProcessor,
|
||||||
}
|
}
|
||||||
|
|
||||||
hans_output_modes = {
|
|
||||||
"hans": "classification",
|
|
||||||
}
|
|
||||||
|
Loading…
Reference in New Issue
Block a user