mirror of
https://github.com/FlagOpen/FlagEmbedding.git
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96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
import logging
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import os
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from pathlib import Path
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from transformers import AutoConfig, AutoTokenizer, TrainingArguments
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from transformers import (
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HfArgumentParser,
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set_seed,
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)
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from .arguments import ModelArguments, DataArguments
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from .data import TrainDatasetForCE, GroupCollator
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from .modeling import CrossEncoder
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from .trainer import CETrainer
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logger = logging.getLogger(__name__)
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def main():
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parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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model_args: ModelArguments
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data_args: DataArguments
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training_args: TrainingArguments
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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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logger.info("Model parameters %s", model_args)
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logger.info("Data parameters %s", data_args)
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set_seed(training_args.seed)
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num_labels = 1
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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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use_fast=False,
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)
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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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cache_dir=model_args.cache_dir,
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)
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_model_class = CrossEncoder
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model = _model_class.from_pretrained(
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model_args, data_args, training_args,
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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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train_dataset = TrainDatasetForCE(data_args, tokenizer=tokenizer)
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_trainer_class = CETrainer
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trainer = _trainer_class(
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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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data_collator=GroupCollator(tokenizer),
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tokenizer=tokenizer
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)
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Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)
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trainer.train()
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trainer.save_model()
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if __name__ == "__main__":
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main()
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