48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
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from transformers import Trainer, TrainingArguments, DataCollatorForSeq2Seq
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class TrainerManager:
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def __init__(self, config, model, tokenizer, train_dataset, eval_dataset):
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self.config = config
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self.model = model
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self.tokenizer = tokenizer
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self.train_dataset = train_dataset
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self.eval_dataset = eval_dataset
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def create_trainer(self):
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args = TrainingArguments(
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output_dir=self.config.output_dir,
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per_device_train_batch_size=self.config.per_device_train_batch_size,
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gradient_accumulation_steps=self.config.gradient_accumulation_steps,
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num_train_epochs=self.config.num_train_epochs,
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learning_rate=self.config.learning_rate,
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lr_scheduler_type=self.config.lr_scheduler_type,
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warmup_steps=self.config.warmup_steps,
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logging_steps=self.config.logging_steps,
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save_steps=self.config.save_steps,
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evaluation_strategy=self.config.evaluation_strategy,
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eval_steps=self.config.eval_steps,
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save_strategy=self.config.save_strategy,
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save_total_limit=self.config.save_total_limit,
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bf16=True,
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report_to="none",
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remove_unused_columns=False,
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deepspeed=self.config.deepspeed,
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load_best_model_at_end=True,
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metric_for_best_model="eval_loss",
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greater_is_better=False,
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)
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collator = DataCollatorForSeq2Seq(self.tokenizer, model=self.model, padding=True)
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from callbacks import build_callbacks
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trainer = Trainer(
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model=self.model,
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args=args,
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train_dataset=self.train_dataset,
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eval_dataset=self.eval_dataset,
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data_collator=collator,
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callbacks=build_callbacks(self.config),
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)
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return trainer
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