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| from transformers import Trainer, TrainingArguments | |
| def get_training_args(output_dir="outputs/model"): | |
| return TrainingArguments( | |
| output_dir=output_dir, | |
| evaluation_strategy="epoch", | |
| save_strategy="epoch", | |
| learning_rate=2e-5, | |
| per_device_train_batch_size=16, | |
| per_device_eval_batch_size=16, | |
| num_train_epochs=3, | |
| weight_decay=0.01, | |
| logging_dir="outputs/logs", | |
| logging_steps=10, | |
| load_best_model_at_end=True, | |
| metric_for_best_model="f1" | |
| ) | |
| def train_model(model, args, train_dataset, val_dataset, compute_metrics): | |
| trainer = Trainer( | |
| model=model, | |
| args=args, | |
| train_dataset=train_dataset, | |
| eval_dataset=val_dataset, | |
| compute_metrics=compute_metrics | |
| ) | |
| trainer.train() | |
| return trainer | |