| training_args = TrainingArguments( | |
| output_dir="my_awesome_model", | |
| learning_rate=2e-5, | |
| per_device_train_batch_size=16, | |
| per_device_eval_batch_size=16, | |
| num_train_epochs=2, | |
| weight_decay=0.01, | |
| evaluation_strategy="epoch", | |
| save_strategy="epoch", | |
| load_best_model_at_end=True, | |
| push_to_hub=True, | |
| ) | |
| trainer = Trainer( | |
| model=model, | |
| args=training_args, | |
| train_dataset=tokenized_imdb["train"], | |
| eval_dataset=tokenized_imdb["test"], | |
| tokenizer=tokenizer, | |
| data_collator=data_collator, | |
| compute_metrics=compute_metrics, | |
| ) | |
| trainer.train() | |
| [Trainer] applies dynamic padding by default when you pass tokenizer to it. |