| training_args = TrainingArguments( | |
| output_dir="my_awesome_mind_model", | |
| evaluation_strategy="epoch", | |
| save_strategy="epoch", | |
| learning_rate=3e-5, | |
| per_device_train_batch_size=32, | |
| gradient_accumulation_steps=4, | |
| per_device_eval_batch_size=32, | |
| num_train_epochs=10, | |
| warmup_ratio=0.1, | |
| logging_steps=10, | |
| load_best_model_at_end=True, | |
| metric_for_best_model="accuracy", | |
| push_to_hub=True, | |
| ) | |
| trainer = Trainer( | |
| model=model, | |
| args=training_args, | |
| train_dataset=encoded_minds["train"], | |
| eval_dataset=encoded_minds["test"], | |
| tokenizer=feature_extractor, | |
| compute_metrics=compute_metrics, | |
| ) | |
| trainer.train() | |
| Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: | |
| trainer.push_to_hub() | |
| For a more in-depth example of how to finetune a model for audio classification, take a look at the corresponding PyTorch notebook. |