| from transformers import TrainingArguments | |
| training_args = TrainingArguments( | |
| output_dir="your-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, | |
| ) | |
| Pass training_args to the [Trainer] along with a model, dataset, something to preprocess the dataset with (depending on your data type it could be a tokenizer, feature extractor or image processor), a data collator, and a function to compute the metrics you want to track during training. |