| training_args = Seq2SeqTrainingArguments( | |
| output_dir="my_awesome_opus_books_model", | |
| evaluation_strategy="epoch", | |
| learning_rate=2e-5, | |
| per_device_train_batch_size=16, | |
| per_device_eval_batch_size=16, | |
| weight_decay=0.01, | |
| save_total_limit=3, | |
| num_train_epochs=2, | |
| predict_with_generate=True, | |
| fp16=True, | |
| push_to_hub=True, | |
| ) | |
| trainer = Seq2SeqTrainer( | |
| model=model, | |
| args=training_args, | |
| train_dataset=tokenized_books["train"], | |
| eval_dataset=tokenized_books["test"], | |
| tokenizer=tokenizer, | |
| data_collator=data_collator, | |
| 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() | |
| If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! |