| training_args = TrainingArguments( | |
| output_dir="my_awesome_eli5_clm-model", | |
| evaluation_strategy="epoch", | |
| learning_rate=2e-5, | |
| weight_decay=0.01, | |
| push_to_hub=True, | |
| ) | |
| trainer = Trainer( | |
| model=model, | |
| args=training_args, | |
| train_dataset=lm_dataset["train"], | |
| eval_dataset=lm_dataset["test"], | |
| data_collator=data_collator, | |
| ) | |
| trainer.train() | |
| Once training is completed, use the [~transformers.Trainer.evaluate] method to evaluate your model and get its perplexity: | |
| import math | |
| eval_results = trainer.evaluate() | |
| print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") | |
| Perplexity: 49.61 | |
| Then 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! |