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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! |