training_args = TrainingArguments(
     output_dir="my_awesome_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,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_imdb["train"],
     eval_dataset=tokenized_imdb["test"],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

[Trainer] applies dynamic padding by default when you pass tokenizer to it.