model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
```
At this point, only three steps remain:
Define your training hyperparameters in [TrainingArguments].
Pass the training arguments to a [Trainer] along with the model, dataset, tokenizer, and data collator.
Call [Trainer.train()] to fine-tune your model.
```python
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
    output_dir="./results",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=5,
    weight_decay=0.01,
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_imdb["train"],
    eval_dataset=tokenized_imdb["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
