[Trainer] or the flags to activate the different training options it supports. Let's begin by
using all the defaults, the only thing we then have to provide is a directory in which the checkpoints will be saved:
```python
from transformers import TrainingArguments
training_args = TrainingArguments("test_trainer")
```
Then we can instantiate a [Trainer] like this:
```python
from transformers import Trainer
trainer = Trainer(model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset)
```
To fine-tune our model, we just need to call
python
trainer.train()
which will start a training that you can follow with a progress bar, which should take a couple of minutes to complete
(as long as you have access to a GPU). It won't actually tell you anything useful about how well (or badly) your model
