If you want to fine-tune your model and regularly report the evaluation metrics (for instance at the end of each
epoch), here is how you should define your training arguments:
```python
from transformers import TrainingArguments
training_args = TrainingArguments("test_trainer", evaluation_strategy="epoch")
```
See the documentation of [TrainingArguments] for more options.
Fine-tuning with Keras
Models can also be trained natively in TensorFlow using the Keras API. First, let's define our model:
```python
import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
```
Then we will need to convert our datasets from before in standard tf.data.Dataset. Since we have fixed shapes,
