```
Compile the model:
```python
import tensorflow as tf
model.compile(optimizer=optimizer)
```
Finally, fine-tune the model by calling model.fit:
python
model.fit(
    tf_train_set,
    validation_data=tf_validation_set,
    epochs=num_train_epochs,
)
Token classification with WNUT emerging entities
Token classification refers to the task of classifying individual tokens in a sentence. One of the most common token
classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence,
such as a person, location, or organization. In this example, learn how to fine-tune a model on the WNUT 17 dataset to detect new entities.
For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
PyTorch notebook
