    columns=["attention_mask", "input_ids", "labels"],
    shuffle=True,
    batch_size=16,
    collate_fn=data_collator,
)
tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
    columns=["attention_mask", "input_ids", "labels"],
    shuffle=False,
    batch_size=16,
    collate_fn=data_collator,
)
```
Load the model with the [TFAutoModelForTokenClassification] class along with the number of expected labels:
```python
from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
```
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```python
from transformers import create_optimizer
batch_size = 16
num_train_epochs = 3
num_train_steps = (len(tokenized_datasets["train"]) // batch_size) * num_train_epochs
