tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
Finally, pad your text and labels, so they are a uniform length:
```python
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer)
```
Fine-tune with the Trainer API
Load your model with the [AutoModelForTokenClassification] class along with the number of expected labels:
```python
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
```
Gather your training arguments in [TrainingArguments]:
python
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
