    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)
Collect your model, training arguments, dataset, data collator, and tokenizer in [Trainer]:
python
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_wnut["train"],
    eval_dataset=tokenized_wnut["test"],
    data_collator=data_collator,
    tokenizer=tokenizer,
)
Fine-tune your model:
python
trainer.train()
Fine-tune with TensorFlow
Batch your examples together and pad your text and labels, so they are a uniform length:
```python
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")
```
Convert your datasets to the tf.data.Dataset format with to_tf_dataset:
```python
tf_train_set = tokenized_wnut["train"].to_tf_dataset(
