batched=True to apply the preprocessing function to multiple elements of the dataset at once for faster
preprocessing:
python
tokenized_imdb = imdb.map(preprocess_function, batched=True)
Lastly, pad your text so they are a uniform length. While it is possible to pad your text in the tokenizer function
by setting padding=True, it is more efficient to only pad the text to the length of the longest element in its
batch. This is known as dynamic padding. You can do this with the DataCollatorWithPadding function:
```python
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
```
Fine-tune with the Trainer API
Now load your model with the [AutoModelForSequenceClassification] class along with the number of expected labels:
```python
from transformers import AutoModelForSequenceClassification
