HuggingsFace dataset example give error
Nice catch @donhuvy, that's because some models don't need the `token_type_ids`, as in this case, as the example is using `distilbert-base-cased`, and according to the DistilBert documentation at https://huggingface.co/transformers/v3.0.2/model_doc/distilbert.html, `DistilBert doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP])`. `token_type_ids` are neither required in some other well known models such as RoBERTa. 

Here the issue comes due to a mismatch between the tokenizer and the model, as the Colab is using a BERT tokenizer (`bert-base-cased`), while the model is a DistilBERT (`distilbert-base-cased`), so aligning the tokenizer and the model solves it! #self-assign @donhuvy I've created https://github.com/huggingface/datasets/pull/5902 to solve it! 🤗 This has been addressed in #5902.

The Quicktour notebook is deprecated now - please use the notebook version of the [Quickstart doc page](https://huggingface.co/docs/datasets/main/en/quickstart) instead ("Open in Colab" button).
### Describe the bug

![image](https://github.com/huggingface/datasets/assets/1328316/1f4f0086-3db9-4c79-906b-05a375357cce)


![image](https://github.com/huggingface/datasets/assets/1328316/733ebd3d-89b9-4ece-b80a-00ab5b0a4122)


### Steps to reproduce the bug

Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz

```python
# Now let's train our model
device = 'cuda' if torch.cuda.is_available() else 'cpu'

model.train().to(device)
for i, batch in enumerate(dataloader):
    batch.to(device)
    outputs = model(**batch)
    loss = outputs.loss
    loss.backward()
    optimizer.step()
    model.zero_grad()
    print(f'Step {i} - loss: {loss:.3}')
    if i > 5:
        break
```

Error

```python
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
[<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>()
      5 for i, batch in enumerate(dataloader):
      6     batch.to(device)
----> 7     outputs = model(**batch)
      8     loss = outputs.loss
      9     loss.backward()

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
   1499                 or _global_backward_pre_hooks or _global_backward_hooks
   1500                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501             return forward_call(*args, **kwargs)
   1502         # Do not call functions when jit is used
   1503         full_backward_hooks, non_full_backward_hooks = [], []

TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids'
```

https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156



### Expected behavior

Run success on Google Colab (free)

### Environment info

Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem)