[docs]Incorrect way of input encoding for "multiple choice" models in documentation?
Author: Riroaki
Pull request: no

In the documentation about "xxForMultipleChoice" models like BERT, ALBERT, RoBERTa, the examples goes like [this](https://huggingface.co/transformers/model_doc/bert.html#bertformultiplechoice):
```
>>> from transformers import BertTokenizer, BertForMultipleChoice
>>> import torch

>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> model = BertForMultipleChoice.from_pretrained('bert-base-uncased')

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

>>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k,v in encoding.items()}, labels=labels)  # batch size is 1
...
```
In current version (4.5.1), the `encoding` actually consists of 2 sentences: `[prompt + prompt]`, and `[choice0 + choice1]`, which to my knowledge is incorrect, as each encoded sentence should include one prompt and one choice. I think the `encoding` supposed to be like:
```
tokenizer([[prompt, choice0], [prompt, choice1]], return_tensors='pt', padding=True)
```
So, is there anything wrong?

Comments:
@Riroaki I agree with you! It should be:
```python
tokenizer([[prompt, choice0], [prompt, choice1]], return_tensors='pt', padding=True)
```

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@sgugger @SBrandeis 
Please have a look at the example scripts.🙏

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.

I'm running into this issue as well.

I'm not super familiar working with multiple choice models, but I think that given #6074, and the [run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py) example, these should be passed as two lists, instead of one.

In other words, in the example, instead of

```python
encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
```

it should be 

```python
encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors='pt', padding=True)
```

It would be awesome to make this two-character-deletion change, as it just tripped me up when starting working on a multiple choice model!

This has been fixed, but you need to switch to the master documentation to see the change.