fixed multiplechoice tokenization
# What does this PR do?

The model would have seen two sequences:
1. [CLS]prompt[SEP]prompt[SEP]
2. [CLS]choice0[SEP]choice1[SEP]

That is not correct as we want a contextualized embedding of prompt and choice.

This PR fixes the documentation



## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Documentation: @sgugger
Thanks for your PR but the documentation is correct: when tokenizing several pairs of sentences, the tokenizer API takes the list of first sentences, then the list of second sentences. Here the first sentences are the prompt (twice) and the second sentences are the choices.<|||||>@sgugger that doesn't seem to be correct:
```python
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, return_attention_mask=False, return_token_type_ids=False)
print(tokenizer.decode(encoding.input_ids[0]))
print(tokenizer.decode(encoding.input_ids[1]))
```
Output:
```
[CLS] in italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. [SEP] in italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. [SEP]
[CLS] it is eaten with a fork and a knife. [SEP] it is eaten while held in the hand. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
```<|||||>An alternative fix is to:
```python
encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors='pt', padding=True, return_attention_mask=False, return_token_type_ids=False)
print(tokenizer.decode(encoding.input_ids[0]))
print(tokenizer.decode(encoding.input_ids[1]))
```
Output:
```
[CLS] in italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. [SEP] it is eaten with a fork and a knife. [SEP]
[CLS] in italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. [SEP] it is eaten while held in the hand. [SEP] [PAD]
```
The problem is you are currently not giving the tokenizer two lists. You are only giving him one list (first sentence).<|||||>Yes your second fix is the good one! I missed the extra pair of brackets.<|||||>@sgugger: pushed!<|||||>Thanks a lot!