Cannot use the RobertaForMultipleChoice model for processing multiple choice questions with 4 options
Hello,

When I try to use the `RobertaForMultipleChoice` pretrained model with the code below, it generates an error:

```python
from transformers import RobertaTokenizer, RobertaForMultipleChoice
import torch
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMultipleChoice.from_pretrained('roberta-base')
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."
choice2 = "It is eathen with a napkin."
choice3 = "It is not eatable."
labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
encoding = tokenizer([[prompt, prompt,prompt,prompt], [choice0, choice1, choice2, choice3]], return_tensors='pt', return_token_type_ids=True,padding=True)
```

The error message is:

```python

  File "/Users/hyunjindominiquecho/opt/anaconda3/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 534, in _batch_encode_plus
    ids, pair_ids = ids_or_pair_ids

ValueError: too many values to unpack (expected 2)
```

What am I doing wrong here? Thank you.


This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>Good question. `xxxForMultipleChoice` models are actually a bit tricky. The way you should provide the data to the tokenizer is as follows:

```
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."
choice2 = "It is eathen with a napkin."
choice3 = "It is not eatable."

encoded_dict = bert_tokenizer([prompt, prompt, prompt, prompt], 
                                 [choice0, choice1, choice2, choice3], 
                                return_tensors='pt', 
                                padding='max_length')
```
Note the difference: we provide 2 lists to the tokenizer rather than one. The first list contains the first sequence of every training example, the second list contains the second sequence. Every training example will then be encoded as `[CLS] prompt [SEP] choice x [SEP]`.

For more details, see [here](https://github.com/huggingface/transformers/issues/7701#issuecomment-707149546).