fixed multiplechoice tokenization (#12362)

* fixed multiplechoice tokenization

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

* removed outer brackets for proper sequence generation

src/transformers/file_utils.py

@@ -816,7 +816,7 @@ PT_MULTIPLE_CHOICE_SAMPLE = r"""
         >>> 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)
+        >>> 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
 
         >>> # the linear classifier still needs to be trained