Documentation error in question-answering pipeline
Hi,
The [QuestionAnsweringPipeline](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=pipelines#transformers.QuestionAnsweringPipeline.__call__) is returning the start and end position of the context string and not according to "the tokenized version of the input." as mentioned in the doc.

```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
qa_pipeline = pipeline("question-answering")
text = r"""
🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
TensorFlow 2.0 and PyTorch.
"""

questions = [
    "How many pretrained models?"
]

result = qa_pipeline(question=questions, context=text)
print(result)

#this is the correct answer
print(text[int(result["start"]):int(result["end"])])

#this is not correct
print(tokenizer.tokenize(text[int(result["start"]):int(result["end"])))
```

Sorry if I misunderstood the doc
You are correct! Do you want to open a PR fixing the docs?<|||||>Sure, I'll do that. Closing this issue for now, I'll refer to this in the PR.<|||||>There is one more thing, sorry I didn't realize this earlier, and please let me know if I am wrong, The [span_to_answer](https://huggingface.co/transformers/main_classes/pipelines.html#transformers.QuestionAnsweringPipeline.span_to_answer) method of this pipeline object doesn't add much value, because the object itself when called with `question` and `context` will return the start and end indexes of the answer in the context. Moreover, it will give the wrong results because `span_to_answer` is expecting token index and not string indexes.
In continuation to above code;
```python
print(len(tokenizer.tokenize(text)))
# output: 96

 print(qa_pipeline.span_to_answer(text=text,start=int(result[0]["start"]), end=int(result[0]["end"])))
# this will print {'answer': '', 'start': 0, 'end': 0} because the start index of the answer is 256 and end index is 264 while the 
# tokenized length is 96, this line in the function will stop the loop
# if token_idx > end:
#                break

```
This part in `__call__` [method](https://huggingface.co/transformers/main_classes/pipelines.html#transformers.QuestionAnsweringPipeline.__call__) is already taking care of remapping to string indexes:
```python
             # Convert the answer (tokens) back to the original text
                answers += [
                    {
                        "score": score.item(),
                        "start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
                        "end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
                        "answer": " ".join(
                            example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
                        ),
                    }
```
Unless we have some method to get the token index, this method I think will not work.
<|||||>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.
