QA Pipeline: Key Error due to predicting a token outside of allowed context
# 🐛 Bug

## Information

Model: distilbert
Language: English
The problem arises when using: QA inference via `pipeline`

The pipeline throws an exception when the model predicts a token that is not part of the document (e.g. final special token).
In the example below, the model predicts token 13 to be the end of the answer span. 
The context however ends at token 12 and token 13 is the final [SEP] token. Therefore, we get a key error when trying to access 
`feature.token_to_orig_map[13])` in here: 

https://github.com/huggingface/transformers/blob/ce374ba87767d551f720242d5e64bfa976531079/src/transformers/pipelines.py#L1370-L1380

## To reproduce

```
nlp = pipeline("question-answering",model="distilbert-base-uncased-distilled-squad",
                                  tokenizer="distilbert-base-uncased",
                                  device=-1)

nlp(question="test finding", context="My name is Carla and I live in Berlin")
```

results in 

```
Traceback (most recent call last):
  File "/home/mp/deepset/dev/haystack/debug.py", line 16, in <module>
    nlp(question="test finding", context="My name is Carla and I live in Berlin")
  File "/home/mp/miniconda3/envs/py37/lib/python3.7/site-packages/transformers/pipelines.py", line 1316, in __call__
    for s, e, score in zip(starts, ends, scores)
  File "/home/mp/miniconda3/envs/py37/lib/python3.7/site-packages/transformers/pipelines.py", line 1316, in <listcomp>
    for s, e, score in zip(starts, ends, scores)
KeyError: 13
```

## Expected behavior

Predictions that are pointing to tokens that are not part of the "context" (here: the last [SEP] token) should be filtered out from possible answers. 

## Environment info

     
- `transformers` version: 3.0.2
- Platform: Ubuntu 18.04
- Python version: 3.7.6
- PyTorch version (GPU?): 1.5.1, CPU
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

Hi @tholor, 

Thanks for reporting the issue. 

We did have an issue where predictions were going out of bounds on QA pipeline and it has been fixed on master: 

```python
>>> nlp = pipeline("question-answering",model="distilbert-base-uncased-distilled-squad",
                                  tokenizer="distilbert-base-uncased",
                                  device=-1)

>>> nlp(question="test finding", context="My name is Carla and I live in Berlin")
>>> {'score': 0.41493675112724304, 'start': 11, 'end': 16, 'answer': 'Carla'}
```

If you are able to checkout from master branch I would be happy to hear back from you to make sure it's working as expected on your side as well.

Let us know 😃 
Morgan<|||||>Hi @mfuntowicz , 
Works like a charm now. Thanks for the fix!