Tokenizer Fast bug: ValueError: TextInputSequence must be str
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version:
- Platform: In a Colab enviroment aswell as on my local windows version
- Python version: 3.7.4
- PyTorch version (GPU?): Yes and No
- Tensorflow version (GPU?): I didn't try with tensorflow, but I suspect that it has nothing to do with it
- Using GPU in script?: I used the automodeling on a GPU session in Colab
- Using distributed or parallel set-up in script?: Nope

### Who can help
@mfuntowicz 

## Information
Model I am using: Initially Electra but I tested it out with BERT, DistilBERT and RoBERTa

It's using your scripts, but again, it believe it wouldn't work if I did it myself. The model is trained on SQuAD.

#### Error traceback
```
"""
Traceback (most recent call last):
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
    return list(map(*args))
  File "/usr/local/lib/python3.6/dist-packages/transformers/data/processors/squad.py", line 165, in squad_convert_example_to_features
    return_token_type_ids=True,
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py", line 2050, in encode_plus
    **kwargs,
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_fast.py", line 473, in _encode_plus
    **kwargs,
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_fast.py", line 376, in _batch_encode_plus
    is_pretokenized=is_split_into_words,
  File "/usr/local/lib/python3.6/dist-packages/tokenizers/implementations/base_tokenizer.py", line 212, in encode
    return self._tokenizer.encode(sequence, pair, is_pretokenized, add_special_tokens)
ValueError: TextInputSequence must be str
"""
```

## To reproduce
Steps to reproduce the behavior:

1. Download model and tokenizer (fast)
2. Test it out with the transformers pipeline for a question answering task

I've also made a small notebook to test it out for yourself.  [here](https://colab.research.google.com/drive/11_qK3w7OWBTYC_GdspAjFna2XJkBgODU?usp=sharing)

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## Expected behavior
Instead of giving an error, I would expect the tokenizer to work...
<!-- A clear and concise description of what you would expect to happen. -->
Hi, thanks for opening such a detailed issue with a notebook!

Unfortunately, fast tokenizers don’t currently work with the QA pipeline. They will in the second pipeline version which is expected in a few weeks to a few months, but right now please use the slow tokenizers for the QA pipeline. 

Thanks!<|||||>I think the issue is still there.<|||||>Please open a new issue with your environment, an example of what the issue is and how you expect it to work. Thank you.<|||||>> Hi, thanks for opening such a detailed issue with a notebook!
> 
> Unfortunately, fast tokenizers don’t currently work with the QA pipeline. They will in the second pipeline version which is expected in a few weeks to a few months, but right now please use the slow tokenizers for the QA pipeline.
> 
> Thanks!


~and how do I do that? I don't understand the difference from slow and fast tokenizers. Do I need to train my tokenizer again, or can I just somehow "cast" the fast into the slow version?~


I could fix this simply by changing:


    from transformers import RobertaTokenizerFast 
    tokenizer = RobertaTokenizerFast

to:

    from transformers import RobertaTokenizer 
    tokenizer = RobertaTokenizer<|||||>I also find this problem when using transformers. I check my data and find that if csv file contains much Null data or the length of str is 0, this error will be returned. I filter these data and I can successfully run my code.<|||||>double check the data and make sure there is no nan in your data, this is the problem i encountered