DistilBertForQuestionAnswering returns [UNK]
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...):Distilbert

Language I am using the model on (English, Chinese ...):English

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [X] my own modified scripts: (give details below)

The tasks I am working on is:
* [X] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce
Distilber for question answering is return a sentence of [UNK]

Steps to reproduce the behavior:
Code snippet I am using :
```python
import torch 
from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad')
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')

torch_device = "cuda" if torch.cuda.is_available() else "cpu"


def answer(question, text):
    input_dict = tokenizer.encode_plus(
        question, text, return_tensors='pt', max_length=512)
    input_ids = input_dict["input_ids"].to('cpu')
    start_scores, end_scores = model(input_ids)

    start = torch.argmax(start_scores)
    end = torch.argmax(end_scores)

    all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    answer = ''.join(all_tokens[start: end + 1]).replace('▁', ' ').strip()
    answer = answer.replace('[SEP]', '')
    return answer if answer != '[CLS]' and len(answer) != 0 else 'could not find an answer'

```

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## Expected behavior
Using the Albert is giving a good results, but DistilBert tokenizer is not working.
<!-- A clear and concise description of what you would expect to happen. -->

## 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: 2.2.1
- Platform: Linux 18.04
- Python version: 3.6.9
- PyTorch version (GPU?): pytorch-pretrained-bert==0.6.2
- Tensorflow version (GPU?):NO
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

Solved : Had to update transformers & torch
this code is working fine :
```python
from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering
import torch

tokenizer = DistilBertTokenizer.from_pretrained(
    'distilbert-base-uncased-distilled-squad')
model = DistilBertForQuestionAnswering.from_pretrained(
    'distilbert-base-uncased-distilled-squad')
input_ids = torch.tensor(tokenizer.encode(
    question, corpus, max_length=256, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(input_ids, start_positions=start_positions,
                end_positions=end_positions)
loss, start_scores, end_scores = outputs[:3]

all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
answer = tokenizer.convert_tokens_to_string(
    all_tokens[torch.argmax(start_scores): torch.argmax(end_scores) + 1])
print('*************')
print(answer)
```