Question Answering Documentation Example Bug
## 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: 3.5.0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.6.12
- PyTorch version (GPU?): 1.7.0 (False)
- Tensorflow version (GPU?): 2.3.1 (False)
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: parallel


### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.
 
 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten @TevenLeScao
 Blenderbot: @patrickvonplaten
 Bart: @patrickvonplaten
 Marian: @patrickvonplaten
 Pegasus: @patrickvonplaten
 mBART: @patrickvonplaten
 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
 TransfoXL/XLNet: @TevenLeScao
 RAG: @patrickvonplaten, @lhoestq
 FSMT: @stas00
 examples/seq2seq: @patil-suraj
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 --> @sgugger 

## Information

Model I am using (Bert, XLNet ...): bert-large-uncased-whole-word-masking-finetuned-squad

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

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

## To reproduce

Steps to reproduce the behavior:

Trying to run this [example script](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) for TF, I kept on getting error:

```
InvalidArgumentError: Value for attr 'T' of string is not in the list of allowed values: float, double, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool
	; NodeDef: {{node ArgMax}}; Op<name=ArgMax; signature=input:T, dimension:Tidx -> output:output_type; attr=T:type,allowed=[DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, ..., DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_BOOL]; attr=Tidx:type,default=DT_INT32,allowed=[DT_INT32, DT_INT64]; attr=output_type:type,default=DT_INT64,allowed=[DT_INT32, DT_INT64]> [Op:ArgMax]
```
at the line where` tf.argmax()` is called on `answer_start_scores` and `answer_end_scores`


## Expected behavior

This error is normal since `type(answer_start_scores)` is `str`, so I propose the following amendment to the documentation:
from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
```
import tensorflow as tf
tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad", return_dict=True)
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 are available in 🤗 Transformers?",
    "What does 🤗 Transformers provide?",
    "🤗 Transformers provides interoperability between which frameworks?",
]
for question in questions:
    inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")
    input_ids = inputs["input_ids"].numpy()[0]
    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
    output = model(inputs)
    answer_start = tf.argmax(
        output.start_logits, axis=1
    ).numpy()[0]  # Get the most likely beginning of answer with the argmax of the score
    answer_end = (
        tf.argmax(output.end_logits, axis=1) + 1
    ).numpy()[0]  # Get the most likely end of answer with the argmax of the score
    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
    print(f"Question: {question}")
    print(f"Answer: {answer}")
```

Best,
Irem
Hi! That sounds good, do you want to open a PR?<|||||>Yes, I am on it. Thanks!
<|||||>+1
any updates?<|||||>@iremnasir can you please let me know how exactly you managed to solve it?  I am having the same issue. 
![Screenshot 2020-12-07 at 00 56 45](https://user-images.githubusercontent.com/28517335/101344563-ae506700-38ab-11eb-9e12-98abe95e238c.png)
<|||||>Hi, you can see my answer above for the solution in Expected Behavior section<|||||>Got it. Thanks

As  got the error for AutoTokenizer: `NameError: name 'AutoTokenizer' is not defined `
Just imported all:
`from transformers import * ` earlier I was only importing `pipeline` from transformers<|||||>This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.

If you think this still needs to be addressed please comment on this thread.<|||||>Thanks @iremnasir, it worked. Why hasn't this been merged yet?