Issue with XLNet using xlnet-base-cased
# 🐛 Bug

## Information

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

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

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:
* [x] an official GLUE/SQUaD task: SQUaD v1.1
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Clone the repository
```
!git clone https://github.com/huggingface/transformers.git
```
2.
```
!python ./transformers/examples/question-answering/run_squad.py \
    --model_type xlnet \
    --model_name_or_path xlnet-base-cased \
    --do_train \
    --do_eval \
    --train_file $SQuAD_Dir/train-v1.1.json \
    --predict_file $SQuAD_Dir/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./model_output \
    --per_gpu_eval_batch_size=4  \
    --per_gpu_train_batch_size=4   \
    --save_steps 5000
```
3. Error
```
Epoch:   0% 0/2 [00:00<?, ?it/s]
Iteration:   0% 0/15852 [00:00<?, ?it/s]Traceback (most recent call last):
  File "./transformers/examples/question-answering/run_squad.py", line 830, in <module>
    main()
  File "./transformers/examples/question-answering/run_squad.py", line 769, in main
    global_step, tr_loss = train(args, train_dataset, model, tokenizer)
  File "./transformers/examples/question-answering/run_squad.py", line 204, in train
    outputs = model(**inputs)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
TypeError: forward() got an unexpected keyword argument 'cls_index'
```

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## Expected behavior

The model should start training from the first epoch.
<!-- 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: 2.9.0
- Platform: Google Colab Pro
- Python version: 3.6.9
- PyTorch version (GPU?): 1.5.0+cu101 (Yes)
- Tensorflow version (GPU?): 2.2.0 (Yes)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

I also have the same problem @alexandrenriq <|||||>I think there's a mistake in the code which sets the `AutoModelForQuestionAnswering` as `XLNetForQuestionAnsweringSimple`. You can run the code by substituting `AutoModelForQuestionAnswering` into `XLNetForQuestionAnswering` (or by removing `cls_index` and `p_mask` in the batches to make `XLNetForQuestionAnsweringSimple` work). 

Nevertheless, after the training is done, I can't reproduce the scores right (`Results: {'exact': 0.03784295175023652, 'f1': 0.6317807409886281,...`).<|||||>I also found the same issue, and I believe this issue appears for a very very long time. <|||||>see also #3535<|||||>> see also #3535

@brettkoonce I saw it, it didn't 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.
