DebertaForSequenceClassification documents  examples  report RuntimeError: Index tensor must have the same number of dimensions as input tensor
## Environment info
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- `transformers` version:
- Platform: macos
- Python version: 3.8.3
- PyTorch version (GPU?): no
- Tensorflow version (GPU?): no 
- Using GPU in script?: no 
- Using distributed or parallel set-up in script?: no

### Who can help
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @jplu

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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## Information

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

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

    from transformers import DebertaTokenizer, DebertaForSequenceClassification
    import torch
    tokenizer = DebertaTokenizer.from_pretrained('microsoft/deberta-base')
    model = DebertaForSequenceClassification.from_pretrained('microsoft/deberta-base')
    inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    labels = torch.tensor([1]).unsqueeze(0) 
    outputs = model(**inputs, labels=labels)
    loss = outputs.loss
    logits = outputs.logits
    print(loss)
    print(logits)


Exception like below: 

Traceback (most recent call last):
  File "/Users/admin/git/transformers/myexample4/deberta_MLM.py", line 65, in <module>
    sequence_classify()
  File "/Users/admin/git/transformers/myexample4/deberta_MLM.py", line 45, in sequence_classify
    outputs = model(**inputs, labels=labels)
  File "/Users/admin/virtulenv/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/admin/git/transformers/src/transformers/models/deberta/modeling_deberta.py", line 1169, in forward
    labels = torch.gather(labels, 0, label_index.view(-1))
RuntimeError: Index tensor must have the same number of dimensions as input tensor

Process finished with exit code 1


Steps to reproduce the behavior:

1.
2.
3.

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

You shouldn't unsqueeze your labels, because the `labels` should just be a tensor of shape `(batch_size,)`. 