Cannot run regression with DebertaV2ForSequenceClassification and DebertaForSequenceClassification
Author: mingfengwan
Pull request: no

## Environment info
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     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.11.2
- Platform: Linux-4.15.0-154-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.4
- PyTorch version (GPU?): 1.9.1+cu102 (True)
- Tensorflow version (GPU?): 2.2.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: tried both CPU and GPU
- Using distributed or parallel set-up in script?: tried both

## Information

Model I am using (Bert, XLNet ...): DebertaV2ForSequenceClassification, DebertaForSequenceClassification

The problem arises when using:
* [x] my own modified scripts: (give details below)


The tasks I am working on is:
Regression with DebertaForSequenceClassification on my own dataset

## To reproduce

Steps to reproduce the behavior:
Basically use the example script but set num_labels=1
```
from transformers import DebertaTokenizer, DebertaForSequenceClassification
import torch
tokenizer = DebertaTokenizer.from_pretrained('microsoft/deberta-base')
model = DebertaForSequenceClassification.from_pretrained('microsoft/deberta-base', num_labels=1)
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
```
or 
```
from transformers import DebertaV2Tokenizer, DebertaV2ForSequenceClassification
import torch
tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v2-xlarge')
model = DebertaV2ForSequenceClassification.from_pretrained('microsoft/deberta-v2-xlarge', num_labels=1)
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
```

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## Expected behavior

outputs the regression 

## Actual behavior
RuntimeError: "mse_cpu" not implemented for 'Long' (CPU)
RuntimeError: "mse_cuda" not implemented for 'Long' (GPU)

## Who can help
@LysandreJik @sgugger 

Comments:
I think this is because the `labels` tensor is of type `long` but the mse loss function expects float tensor. You could try passing a float tensor instead

```python
labels = torch.tensor([1], dtype=torch.float).unsqueeze(0)
```

> I think this is because the `labels` tensor is of type `long` but the mse loss function expects float tensor. You could try passing a float tensor instead
> 
> ```python
> labels = torch.tensor([1], dtype=torch.float).unsqueeze(0)
> ```

Worked thanks