DebertaForSequenceClassification loss computation
# What does this PR do?

This PR changes the way the loss is being computed for `DebertaForSequenceClassification` and `DebertaV2ForSequenceClassification` to make it match what is being done for other bert-like models.

The current way of doing it seems overly complicated and I do not see why the behaviour should be different from other bert-like models, but I might be missing something here... @LysandreJik @sgugger @patrickvonplaten  @BigBird01 what do you think?
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_15864). All of your documentation changes will be reflected on that endpoint.<|||||>No this is a breaking change. We didn't touch the way the loss was computed when adding support for `problem_type` for all models to avoid such a breaking change and we should leave it as is, at least until the next major version IMO.

At the very least, if we do break things there should be a flag to enable back the older behavior.<|||||>Additionally to what @sgugger said, DeBERTa was implemented to respect the paper's implementation which gave SOTA results on sequence classification tasks. Not all transformer models work the best with the same heads, so I personally think it's perfectly fine to have models have different heads to perform the same task, if those heads have been identified as the best for the task at hand.

What is very important is that it still has the same output as other sequence classification models, but that is the case with the `SequenceClassifierOutput`.<|||||>Ok thank you for the explanation, closing this!