Fix problem_type to match with the applied loss function for distillbert sequence classification 
# What does this PR do?
The problem_type in config is not correct with the loss function applied. Can be seen [here](https://github.com/huggingface/transformers/blob/242ec31aa59b358e631d981b545fd08330584ea8/src/transformers/models/distilbert/modeling_distilbert.py#L649). 

This PR fixes this so that the applied loss is consistent with the problem type: `BCEWithLogitsLoss` is applied for the `problem_type` of `single_label_classification`, and `CrossEntropyLoss` is applied for the problem type `multi_label_classification`

Fixes #12014

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## Who can review?
@LysandreJik
cc @abhi1thakur @sgugger <|||||>No, this is incorrect. What `single_label_classification` means each sample can only have one label (but there could be multiple classes) so the loss to use is cross entropy. `multi_label_classification` means each sample can have zero or several labels, so in this case we use bce (because there can't be a softmax).