Add BartForTokenClassification
# What does this PR do?
+ Add BartForTokenClassification class for NER task

## Before submitting
- [x] Did you read the [contributor guideline]
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?

## Who can review?

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@patrickvonplaten, @patil-suraj @sgugger 
Finished ci test and passed all.<|||||>Hey @ZIZUN, 

Thanks for the PR! Do we have a fine-tuned checkpoint for `BartForTokenClassification`? I'm wondering how useful this architecture is<|||||>Hello @patrickvonplaten 
Yes, I tried to finetune this architecture for the Korean NER dataset. The results are as follows.

|                                                                  | Slot F1 (%) |
| ---------------------------------------------------- | ----------- |
| [CNN-BiLSTM-CRF](https://github.com/monologg/korean-ner-pytorch)           | 74.57       |
| **[KoBART(ours)](https://huggingface.co/hyunwoongko/kobart)**                                                 | **84.17** |
| Bert-Multilingual                                                 | 84.20       |
| [KoBERT](https://github.com/monologg/KoBERT-NER)                         | 86.11       |
|  RoBERTa                                                             | 87.58       |

This structure is an auto-regressive architecture, so the score is lower than bi-directional model such as BERT.
But the score is resonable, so I think this architecture is a valueable thing for research.<|||||>Thank you for leaving comments! @sgugger @patil-suraj 
I think you are all right. It's sad but the results are a little disappointing. If I make a notebook, I will share it with you.
Have a nice day.