BLUE 2
Author: anhnt170489
Pull request: yes
Labels: name: wontfix

this PR seemed to be out of date due to being late considered (https://github.com/huggingface/transformers/pull/1238). 

So I updated the code to be able to merge with the latest version. 

In this PR:

- I add BertForMultiLabelClassification, RobertaForTokenClassification, RobertaForMultiLabelClassification.

- I add examples for Finetuning the BERT, RoBERTa models for tasks on BLUE (https://github.com/ncbi-nlp/BLUE_Benchmark). BLUE (Biomedical Language Understanding Evaluation) is similar to GLUE, but for Biomedical data. The "run_blue", "utils_blue" are customized from "run_glue", "utils_glue", but more sufficient, because it contains not only sequence classification, but also token classification, multi-label classification. People may also have more options for examples of fine-tuning BERT/RoBERTa.

- I also add test function to test_examples as well as test data

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=h1) Report
> Merging [#1440](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1615360c71f75da7b8aefd14c5d8a461486f865b?src=pr&el=desc) will **decrease** coverage by `1.21%`.
> The diff coverage is `30.88%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/1440/graphs/tree.svg?width=650&token=9qOlN6Hb1c&height=150&src=pr)](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=tree)

```diff
@@            Coverage Diff            @@
##           master   #1440      +/-   ##
=========================================
- Coverage   84.72%   83.5%   -1.22%     
=========================================
  Files          84      84              
  Lines       12591   12656      +65     
=========================================
- Hits        10668   10569      -99     
- Misses       1923    2087     +164
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [transformers/modeling\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/1440/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2JlcnQucHk=) | `85.54% <27.27%> (-2.63%)` | :arrow_down: |
| [transformers/modeling\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1440/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3JvYmVydGEucHk=) | `57.06% <32.6%> (-14.16%)` | :arrow_down: |
| [transformers/modeling\_tf\_pytorch\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1440/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3B5dG9yY2hfdXRpbHMucHk=) | `10.48% <0%> (-66.44%)` | :arrow_down: |
| [transformers/tests/modeling\_tf\_common\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1440/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `87.5% <0%> (-7.5%)` | :arrow_down: |
| [transformers/modeling\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1440/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3hsbmV0LnB5) | `71.25% <0%> (-0.9%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=footer). Last update [1615360...e7ffd9a](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


Ok, I think this is great, sorry for the delay in reviewing the PR.

Do you want to just add the new RoBERTa models in the tests, at least [this line](https://github.com/huggingface/transformers/blob/master/transformers/tests/modeling_roberta_test.py#L38).

Also, optional but could be nice if you feel like it: add TF 2.0 counterparts to your new PyTorch heads (you can just copy-past-adapt) the relevant Bert heads.

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.