[WIP] Add support for CamembertForTokenClassification
Author: stefan-it
Pull request: yes

Hi,

this PR adds a `CamembertForTokenClassification` implementation, so that fine-tuning of NER models is possible.

Tasks:

* [x] Implement `CamembertForTokenClassification`
* [x] Add `CamembertForTokenClassification` to module import list
* [x] Add support for CamemBERT model in `run_ner.py` example 

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=h1) Report
> Merging [#1860](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3916b334a86484af8442d1cfdb2f15695feae581?src=pr&el=desc) will **decrease** coverage by `0.03%`.
> The diff coverage is `55.55%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/1860/graphs/tree.svg?width=650&token=9qOlN6Hb1c&height=150&src=pr)](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #1860      +/-   ##
==========================================
- Coverage   84.08%   84.04%   -0.04%     
==========================================
  Files          97       97              
  Lines       14316    14333      +17     
==========================================
+ Hits        12037    12046       +9     
- Misses       2279     2287       +8
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [transformers/modeling\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/1860/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2NhbWVtYmVydC5weQ==) | `100% <100%> (ø)` | :arrow_up: |
| [transformers/tokenization\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/1860/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl9jYW1lbWJlcnQucHk=) | `36.92% <38.46%> (+0.38%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1860?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/1860?src=pr&el=footer). Last update [3916b33...56c8486](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


This looks good to me @stefan-it 👌 

We'll also need to do the TF models but that can be in a different PR.

cc @LysandreJik @thomwolf 

I tried to replicate the results for PoS tagging on ParTUT dataset (using the tagged 2.2 version from [here](https://github.com/UniversalDependencies/UD_French-ParTUT)). I could replicate the results mentioned in the CamemBERT paper for the multilingual (cased) BERT model: `run_ner` outputs F1-score, so I added the `accuracy_score` metric from the `seqeval` module for debugging ;) 

However, the CamemBERT model is ~10% behind it. I'm trying to figure out why, loss is really high compared to mBERT...


LGTM thanks!