CamembertForCausalLM
This PR adds `CamembertForCausalLM` by subclassing `RobertaForCausalLM`, so that it can be used with the `EncoderDecoderModel`.

@patrickvonplaten 
@patrickvonplaten  didn't add any tests since it subclasses `RobertaForCausalLM` which is already tested.<|||||># [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=h1) Report
> Merging [#6577](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/12d7624199e727f37bef7f53d527df7fabdb1fd6&el=desc) will **increase** coverage by `1.14%`.
> The diff coverage is `100.00%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/6577/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #6577      +/-   ##
==========================================
+ Coverage   79.18%   80.33%   +1.14%     
==========================================
  Files         156      156              
  Lines       28129    28132       +3     
==========================================
+ Hits        22275    22599     +324     
+ Misses       5854     5533     -321     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/\_\_init\_\_.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.28% <ø> (ø)` | |
| [src/transformers/modeling\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.73% <ø> (ø)` | |
| [src/transformers/modeling\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jYW1lbWJlcnQucHk=) | `100.00% <100.00%> (ø)` | |
| [src/transformers/tokenization\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |
| [src/transformers/modeling\_tf\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `69.06% <0.00%> (-29.32%)` | :arrow_down: |
| [src/transformers/modeling\_tf\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-23.17%)` | :arrow_down: |
| [src/transformers/modeling\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.26% <0.00%> (-0.17%)` | :arrow_down: |
| [src/transformers/generation\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |
| [src/transformers/configuration\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.59% <0.00%> (+0.68%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |
| ... and [9 more](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree-more) | |

------

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> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=footer). Last update [12d7624...1e68a94](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>hi @patrickvonplaten , could you take a look ? Thanks !<|||||>Yes, right. But this intended for the `EncoderDecoder` model class which can use pre-trained encoder for seq2seq tasks.
See PR #6411 and #6538 . Also Patrick shared this awesome paper which explores this idea and has shown some good results on seq2seq tasks.  https://arxiv.org/pdf/1907.12461.pdf<|||||>Thanks for the explanation! Yes it makes perfect sense to use these models for seq2seq but I still think maybe we should add a note somewhere in the document to explain this?<|||||>Definitely! Will update the docs to explicitly state that this is intended for seq2seq and might not perform well on just causal modelling.  Will also link the paper in `EncoderDecoder` doc page, so people will know what to choose.<|||||>Btw, @patil-suraj did you start doing some Roberta2Roberta experiments? Wanted to start running some experiments next week - wanted to check if you already had some interesting results<|||||>@patrickvonplaten  Just started one experiment for qg and going to run cnn/dm after that, will let you know the results. 
Since Roberta has `bos`, what's the `decoder_start_token_id` for `Roberta2Roberta`, is it `bos` or `pad` token ?<|||||>In my experiments with bert2bert, I used the same token for encoder bos and decoder bos, but it's up to you! `bos` makes more sense to me in the case of Roberta2Roberta.<|||||>Okay, Thanks Patrick!