Adds MuRIL - BERT based model for 17 Indian Languages to the library
Author: ravi03071991
Pull request: yes

# What does this PR do?

This PR adds a TensorFlow-based MuRIL model to the library. More details about the MuRIL model can be found [here](https://tfhub.dev/google/MuRIL/1).

Fixes: #9190
@LysandreJik @patrickvonplaten 

Comments:
Hey @ravi03071991, 

thanks a lot for the new model! There are quite some empty files in the PR - can we maybe delete those?

From https://tfhub.dev/google/MuRIL/1 it seems that MuRIL is the same as BERT - do we need a new model class? It would be awesome if you could specify the differences between MuRIL and BERT in this PR :-) 

> Hey @ravi03071991,
> 
> thanks a lot for the new model! There are quite some empty files in the PR - can we maybe delete those?

Sure. We can delete them.

- I've posted an adapted MuRIL BERT model here https://huggingface.co/monsoon-nlp/muril-adapted-local
- Simran Khanuja has posted here https://huggingface.co/simran-kh/muril-cased-temp
- there is also https://huggingface.co/google/muril-cased/tree/main but it has no model files

Does this do the job?

Hey @ravi03071991 and @mapmeld, 

So what I understand is that the model can be used with **no** code addition using `BertModel` and `BertTokenizer` - is this correct? I think in this case it does make more sense to just add a model to the model hub as it's done with this checkpoint: https://huggingface.co/monsoon-nlp/muril-adapted-local/tree/main 

Did you guys check whether the model works as expected? We could do some quick fine-tuning evaluation on the XTREME benchmark to make sure the model behaves correctly in transformers. We should get more or less the same results as shown on the tf-hub: https://tfhub.dev/google/MuRIL/1 . I think it'll be pretty easy to do some fine-tuning / evaluation by slightly adapting this notebooks: https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb to use the XTREME dataset from `datasets`: https://huggingface.co/datasets/xtreme . Would someone be interested in giving it a shot at making such a notebook?

I think with such a notebook, we can upload the pre-trained checkpoint to an "official" org name in the hub - probably `google/muril-bert-base` or something (google trained the model no?). Then we're happy to do some promotion on the model as well :-) 

> Hey @ravi03071991 and @mapmeld,
> 
> So what I understand is that the model can be used with **no** code addition using `BertModel` and `BertTokenizer` - is this correct? I think in this case it does make more sense to just add a model to the model hub as it's done with this checkpoint: https://huggingface.co/monsoon-nlp/muril-adapted-local/tree/main
> 
> Did you guys check whether the model works as expected? We could do some quick fine-tuning evaluation on the XTREME benchmark to make sure the model behaves correctly in transformers. We should get more or less the same results as shown on the tf-hub: https://tfhub.dev/google/MuRIL/1 . I think it'll be pretty easy to do some fine-tuning / evaluation by slightly adapting this notebooks: https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb to use the XTREME dataset from `datasets`: https://huggingface.co/datasets/xtreme . Would someone be interested in giving it a shot at making such a notebook?
> 
> I think with such a notebook, we can upload the pre-trained checkpoint to an "official" org name in the hub - probably `google/muril-bert-base` or something (google trained the model no?). Then we're happy to do some promotion on the model as well :-)

 Sure. I am can take up the task of making the notebook.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.