Improving TrOCR results with LM 🚀
Author: gagan3012
Pull request: yes

# What does this PR do?
This PR adds `TrOCRProcessorWithLM` which gives us the ability to add a Kenlm Langauge model to improve the metrics results of TrOCR
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Fixes # (issue)


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@NielsRogge @patrickvonplaten @patil-suraj 
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
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Library:

- benchmarks: @patrickvonplaten
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- ray/raytune: @richardliaw, @amogkam
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- tokenizers: @n1t0, @LysandreJik
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HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19174). All of your documentation changes will be reflected on that endpoint.

Hi,

Thanks for your PR! Wonder if it makes sense to add another language model on top of the decoder of TrOCR (which already is a language model)? For instance, beam search is already supported (as you can use the [generate](https://huggingface.co/docs/transformers/v4.22.2/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate) method to autoregressively generate text). What would be the benefit of adding this other decoder on top?

Did you see a boost in performance?

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/main/CONTRIBUTING.md) are likely to be ignored.