Added Deberta model type support
Author: FilipposVentirozos
Pull request: yes

# What does this PR do?

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Fixes in the token_classification example the support for Deberta. Since it was not included in an 'if' clause. Specifically, I added there since it's mentioned that Deberta needs the 'add_prefix_space' to be True and that 'if' clause covers this parameter. I tested it and it works well.


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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @ylacombe, @eustlb
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Comments:
Hey @FilipposVentirozos, have you tried running the script with DeBERTa? Does it run/train successfully?

Hello @LysandreJik  yes it runs as intended. That is Deberta version 1 (microsoft/deberta-base). The newer versions (i.e. v2, v3) still not supported cause of the need of the slow tokenisation instead, may look into it more later.

I tried again with `microsoft/deberta-v3-base` and it actually works. FYI, I tested it locally on my Mac and on my custom dataset and on Conll2003 that was for train, eval and predict. If you would like me to do any other experiment, please let me know.

Ok let's merge it then! Thanks for your efforts