Fix integration tests of DeBERTa
Author: BigBird01
Pull request: yes

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Comments:
@LysandreJik I just fix the numeric part of the tests. Another issue is that I just made the change to the model state keys, i.e. change  bert.encoder to deberta.encoder. However, I can only upload the model to **DeBERTa/deberta-base, DeBERTa/deberta-large**. Could you help to mv those two model to the namespace of **microsoft**? Or could you add me to the organization **Microsoft**?

Hi! Sure, I can add you to the `microsoft` organization. What's your username on the hub? Thanks!

I'm uploading the two models with the modified names `bert` -> `deberta` right now.

> Hi! Sure, I can add you to the `microsoft` organization. What's your username on the hub? Thanks!

The name is **_DeBERTa_**

Cool, I'm adding you! I've done a PR here #7229 that solves all the integration tests. Do you mind reviewing it before we merge it? I've added comments to explain why the changes were so.

> > Hi! Sure, I can add you to the `microsoft` organization. What's your username on the hub? Thanks!
> 
> The name is **_DeBERTa_**

Hi, @LysandreJik 

Did you add me **DeBERTa* to `microsoft`? I still can't see my account under `Microsoft`.
Seems the model you uploaded to `Microsoft/deberta-base` and `Microsoft/deberta-large` is not loadable due to a format issue. 


I've added you manually @BigBird01, but you should have been able to request to join from the website – was this not the case?

@BigBird01, what's the issue you have? I can load both:
```py
>>> from transformers import DebertaModel
>>> model = DebertaModel.from_pretrained("microsoft/deberta-base")
Downloading: 100%|██████████| 448/448 [00:00<00:00, 510kB/s]
Downloading: 100%|██████████| 559M/559M [00:50<00:00, 11.1MB/s]
Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['deberta.embeddings.position_embeddings.weight']
- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
>>> model = DebertaModel.from_pretrained("microsoft/deberta-large")
Downloading: 100%|██████████| 449/449 [00:00<00:00, 578kB/s]
Downloading: 100%|██████████| 1.63G/1.63G [02:42<00:00, 9.98MB/s]
Some weights of the model checkpoint at microsoft/deberta-large were not used when initializing DebertaModel: ['deberta.embeddings.position_embeddings.weight']
- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

```