Add Doc Test GPT-J
Author: ArEnSc
Pull request: yes

# What does this PR do?

Fixes the broken doc tests for GPT-J
Apart of the documentation sprint work.

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github [issue] (https://github.com/huggingface/transformers/issues/16292)? Please add a link
      to it if that's the case.
  
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?

## Who can review?
@ydshieh 
@sgugger
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Comments:
_The documentation is not available anymore as the PR was closed or merged._

@ArEnSc 

Thank you for working on GPT-J.
Maybe you can just put any expected value as empty string. Once other parts are done,
please ping me and I will try to get and put the expected values :)

> @ArEnSc
> 
> Thank you for working on GPT-J. Maybe you can just put any expected value as empty string. Once other parts are done, please ping me and I will try to get and put the expected values :)

yep please do and let me know and we can close this =)

Hi, @ArEnSc 

After discussing with the team, we found that there is no real checkpoints for finetuned GPT-J model on downstream tasks. (There is one for text seq. classification, but it is for Korean language).

If you still want to work on this GPT-J doctest, the best we could do for now is to use a tiny model (that is created for testing purpose)

https://huggingface.co/hf-internal-testing/tiny-random-gptj

With this one, there won't be any OOM issue. Let me know if you want to continue, and if so, don't hesitate if you have any issue using this tiny model checkpoint.

Thanks!

> Hi, @ArEnSc
> 
> After discussing with the team, we found that there is no real checkpoints for finetuned GPT-J model on downstream tasks. (There is one for text seq. classification, but it is for Korean language).
> 
> If you still want to work on this GPT-J doctest, the best we could do for now is to use a tiny model (that is created for testing purpose)
> 
> https://huggingface.co/hf-internal-testing/tiny-random-gptj
> 
> With this one, there won't be any OOM issue. Let me know if you want to continue, and if so, don't hesitate if you have any issue using this tiny model checkpoint.
> 
> Thanks!

will do! Ill do this after work today

Hi, @ArEnSc  

Thank you for the effort.

I need to investigate first why it is non deterministic, and see if there is a way to fix this.
We strongly prefer not to disable doctests for some parts in a model.
(Otherwise, our team need to discuss to justify it and make the decision).

I will take a look in this soon!

Hi, @ArEnSc 

I uploaded the checkpoints

```
"ydshieh/tiny-random-gptj-for-sequence-classification"
"ydshieh/tiny-random-gptj-for-question-answering"
```

I tested with `tiny-random-gptj-for-question-answering` and the results are now deterministic. Could you use them please, and let me know if they work well :-)

@ydshieh I think we should be good to go here let me know =)
edit (# limitations under the License..) <-- ill fix this in a bit. Had to trigger CI some how

@ArEnSc Would you mind to remove the comments with (very) long error message - just easier to load this page :-) Thanks!

@ydshieh looks like it good to go!

@ydshieh I think this is done after CI passes =)

@ydshieh we are good to merge! =)

> @ydshieh we are good to merge! =)

Yes! Merged now.
Thanks a lot for working on this doctest for GPT-J, @ArEnSc 🚀 🎉