@patrickvonplaten I would like to start with Maskformer for Tensorflow/Pytorch. Catch up with how the event goes.

Awesome! Let me know if you have any questions :-)

Hello! I'd like to take on Longformer for Tensorflow/Pytorch please.

@patrickvonplaten I would like to start with T5 for pytorch and tensorflow

Sounds great!

LayoutLM is also taken as mentioned by a contributor on Discord!

@patrickvonplaten I would take GPT and GPT-J (TensorFlow editions) if those are still available.

I'm guessing GPT is GPT2?

I will take Bert, Albert, and Bigbird for both Tensorflow/Pytorch

I'll take Swin and ViT for Tensorflow

I'd like DistilBERT for both TF and PT please

@patrickvonplaten I would take GPT and GPT-J (TensorFlow editions) if those are still available.

I'm guessing GPT is GPT2?

@cakiki You can go for GPT2 (I updated the name in the test)

Can I try GPT2 and GPTJ for Pytorch? if @ydshieh you are not doing so?

I would like to try CLIP for Tensorflow and PyTorch.

I'll take CANINE and TAPAS.

Can I try GPT2 and GPTJ for Pytorch? if @ydshieh you are not doing so?

@ArEnSc
No, you can work on these 2 models :-) Thank you!

@ydshieh Since the MobileBertForSequenceClassification is the copy of BertForSequenceClassification, so I think I will do check doc-test of MobileBert as well to overcome the error from make fixup

I'll take FlauBERT and CamemBERT.

@abdouaziz Awesome! Do you plan to work on both PyTorch and TensorFlow versions, or only one of them?

I would like to work on LUKE model for both TF and PT

@Tegzes you're lucky because there's no LUKE in TF ;) the list above actually just duplicates all models, but many models aren't available yet in TF.

In this case, I will also take DeBERTa and DeBERTa-v2 for PyTorch

@ydshieh

I plan to work only with PyTorch

@Tegzes you're lucky because there's no LUKE in TF ;) the list above actually just duplicates all models, but many models aren't available yet in TF.

True - sorry I've been lazy at creating this list!

Happy to work on TrOCR (pytorch and TF)

I take RoBERTa in PT and TF

I would like to pick up XLM-RoBERTa in PT and TF.

I can work on ELECTRA for PT and TF

Hey guys,

We've just merged the first template for Roberta-like model doc tests: #16363 :-)
Lots of models like ELETRA, XLM-RoBERTa, DeBERTa, BERT are very similar in spirit, it would be great if you could try to rebase your PR to the change done in #16363 . Usually all you need to do is to add the correct {expected_outputs}, {expected_loss} and {checkpoint} to the docstring of each model (ideally giving sensible results :-)) until it passes locally and then the file can be added to the tester :-)

Also if you have open PRs and need help, feel free to ping me or @ydshieh and link the PR here so that we can nicely gather everything :-)

One of the most difficult tasks here might be to actually find a well-working model. As a tip what you can do:

1. Find all models of your architecture as it's always stated in the modeling files here:

   transformers/src/transformers/models/roberta/modeling_roberta.py

   Line 67 in 77c5a80

   # See all RoBERTa models at https://huggingface.co/models?filter=roberta

   e.g. for ELECTRA: https://huggingface.co/models?filter=electra
2. Now click on the task (in left sidebar) your working on, e.g. say you work on ForSequenceClassification of a text model go under this task metric: https://huggingface.co/models?other=electra&pipeline_tag=text-classification&sort=downloads
3. Finally, click on the framework metric (in left sidebar) you're working with: e.g. for TF: https://huggingface.co/models?library=tf&other=electra&pipeline_tag=text-classification&sort=downloads . If you see too few or too many not well performing models in TF you might also want to think about converting a good PT model to TF under your Hub name and to use this one instead :-)

I'll take a shot with the PyTorch implementation of CTRL

Here the mirror of RoBERTa for Tensorflow: #16370

Hi, contributors, thank you very much for participating this sprint ❤️.

Here is one tip that might reduce some issues:

Considering the following 2 facts:

• A previous file file_utils.py contains some code regarding documentation. It was recently refactorized to different files. It might be a good idea (necessary in some case) to update your working branch in your local clone.
• The file transformers/utils/documentation_tests.txt will be updated frequently by different contributors during this event.

Some testing issues could be resolved as:

git checkout main  # or `master`, depends on your local clone
git fetch upstream
git pull upstream main  # Hugging Face `transformers` renamed the default branch to `main` recently
git checkout your_working_branch_for_this_sprint
git rebase main  # or `master`

Don't hesitate if you encounter any problem. Enjoy~

I take BART and IBERT for PT

I'd like to take a crack on Transformer-XL and ConvBert

Hi, contributors!

For the model(s) you work with for this sprint, if you could not find any checkpoint for a downstream task, say XXXModelForTokenClassification model, but there is a checkpoint for the base model, what you could do is:

model = XXXModelForTokenClassification.from_pretrained(base_model_checkpoint_name)
model.save_pretrained(local_path)

Then you can upload this new saved checkpoint to Hugging Face Hub, and you can use this uploaded model for the docstring example.

The head part of the model will have randomly initialized weights, and the result is likely to be imperfect, but it is fine for this sprint :-)

I'd like to take a crack on Transformer-XL and ConvBert

@simonzli, great :-). Do you plan to work with the PyTorch or TensorFlow version, or both?

I'd like to take a crack on Transformer-XL and ConvBert

@simonzli, great :-). Do you plan to work with the PyTorch or TensorFlow version, or both?

I'll work on both PyTorch and TensorFlow😊

@patrickvonplaten: I chose XLM-RoBERTa and it's a sub-class of RoBERTa. The comments in the file for both PyTorch and TF suggests that the superclass should be referred for the appropriate documentation alongside usage examples (XLM-RoBERTa documentations shows RoBERTa examples). Should I still be adding examples for XLM-RoBERTa or should I pick some other model?

@AbinayaM02 :

Could you show me which line you see suggests that the superclass should be referred for the appropriate documentation in the XLM-RoBERTa model file, please? Thank you :-)