Following @sshleifer example, here is what I worked on the past few weeks and plan to work in the near future.
).
Model outputs:
Finished cleaning up all model outputs and made sure PyTorch and TensorFlow have the same API.
Documentation:
- Work on cleaning up and updating docstrings and documentation of the main classes: config, tokenizer, models and pipelines.
- Automatic conversion of the tutorials into notebooks and added the “open in colab” button.
Trainer:
- A bit of clean-up to make sure
Trainer
andTFTrainer
have the same API. - Exposed the customization points when the user wants to subclass and override.
- Initial work to add hyperparameter search (see #6576).
- Initial work to have an easy bridge between nlp and
Trainer
(see #6449).
Repository consistency:
People love the fact each model file is self-contained and the code is not refactored since they can then quickly experiment, but it can be hard to maintain! Added a script that checks all models are tested (by the common tests) and documented.
Funnel Transformer:
Paper - Initial work to understand the implementation and port it to Transformers. PyTorch version is almost done.
Plans:
- Continue the work on
Trainer
with hyperparameter search and nlp interface. - Work on refactoring all examples to use
Trainer
and nlp - Finish porting Funnel Transformer.