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 and TFTrainer have the same API.
Exposed the customization points when the user wants to subclass and override.
Initial work to add hyperparameter search (see #6576 1).
Initial work to have an easy bridge between  nlp and Trainer (see #6449 1).

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 5 - 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.