A very good first issue IMO!

See huggingface/transformers#4829 (comment)

we should update the eval dataset to pick one start_position (or the most frequent one)

Optionally, use the huggingface/nlp library to get the eval dataset, and hook it into the Trainer.

Also referenced in huggingface/transformers#6997 (comment)

From paper, it mentioned

Instead, the training data generator chooses 15% of tokens at random, e.g., in the sentence my
dog is hairy it chooses hairy.

It means that 15% of token will be choose for sure.

From https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/dataset/dataset.py#L68,
for every single token, it has 15% of chance that go though the followup procedure.

Describe the feature

I think enforcing typing in methods parameters can be helpful for robustness, readability and stability of the code.

By using mypy static type checker, we can see potential improvements for jina:

Usage:

pip install mypy
mypy --ignore-missing-imports jina

Do not get overwhelmed by the errors. Let's slowly keep improving until we can eve

If I want to use both of them, how to modify code in aen.py? Thanks a lot.