Support all negative values in ClassLabel
_The documentation is not available anymore as the PR was closed or merged._ Thanks for this fix! I'm not sure what the release timeline is, but FYI #4508 is a breaking issue for transformer token classification using Trainer and PyTorch. PyTorch defaults to -100 as the ignored label for [negative log loss](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html?highlight=nllloss#torch.nn.NLLLoss), so switching labels to -1 leads to index errors using Trainer defaults.

As a workaround, I'm using master branch directly (`pip install git+https://github.com/huggingface/datasets.git@master` for anyone who needs to do the same) until this gets released. The new release `2.4` fixes the issue, feel free to update `datasets` :) 
```
pip install -U datasets
``` @lhoestq I hope it's OK to ping you here. I've noticed that `encode_example` does only work with -1. I already created #7645 to fix the documentation, but then I stumbled across your original changes to the docs text in this PR.

I am talking about this part in `ClassLabel -> encode_example`:

https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/features/features.py#L1126-L1129
We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3

Fix https://github.com/huggingface/datasets/issues/4508