[Feature Request] add a classification / estimation layer on top of Encoders

This has been a long time request.
Recently resurfaced with #1534 .
#1342 is also an example on how to perform classification but HF library is needed.

Technically this is not very difficult since it is just a matter of adding some extra Linear layer after the Encoder.
However this can take various form since all features are not identical.

I would like two use-cases to work to make sure we can check the output.

Use-case 1: XLM-Roberta classifier
it is just a matter of adding a new "Layer" (in CT2 taxonomy) named "classifier" which replicates this: https://github.com/huggingface/transformers/blob/main/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L1167
which you can see here: https://github.com/huggingface/transformers/blob/main/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L1434-L1453
is a very simple module.

Use-case 2: Unbabel Comet Estimator
This is again a very simple add-on.
A new "Layer" named "estimator" needs to be added based on this:
https://github.com/Unbabel/COMET/blob/master/comet/modules/feedforward.py#L26-L72
since we just need to use it for inference there are only 3 nn.Linear named:
estimator.ff.0
estimator.ff.3
estimator.ff.6
because 1, 2, 4, 5 are not learned weights.

Of course the converter for https://github.com/OpenNMT/CTranslate2/blob/master/python/ctranslate2/converters/transformers.py#L1970
would need to be modified to reflect the new layer.

If everything works fine for XLM-Roberta we can eventually adapt for other encoder-based models.

LMK if you need more info to implement.

Start with the Transformers converter at python/ctranslate2/converters/transformers.py around line 1970, then compare the linked XLM-Roberta classifier and COMET feedforward definitions. Trace how encoder layers are represented and converted before deciding how the classifier and estimator layers fit. Done means both referenced use cases can be converted and run for inference, with the new learned weights handled.