Giving hard_negative a higher weight when calculating loss

[yjoonjang]

Hello, I am a Korean student who loves the work of sentence-transformers team.
I've used a lot of losses you made when finetuning an embedding model.

However, I thought that it will be nice if there is a loss that gives hard_negative a higher weight in loss calculation.
For example, the input will be like: (anchor, positive, hard_negative) triplets,
and when caculating loss, utilizing
lambda * (similarity between anchor-hard_negative) + (1-lambda) * (similarity between anchor-in_batch_negatives)
looks pretty good for me.

Because the input consists of (anchor, positive, hard_negative),
in-batch negatives will include both positives and hard negatives from same batch.

Can I hear your thoughts about this idea?
And also, it would be grateful if you let me know if there are some losses similar to this idea that I missed.

Thank you for reading.

[yjoonjang]

@tomaarsen
Could you please check my issue? Thank you.