If they received a low score by the cross-encoder, we saved them as hard negatives: They got a high score from the bi-encoder, but a low-score from the (better) cross-encoder.
We then trained the v2 models with these new hard negatives.
Version Histroy
As we work on the topic, we will publish updated (and improved) models.
Version 3
Version 2
Version 1
