- Models with normalized embeddings will prefer the retrieval of shorter passages, while models tuned for dot-product will prefer the retrieval of longer passages. Depending on your task, you might prefer the one or the other type of model.
- Encoding speeds are per second and were measured on a V100 GPU and an 8 core Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz
Changes in v5
Models with normalized embeddings were added: These are the v3 cosine-similarity models, but with an additional normalize layer on-top.
New models trained with MarginMSE loss trained: msmarco-distilbert-dot-v5 and msmarco-bert-base-dot-v5
Changes in v4
Just one new model was trained with better hard negatives, leading to a small improvement compared to v3
Changes in v3
The models from v2 have been used for find for all training queries similar passages. An MS MARCO Cross-Encoder based on the electra-base-model has been then used to classify if these retrieved passages answer the question.
