opensearch neural sparse encoding v2 distill Select the model The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' zero shot performance on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets. Model Inference free for Retrieval Model Parameters AVG NDCG@10 AVG FLOPS opensearch neural sparse encoding v1 133M 0.524 11.4 opensearch neural sparse encoding v2 distill 67M 0.528 8.3 opensearch neural sparse encoding doc v1 ✔️ 133M 0.490 2.3 opensearch neural sparse encoding doc v2 distill ✔️ 67M 0.504 1.8 opensearch neural sparse encoding doc v2 mini ✔️ 23M 0.497 1.7 opensearch neural sparse encoding doc v3 distill ✔️ 67M 0.517 1.8 opensearch neural sparse encoding doc v3 gte ✔️ 133M 0.546 1.7 Overview Paper : Towards Competitive Search Relevance For Inference Free Learned Sparse Retrievers Fine tuning sample : opensearch sparse model tuning sample T…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy