AI is happening. The future is updating.opensearch-neural-sparse-encoding-doc-v3-distill
The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' performance on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v3 series of models have better search relevance, efficiency and inference speed than the v1 and v2 series. The specific advantages and disadvantages may vary across different datasets. - **Paper**: Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers - **Codes**: opensearch-sparse-model-tuning-sample
The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' performance on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v3 series of models have better search relevance, efficiency and inference speed than the v1 and v2 series. The specific advantages and disadvantages may vary across different datasets. - **Paper**: Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers - **Codes**: opensearch-sparse-model-tuning-sample