opensearch-neural-sparse-encoding-doc-v2-mini

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. - **Paper**: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers - **Fine-tuning sample**: opensearch-sparse-model-tuning-sample

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