opensearch-neural-sparse-encoding-doc-v1
opensearch-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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opensearch-neural-sparse-encoding-doc-v2-distill
opensearch-projectThe 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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View details ↗opensearch-neural-sparse-encoding-doc-v2-mini
opensearch-projectThe 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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View details ↗opensearch-neural-sparse-encoding-doc-v3-distill
opensearch-projectThe 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
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View details ↗opensearch-neural-sparse-encoding-doc-v3-gte
opensearch-projectThe 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. This is a learned sparse retrieval model. It encodes the documents to 30522 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors.
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View details ↗opensearch-neural-sparse-encoding-multilingual-v1
opensearch-projectThe model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' performance on **MIRACL** benchmark (we exclude th since the uncased backbone can not encode it). **We recommend to use it with max_ratio pruning.** - **Paper**: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers - **Fine-tuning sample**: opensearch-sparse-model-tuning-sample This is a learned sparse retrieval model. It encodes the documents to 105879 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors.
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View details ↗opensearch-neural-sparse-encoding-v1
opensearch-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opensearch-neural-sparse-encoding-v2-distill
opensearch-projectThe 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. This is a learned sparse retrieval model. It encodes the queries and documents to 30522 dimensional **sparse vectors**. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token.
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View details ↗opensearch-semantic-highlighter-v1
opensearch-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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