AI正在发生 未来正在更新opensearch-neural-sparse-encoding-multilingual-v1
The 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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The 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.