update paper link (#2)
Browse files- update paper link (bfe7d4cc79193b9f53dac1f4247219774c951f18)
Co-authored-by: Xinjie Shen <[email protected]>
README.md
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| [opensearch-neural-sparse-encoding-doc-v3-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill) | ✔️ | 67M | 0.517 | 1.8 |
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## Overview
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- **Paper**: Coming Soon
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- **Codes**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample/tree/l0_enhance)
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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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| [opensearch-neural-sparse-encoding-doc-v3-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill) | ✔️ | 67M | 0.517 | 1.8 |
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## Overview
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- **Paper**: [Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers ](https://arxiv.org/abs/2504.14839)
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- **Codes**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample/tree/l0_enhance)
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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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