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README.md
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# BGE-M3
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In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
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- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
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- MCLS: A simple method to improve the performance on long text without fine-tuning.
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If you have no enough resource to fine-tuning model with long text, the method is useful.
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Refer to our [report]() for more details.
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**The fine-tuning codes and datasets will be open-sourced in the near future.**
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For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
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# BGE-M3
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In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
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- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
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- MCLS: A simple method to improve the performance on long text without fine-tuning.
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If you have no enough resource to fine-tuning model with long text, the method is useful.
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Refer to our [report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) for more details.
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**The fine-tuning codes and datasets will be open-sourced in the near future.**
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