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@ -36,7 +36,7 @@ FlagEmbedding专注于检索增强llm领域，目前包括以下项目:
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## 更新
- 3/18/2024: 发布新的[rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), 更好对性能同时支持多语言和长文本。:fire:
- 3/18/2024: 发布新的[rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), 拥有更好的性能同时支持多语言和长文本。 :fire:
- 3/18/2024: 发布[Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual)，该项目通过引入image token embedding赋予BGE视觉编码能力。Visualized-BGE可以对混合图文数据进行编码，用于广泛的混合模态检索任务。 :fire:
- 1/30/2024: 发布**BGE-M3**, 第一个具有多功能、多语言和多粒度特性的文本检索模型，高效支持多语言（100+语言）、长文本（至多8192长度的输入文本）、和混合检索（稠密、稀疏、多向量）。 详见[report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf)和[代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) :fire:
- 1/9/2024: 发布[Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), 一个有效、高效、兼容、低成本（训练）的扩展大预言模型上下文长度的方法。[技术报告](https://arxiv.org/abs/2401.03462)