Merge pull request #1156 from ZiyiXia/master

community

Author: ZiyiXia

README.md

@@ -43,7 +43,7 @@ FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following p
 - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB), [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench), [MLVU](https://github.com/JUNJIE99/MLVU)
 
 ## News
-- 29/10/2024: :earth_asia: We created WeChat group for BGE. Scan the [QR code](./BGE_WeChat_Group.png) to join the group chat! To get the first hand message about our updates and new release, or having any questions or idea, join us now!
+- 29/10/2024: :earth_asia: We created WeChat group for BGE. Scan the [QR code](./BGE_WeChat_Group.png) to join the group chat! To get the first hand message about our updates and new release, or having any questions or ideas, join us now!
 - <img src="./BGE_WeChat_Group.png" alt="bge_wechat_group" class="center" width="200">
 - 22/10/2024:  :fire: We release another interesting model: [OmniGen](https://github.com/VectorSpaceLab/OmniGen), which is a unified image generation model supporting various tasks. OmniGen can accomplish complex image generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary models such as pose detection and face detection.
 - 9/10/2024: Introducing **MemoRAG**, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1) :fire:

README_zh.md

@@ -43,6 +43,8 @@ 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), [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench), [MLVU](https://github.com/JUNJIE99/MLVU)
 
 ## 更新
+- 29/10/2024: :earth_asia: 我们建立了[BGE技术交流群](./BGE_WeChat_Group.png)，欢迎扫码入群！
+- <img src="./BGE_WeChat_Group.png" alt="bge_wechat_group" class="center" width="200">
 - 9/2/2024: 开始维护更新[教程](./Tutorials/)，教程文件夹中的内容会在未来不断丰富，欢迎持续关注！ :books:
 - 7/26/2024：发布[bge-en-icl](https://huggingface.co/BAAI/bge-en-icl)。这是一个结合了上下文学习能力的文本检索模型，通过提供与任务相关的查询-回答示例，可以编码语义更丰富的查询，进一步增强嵌入的语义表征能力。 :fire:
 - 7/26/2024: 发布[bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2)。这是一个基于gemma-2-9b的多语言文本向量模型，同时支持多种语言和多样的下游任务，在多语言检索数据集 MIRACL, MTEB-fr, MTEB-pl 上取得了迄今最好的实验结果。 :fire: