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安裝中文字典英文字典辭典工具!
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- BAAI bge-reranker-v2-m3 - Hugging Face
For multilingual, utilize BAAI bge-reranker-v2-m3 and BAAI bge-reranker-v2-gemma For Chinese or English, utilize BAAI bge-reranker-v2-m3 and BAAI bge-reranker-v2-minicpm-layerwise For efficiency, utilize BAAI bge-reranker-v2-m3 and the low layer of BAAI bge-reranker-v2-minicpm-layerwise
- bge-reranker-v2-m3
Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities
- BGE-Reranker-v2 — BGE documentation
For efficiency, utilize BAAI bge-reranker-v2-m3 and the low layer of BAAI bge-reranker-v2-minicpm-layerwise For better performance, recommand BAAI bge-reranker-v2-minicpm-layerwise and BAAI bge-reranker-v2-gemma
- RAG 再添新利器！智源开源最强检索排序模型 BGE Re-Ranker v2. 0
近日，智源团队再度推出新一代检索排序模型 BGE Re-Ranker v2 0，同时扩展向量模型BGE的“文本+图片”混合检索能力。 BGE Re-Ranker v2 0 支持更多语言，更长文本长度，并在英文检索基准MTEB、中文检索基准C-MTEB、多语言检索基准MIRACL、LLaMA-Index Evaluation等主流基准上取得了state-of-the-art的结果。 BGE-v1 5、BGE-M3以融入visual token的方式进一步新增“文本+图片”混合检索能力，同时保持优异的文本检索性能。 如图1所示，检索排序模型是信息检索及RAG pipeline中的重要组成部分。 与向量模型与稀疏检索模型相比，检索排序模型会利用更加复杂的判定函数以获得更加精细的相关关系。
- HuggingFace - BAAI - BGE Reranker v2 m3 - GitHub
This repo provides a hands-on demonstration of using BAAl's (Beijing Academy of Artificial Intelligence) BGE-Reranker-v2-m3 model from Hugging Face for sequence reranking tasks It includes two Jupyter notebooks: BGE_Reranker_Local ipynb: For running the model locally using PyTorch and Hugging Face Transformers
- bge-reranker-v2-m3 | AI Model Details
The bge-reranker-v2-m3 model is a lightweight reranker model from BAAI that possesses strong multilingual capabilities It is built on top of the bge-m3 base model, which is a versatile AI model that can simultaneously perform dense retrieval, multi-vector retrieval, and sparse retrieval
- BAAI-bge-reranker-v2-m3 | TWS - docs. twcc. ai
BAAI-bge-reranker-v2-m3 is a lightweight reranking model based on bge-m3, with 568 million parameters It has multilingual capabilities, is easy to deploy, and offers fast inference speed By using BAAI-bge-reranker-v2-m3, you agree to comply with the licensing terms
- BGE Reranker — BGE documentation
bge-reranker-v2-m3 is trained based on bge-m3, introducing great multi-lingual capability as keeping a slim model size You're using a XLMRobertaTokenizerFast tokenizer
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