BGE-Reranker
A reranking model based on a cross-encoder that performs fine-grained scoring of query-document pairs. Its accuracy exceeds that of vector similarity, and it is used in the reranking stage of two-stage retrieval in RAG systems.
Core Facts
Commonly used Rerank models include Cohere Rerank and BGE-Reranker.
Common open-source reranking options include BGE-Reranker, the Cohere Rerank API, and others.
Reranking models such as Cohere Rerank and BGE-Reranker use a Cross-Encoder architecture to perform fine-grained relevance scoring between queries and documents, and are a key component in optimizing RAG precision.
Timeline (last 90 days)
Reranking uses a cross-encoder to score query-document pairs one by one, with accuracy far higher than vector similarity but at higher computational cost, so fine ranking is done only on the small number of candidates after coarse ranking, forming a two-stage architecture of coarse ranking plus fine ranking.
Common open-source re-ranking models include Cohere Rerank, BGE-Reranker, and the ms-marco series.
Common open-source reranking options include BGE-Reranker, the Cohere Rerank API, and others.
Commonly used open-source reranking models include the BGE-Reranker series and models released by BAAI.
重排序通常使用交叉编码器实现，将查询和候选文档拼接后同时输入，通常只对Top-50到Top-100候选重排，最终选取Top-3到Top-5送入生成模型
All Facts (8)
Commonly used Rerank models include Cohere Rerank and BGE-Reranker.
80%VerifiedCommon open-source reranking options include BGE-Reranker, the Cohere Rerank API, and others.
65%VerifiedReranking models such as Cohere Rerank and BGE-Reranker use a Cross-Encoder architecture to perform fine-grained relevance scoring between queries and documents, and are a key component in optimizing RAG precision.
65%UnverifiedReranker模型（如Cohere Rerank、BGE-Reranker、bce-reranker）同时考虑Query和Document的交互信息，排序精度更高但计算成本也更大
90%UnverifiedReranking uses a cross-encoder to score query-document pairs one by one, with accuracy far higher than vector similarity but at higher computational cost, so fine ranking is done only on the small number of candidates after coarse ranking, forming a two-stage architecture of coarse ranking plus fine ranking.
50%UnverifiedCommon open-source re-ranking models include Cohere Rerank, BGE-Reranker, and the ms-marco series.
50%UnverifiedCommonly used open-source reranking models include the BGE-Reranker series and models released by BAAI.
50%Unverified重排序通常使用交叉编码器实现，将查询和候选文档拼接后同时输入，通常只对Top-50到Top-100候选重排，最终选取Top-3到Top-5送入生成模型
50%