Bge Large En

bge-large-zh, open-sourced by the Beijing Academy of Artificial Intelligence (BAAI), is currently the most powerful vector representation model for Chinese tasks, with its semantic representation capabilities comprehensively surpassing those of similar open-source models.

BAAI/bge-large-en-v1.5 is a large English text embedding model and part of the BGE (BAAI General Embedding) series. It achieves excellent performance on the MTEB benchmark, with an average score of 64.23 across 56 datasets, excelling in tasks such as retrieval, clustering, and text pair classification. The model supports a maximum input length of 512 tokens and is suitable for various natural language processing tasks, such as text retrieval and semantic similarity computation.

BAAI/bge-large-zh-v1.5 is a large Chinese text embedding model and part of the BGE (BAAI General Embedding) series. It performs excellently on the C-MTEB benchmark, achieving an average score of 64.53 across 31 datasets, with outstanding results in tasks such as retrieval, semantic similarity, and text pair classification. The model supports a maximum input length of 512 tokens and is suitable for various Chinese natural language processing tasks, such as text retrieval and semantic similarity computation.

BAAI/bge-reranker-v2-m3 is a lightweight multilingual reranking model. It is developed based on the bge-m3 model, offering strong multilingual capabilities, easy deployment, and fast inference. The model takes a query and documents as input and directly outputs similarity scores instead of embedding vectors. It is suitable for multilingual scenarios and performs particularly well in both Chinese and English processing.