bge reranker v2 m3 GGUF Model creator : BAAI Original model : bge reranker v2 m3 GGUF quantization : based on llama.cpp release f4d2b Reranker More details please refer to our Github: FlagEmbedding. Model List Usage Fine tuning Evaluation Citation Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. Model List Model Base model Language layerwise feature : : : : : : : : : BAAI/bge reranker base xlm roberta base Chinese and English Lightweight reranker model, easy to deploy, with fast inference. BAAI/bge reranker large xlm roberta large Chinese and English Lightweight reranker model, easy to deploy, with fast inference. BAAI/bge reranker v2 m3 bge m3 Multilingual Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. BAAI/bge reranker v2 gemma gemma 2b Multilingual Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. BAAI/bge reranker v2 minicpm laye…
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