bge-reranker-v2-m3

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-layerwise | MiniCPM-2B-dpo-bf16 | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | You can select the model according your senario and resource. 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-minicp