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bge-reranker-v2-m3

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bge-reranker-v2-m3 is the newest balance-striking reranker model from BAAI. It outputs rank scores for query-doc pairs and has FP16 inference enabled. This model can be compared to similar query-document ranking models like qwen1.5-110b and reliberate-v3. Model inputs and outputs The bge-reranker-v2-m3 model takes a JSON string as input, which can be a list containing one query and one passage pair, or a list of such pairs. The output is an array. Inputs Input List**: A JSON string containing one or more query-passage pairs. Outputs Output**: An array containing the output of the model. Capabilities The bge-reranker-v2-m3 model can be used to rank query-document pairs, which is useful for a variety of applications such as search, question answering, and information retrieval. What can I use it for? The bge-reranker-v2-m3 model can be used for a variety of applications that involve ranking text-based content, such as web search, recommendation systems, and content moderation. For example, you could use this model to improve the relevance of search results on your website or to automatically filter out low-quality or inappropriate content. Things to try One interesting thing to try with the bge-reranker-v2-m3 model is to experiment with different types of query-document pairs and observe how the model's ranking scores change. You could also try combining this model with other natural language processing models, such as real-esrgan or absolutereality-v1.8.1, to create more sophisticated content ranking and recommendation systems.

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