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. Here, we introduce a lightweight reranker bge reranker v2.5 gemma2 lightweight , which is a multilingual model trained based on gemma2 9b. By integrating token compression capabilities and layerwise reduction, the model can maintain outstanding performance while saving significant resources. Our model primarily demonstrates the following capabilities: Lightweight: The model can be made lightweight through token compression, layerwise reduction, or a combination of both. Outstanding performance: The model has achieved new state of the art (SOTA) performance on both BEIR and MIRACL. We will release a technical report about lightweight reranker soon with more details. You can use bge reranker v2.5 gemma2 lightweight with the following different prompts: Predict whether passage B contains an answ…
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