Ninehills

bge-reranker-base

ninehills

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The bge-reranker-base model from BAAI (Beijing Academy of Artificial Intelligence) is a cross-encoder model that can be used to re-rank the top-k documents returned by an embedding model. It is more accurate than embedding models like BGE-M3 or LLM Embedder, but less efficient. This model can be fine-tuned on your own data to improve performance on specific tasks. Model inputs and outputs Inputs pairs_json**: A JSON string containing input pairs, e.g. [["a", "b"], ["c", "d"]] Outputs scores**: An array of scores for the input pairs use_fp16**: A boolean indicating whether the model used FP16 inference model_name**: The name of the model used Capabilities The bge-reranker-base model can effectively re-rank the top-k documents returned by an embedding model, making the final ranking more accurate. This can be particularly useful when you need high-precision retrieval results, such as for question answering or knowledge-intensive tasks. What can I use it for? You can use the bge-reranker-base model to re-rank the results of an embedding model like BGE-M3 or LLM Embedder. This can help improve the accuracy of your retrieval system, especially for critical applications where precision is important. Things to try You can try fine-tuning the bge-reranker-base model on your own data to further improve its performance on your specific use case. The examples provided can be a good starting point for this.

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