Reranker (Cross Encoder) 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 Details Base model : BAAI/bge reranker v2 m3 The multilingual model has been optimized for Korean. Usage with Transformers Usage with SentenceTransformers First install the Sentence Transformers library: Usage with FlagEmbedding First install the FlagEmbedding library: Fine tune Refer to https://github.com/FlagOpen/FlagEmbedding Evaluation Bi encoder and Cross encoder Bi Encoders convert texts into fixed size vectors and efficiently calculate similarities between them. They are fast and ideal for tasks like semantic search and classification, making them suitable for processing large datasets quickly. Cross Encoders directly compare pairs of texts to compute similarity scores, providing more accurate results. While they are slower due to needing to process each pair, they excel in re ranking top results and are important in Advanced RAG techniques for enhancing text generat…
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