This is a small cross-encoder reranker model trained specifically for the Japanese language. It is designed to be used in retrieval pipelines to rerank candidate documents or passages based on relevance to a query. Built with the sentence-transformers library, it is lightweight and effective for Japanese information retrieval tasks.
Japanese Reranker Cross Encoder Small sits in PulseGate's Foundation models & chat category. It focuses on improving search relevance for Japanese-language queries by reranking retrieved documents using a specialized cross-encoder model. It is built as an open-source project for developers building Japanese search or RAG applications. The project is open source (CC-BY-SA-4.0). Japanese Reranker Cross Encoder Small is available on the web and API.
Behind Japanese Reranker Cross Encoder Small is hotchpotch, and it first shipped in 2022. Development happens publicly on GitHub with 346 stars. Among its 3 catalogued features are Text Reranking, cross-Encoder, and Japanese Language.
Summary written by a language model from the project’s public pages.
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