# Japanese Reranking

This is a very compact and fast Japanese reranking model, suitable for improving the accuracy of RAG systems and can run efficiently on CPUs or edge devices.

This is a very compact and fast Japanese reranking model, suitable for improving the accuracy of RAG systems.

A Japanese general-purpose reranking model built on ModernBERT-Ja, featuring top-tier performance and long-sequence processing capabilities

Ruri Reranker is a general-purpose Japanese reranking model based on the Sentence Transformers architecture, specifically designed for Japanese text relevance ranking tasks.

General-purpose Japanese reranking model for improving relevance ranking in Japanese text retrieval

Ruri Reranker is a Japanese text reranking model based on Transformer architecture, specifically designed to optimize the ranking quality of retrieval results.

The Ruri Reranker is a general-purpose Japanese reranking model specifically designed to improve the relevance ranking of Japanese text retrieval results. The small version maintains high performance while having a smaller parameter count.

This is a Japanese-trained Reranker (CrossEncoder) model for text relevance ranking tasks.

This is a Japanese Reranker (Cross-Encoder) model for text ranking tasks, featuring 24 layers and a hidden layer size of 1024.

This is a Japanese-trained Reranker (Cross-Encoder) model for text ranking tasks.

This is a Japanese-trained Reranker (Cross-Encoder) model for text ranking tasks.