# Query-Passage Matching

This is a sentence-transformers model fine-tuned from intfloat/multilingual-e5-small, specifically optimized for Korean retrieval tasks, mapping text to a 384-dimensional vector space.

Ruri-Large is a high-performance embedding model specialized in Japanese text similarity calculation, based on transformer architecture with support for long text processing (maximum length 8192).

Ruri is a universal text embedding model for Japanese, focusing on sentence similarity and feature extraction tasks.

A cross-encoder model trained on the MS Marco passage ranking task, suitable for query-passage relevance scoring in information retrieval scenarios.