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KoReranker

Korean-specific document reranking.

Overview

Field Value
Type CrossEncoder (Korean)
Library torch, transformers
Default Model Dongjin-kr/ko-reranker

Installation

pip install "autorag-research[gpu]"
# or
uv add "autorag-research[gpu]"

Configuration

_target_: autorag_research.rerankers.koreranker.KoRerankerReranker
model_name: Dongjin-kr/ko-reranker

Options

Option Type Default Description
model_name str Dongjin-kr/ko-reranker KoReranker model name
max_length int 512 Maximum input sequence length
device str None Device (auto-detected)
batch_size int 64 Batch size for multiple queries

Usage

from autorag_research.rerankers import KoRerankerReranker

reranker = KoRerankerReranker()
results = reranker.rerank("RAG란 무엇인가?", ["문서1", "문서2", "문서3"], top_k=2)

When to Use

Specifically designed for Korean language documents. Use this when:

  • Your corpus is primarily Korean text
  • You need Korean-aware relevance scoring