KURE
SOTA Korean retrieval embedding model — 1st place on MTEB-ko-retrieval leaderboard.
Led the lab’s flagship Korean retrieval project; curated large-scale training datasets and trained a dense retriever that achieved State-of-the-Art (1st place) on the MTEB-ko-retrieval leaderboard (as of Aug. 2025).
- Designed and maintained
MTEB-ko-retrieval, establishing a comprehensive evaluation suite and standardized public leaderboard for the Korean IR community - Open-sourced the framework, achieving 200+ GitHub stars and 1.1M+ cumulative downloads on Hugging Face
- Awarded Best Oral Presentation at HCLT 2025
| GitHub | HuggingFace |