Text Ranking
sentence-transformers
Safetensors
Korean
English
modernbert
cross-encoder
reranker
korean
english
text-embeddings-inference
Instructions to use nlpai-lab/KURE-Reranker-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-Reranker-nano with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("nlpai-lab/KURE-Reranker-nano") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Update model card with the current MTEB-ko-retrieval benchmark results
#1
by yjoonjang - opened
- Refresh the model card to match the KURE-Reranker-base card: key characteristics, evaluation on the nine MTEB-ko-retrieval subsets (mean nDCG@10 and mean PPS), and per-dataset NDCG@10 / PPS tables.
- Add assets/pps_vs_ndcg9.png (mean nDCG@10 vs. mean PPS) referenced by the new card.
- Model weights are unchanged.
hongst changed pull request status to merged