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) - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Move the figure legend slightly left of the x=1000 tick
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Align the figure legend with the x=1000 tick
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Use full KURE-Reranker names in the figure and fix a missing space in the PPS note
Verified signature- Figure labels now read KURE-Reranker-base / KURE-Reranker-nano. - Fix a missing space in the PPS note under the results table. - Model weights are unchanged.f5a1703 -
Update model card with the current MTEB-ko-retrieval benchmark results (#1)
- Update model card with the current MTEB-ko-retrieval benchmark results (7d9e2ee042b33fd8fad3bda783557af1d05ab617) Co-authored-by: Youngjoon Jang <yjoonjang@users.noreply.huggingface.co>d4e648c
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init
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