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 evaluation: official Qwen3-Reranker and KaLM-Reranker-V1-Large-R2 added, seq-cls variants and ettin removed
#3
by yjoonjang - opened
- Tables now use the official Qwen/Qwen3-Reranker-{0.6B,4B,8B} instead of the tomaarsen seq-cls conversions, add KaLM-Embedding/KaLM-Reranker-V1-Large-R2, and drop cross-encoder/ettin-reranker-1b-v1 (not multilingual).
- New figure; caption notes the non-uniform x-axis.
- PPS note: same ~550 sampled pairs per task for every model; KaLM uses sdpa.
- Model weights are unchanged.
yjoonjang changed pull request status to merged