Sentence Similarity
Safetensors
sentence-transformers
Korean
English
PyLate
modernbert
ColBERT
late-interaction
multi-vector
feature-extraction
text-embeddings-inference
Instructions to use nlpai-lab/KURE-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-v2 with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="nlpai-lab/KURE-v2") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
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# KURE-v2
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**KURE-v2** is a Korean-English bilingual late-interaction (multi-vector) retrieval model built on
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[skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base). It encodes every token into a
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# 🔎 KURE-v2
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**KURE-v2** is a Korean-English bilingual late-interaction (multi-vector) retrieval model built on
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[skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base). It encodes every token into a
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