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nlpai-lab
/
KURE-Sparse-nano

Feature Extraction
sentence-transformers
Safetensors
Korean
English
modernbert
sparse-encoder
sparse
splade
learned-sparse-retrieval
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use nlpai-lab/KURE-Sparse-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use nlpai-lab/KURE-Sparse-nano with sentence-transformers:

    from sentence_transformers import SparseEncoder
    
    model = SparseEncoder("nlpai-lab/KURE-Sparse-nano")
    
    queries = ["Which planet is known as the Red Planet?"]
    documents = [
    	"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.",
    ]
    
    query_embeddings = model.encode_query(queries)
    document_embeddings = model.encode_document(documents)
    
    similarities = model.similarity(query_embeddings, document_embeddings)
    print(similarities)
  • Notebooks
  • Google Colab
  • Kaggle
KURE-Sparse-nano / assets
786 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
yjoonjang's picture
yjoonjang
Add LACONIC, OpenSearch doc-v3-gte and splade-v3 to BEIR-13, LACONIC RTEB without the dims cap
ad2c805 verified 4 days ago
  • beir_rteb_sparse.png
    255 kB
    xet
    Add LACONIC, OpenSearch doc-v3-gte and splade-v3 to BEIR-13, LACONIC RTEB without the dims cap 4 days ago
  • kure_ndcg.png
    422 kB
    xet
    Model card: summary figure, grouped MTEB(kor, v2) table and RTEB results 6 days ago
  • ndcg_flops.png
    110 kB
    xet
    KURE-Sparse-nano 12 days ago