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naver
/
efficient-splade-VI-BT-large-doc

Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
distilbert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
sparse-encoder
sparse
asymmetric
text-embeddings-inference
Model card Files Files and versions
xet
Community
3

Instructions to use naver/efficient-splade-VI-BT-large-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use naver/efficient-splade-VI-BT-large-doc with sentence-transformers:

    from sentence_transformers import SparseEncoder
    
    model = SparseEncoder("naver/efficient-splade-VI-BT-large-doc")
    
    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)
  • Inference
  • Notebooks
  • Google Colab
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Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

VORTEXRAG: 7-Layer RAG — Causal Drift Filtering + Context Poison Guard [paper + code + demo]

#3 opened 4 months ago by
vigneshwar234

Adding `safetensors` variant of this model

#1 opened over 3 years ago by
SFconvertbot
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