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naver
/
splade-v3-lexical

Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
bert
splade
sparse-encoder
sparse
asymmetric
inference-free
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use naver/splade-v3-lexical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use naver/splade-v3-lexical with sentence-transformers:

    from sentence_transformers import SparseEncoder
    
    model = SparseEncoder("naver/splade-v3-lexical")
    
    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

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  • 1_SpladePooling
    sentence_transformers_support (#2) over 1 year ago
  • 2_Router
    sentence_transformers_support (#2) over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    3.85 kB
    sentence_transformers_support (#2) over 1 year ago
  • config.json
    682 Bytes
    add model over 2 years ago
  • config_sentence_transformers.json
    274 Bytes
    sentence_transformers_support (#2) over 1 year ago
  • data_args.json
    409 Bytes
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  • model_args.json
    292 Bytes
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  • modules.json
    388 Bytes
    sentence_transformers_support (#2) over 1 year ago
  • pytorch_model.bin
    438 MB
    xet
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  • sentence_bert_config.json
    57 Bytes
    sentence_transformers_support (#2) over 1 year ago
  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
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  • training_args.json
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  • vocab.txt
    232 kB
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