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opensearch-project
/
opensearch-neural-sparse-encoding-v1

Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
fill-mask
learned sparse
opensearch
retrieval
passage-retrieval
query-expansion
document-expansion
bag-of-words
sparse-encoder
sparse
splade
text-embeddings-inference
Model card Files Files and versions
xet
Community
5

Instructions to use opensearch-project/opensearch-neural-sparse-encoding-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use opensearch-project/opensearch-neural-sparse-encoding-v1 with sentence-transformers:

    from sentence_transformers import SparseEncoder
    
    model = SparseEncoder("opensearch-project/opensearch-neural-sparse-encoding-v1")
    
    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)
  • Transformers

    How to use opensearch-project/opensearch-neural-sparse-encoding-v1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="opensearch-project/opensearch-neural-sparse-encoding-v1")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1")
    model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

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

#5 opened 5 months ago by
vigneshwar234

Add onnx model version of the checkpoint for accelerated inference

#1 opened over 2 years ago by
bergum
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