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multi-vector-encoder
/
ModernBERT-base-en-v1

Feature Extraction
sentence-transformers
Safetensors
English
modernbert
multi-vector
colbert
late-interaction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use multi-vector-encoder/ModernBERT-base-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use multi-vector-encoder/ModernBERT-base-en-v1 with sentence-transformers:

    from sentence_transformers import MultiVectorEncoder
    
    model = MultiVectorEncoder("multi-vector-encoder/ModernBERT-base-en-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)
  • Notebooks
  • Google Colab
  • Kaggle
ModernBERT-base-en-v1 / 2_Dense
9.44 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
tomaarsen's picture
tomaarsen HF Staff
Add new MultiVectorEncoder model
d905f99 verified 2 days ago
  • config.json
    259 Bytes
    Add new MultiVectorEncoder model 2 days ago
  • model.safetensors
    9.44 MB
    xet
    Add new MultiVectorEncoder model 2 days ago