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p0x0q-dev
/
bge-m3-sparse-experimental

Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use p0x0q-dev/bge-m3-sparse-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use p0x0q-dev/bge-m3-sparse-experimental with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("p0x0q-dev/bge-m3-sparse-experimental")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
bge-m3-sparse-experimental / imgs
1.98 MB
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  • 2 contributors
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