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LDCC
/
bge-m3

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

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

  • Libraries
  • sentence-transformers

    How to use LDCC/bge-m3 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("LDCC/bge-m3")
    
    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 / 1_Pooling
382 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
psyche's picture
psyche
Upload config.json
57fb4b6 over 1 year ago
  • 1_Pooling_config.json
    191 Bytes
    Upload 1_Pooling_config.json over 1 year ago
  • config.json
    191 Bytes
    Upload config.json over 1 year ago