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BAAI
/
bge-m3-unsupervised

Sentence Similarity
sentence-transformers
PyTorch
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • sentence-transformers

    How to use BAAI/bge-m3-unsupervised with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("BAAI/bge-m3-unsupervised")
    
    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-unsupervised
4.57 GB
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  • 1 contributor
History: 14 commits
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  • config.json
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  • modules.json
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

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  • sentence_bert_config.json
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  • sentencepiece.bpe.model
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  • tokenizer_config.json
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