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Alexhuou
/
MNLP_M2_document_encoder

Sentence Similarity
sentence-transformers
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Alexhuou/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Alexhuou/MNLP_M2_document_encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Alexhuou/MNLP_M2_document_encoder")
    
    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
MNLP_M2_document_encoder
1.34 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Alexhuou's picture
Alexhuou
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1131d58 verified 12 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 12 months ago
  • README.md
    67.9 kB
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  • config.json
    624 Bytes
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  • config_sentence_transformers.json
    210 Bytes
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  • model.safetensors
    1.34 GB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.41 kB
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  • vocab.txt
    232 kB
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