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imaneb942
/
MNLP_M2_document_encoder

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

Instructions to use imaneb942/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use imaneb942/MNLP_M2_document_encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("imaneb942/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 / onnx
2.34 GB
Ctrl+K
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  • 1 contributor
History: 1 commit
imaneb942's picture
imaneb942
Upload 24 files
90e6ade verified 12 months ago
  • config.json
    657 Bytes
    Upload 24 files 12 months ago
  • model.onnx
    1.34 GB
    xet
    Upload 24 files 12 months ago
  • model_O4.onnx
    668 MB
    xet
    Upload 24 files 12 months ago
  • model_qint8_avx512_vnni.onnx
    337 MB
    xet
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  • special_tokens_map.json
    132 Bytes
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  • tokenizer.json
    742 kB
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  • tokenizer_config.json
    355 Bytes
    Upload 24 files 12 months ago
  • vocab.txt
    262 kB
    Upload 24 files 12 months ago