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KhaledReda
/
all-MiniLM-L6-v24-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:12582766
loss:CoSENTLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KhaledReda/all-MiniLM-L6-v24-pair_score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use KhaledReda/all-MiniLM-L6-v24-pair_score with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v24-pair_score")
    
    sentences = [
        "police pyramid north",
        "silver sparkle category fashion footwear shoe heels tags silver heels women heels thin straps heels rhinestones heels heels sparkle heels keywords heels sparkle heels attrs gender women brand unidentical generic name heels size 37 features thin straps sparkle material rhinestones color silver description thin straps with rhinestones heels. height 7 cm.",
        "colorful printed long cardigan category fashion casual wear outerwear outerwear tags cardigan colorful cardigan long cardigan printed cardigan keywords cardigan colorful cardigan long cardigan printed cardigan attrs gender women brand u modest generic name cardigan features long types of fashion styles casual everyday wear color colorful pattern printed",
        "courtly black category fashion footwear shoe flats tags flat shoe black shoe black flat shoe courtly keywords courtly attrs color black"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v24-pair_score
91.9 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
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KhaledReda
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    90.9 MB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    57 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
    1.46 kB
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  • vocab.txt
    232 kB
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