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KhaledReda
/
all-MiniLM-L6-v45-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:10827162
loss:CoSENTLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KhaledReda/all-MiniLM-L6-v45-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-v45-pair_score with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v45-pair_score")
    
    sentences = [
        "unisex",
        "daisy white category tags daisy keywords daisy attrs gender women brand dodici generic name product name daisy types of styles casual strap style strap crossbody strap color white description amp with 2 hand",
        "giant fried lemon roll 8 category tags hot roll roll hot roll giant roll lemon keywords giant roll lemon attrs modifier fried",
        "category mayonnaise tags chili sweet and mayo keywords chili sweet and mayo"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v45-pair_score
91.9 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
KhaledReda's picture
KhaledReda
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  • 1_Pooling
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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
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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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