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KhaledReda
/
all-MiniLM-L6-v52-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:29220823
loss:CoSENTLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KhaledReda/all-MiniLM-L6-v52-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-v52-pair_score with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v52-pair_score")
    
    sentences = [
        "women nightgown",
        "wet cat food fancy feast tuna feast grilled purina fancy feast cat grilled tuna fancy feast grilled tuna barbecued tuna fancy feast tuna feast barbecued fancy feast tuna feast meshwe fancy feast tuna feast meshwee fancy feast tuna feast meshwi meshwe tuna meshwee tuna meshwi tuna purina fancy feast grilled tuna",
        "cake moist carrot moist",
        "polyester nightgown"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v52-pair_score
92.1 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
    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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