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KhaledReda
/
all-MiniLM-L6-v47-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:19507210
loss:CoSENTLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KhaledReda/all-MiniLM-L6-v47-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-v47-pair_score with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v47-pair_score")
    
    sentences = [
        "firstvoc crem f fall hr",
        "sandal praia sandals copacabana sandals strappy sandals gladiator sandals faux gold sandals women nine crimes sandals praia 38 gladiator style strappy faux leather brown praia de copacabana beach is a golden sand hotspot outside rio de janeiro. it is a 2.2-mile beach lined with action be it food stalls beach bars or hotels - and a constantly busy promenade. strappy gladiator sandals. faux leather.",
        "home scent event portable aroma fragrance oil bottle portable diffuser fragrance oil aroma oils event aroma 10 ml fragrance oil bottle suitable to use with the portable diffuser.",
        "stomach medicine digest eze 20/cap 2ex.new stomach cap"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v47-pair_score
92 MB
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  • 1 contributor
History: 2 commits
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KhaledReda
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37be1c5 verified about 2 months ago
  • 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
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.txt
    232 kB
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