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mohsayed
/
para_tr_enar_1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:17702
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use mohsayed/para_tr_enar_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use mohsayed/para_tr_enar_1 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("mohsayed/para_tr_enar_1")
    
    sentences = [
        "zonivan 100 mg 20 caps.",
        "fuci-top 2% cream 15 gm",
        "فاستاتينال 10/40مجم 7 قرص",
        "زونيفان 100 مجم 20 كبسولة"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
para_tr_enar_1
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  • 1 contributor
History: 2 commits
mohsayed's picture
mohsayed
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f3dda05 verified about 1 year ago
  • 1_Pooling
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  • .gitattributes
    1.62 kB
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  • README.md
    24 kB
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  • config.json
    619 Bytes
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  • config_sentence_transformers.json
    205 Bytes
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  • model.safetensors
    471 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.45 kB
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  • unigram.json
    14.8 MB
    xet
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