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dostoevskyIdiot
/
intfloat-e5-large-v2-jaiv-v2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:257891
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use dostoevskyIdiot/intfloat-e5-large-v2-jaiv-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use dostoevskyIdiot/intfloat-e5-large-v2-jaiv-v2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("dostoevskyIdiot/intfloat-e5-large-v2-jaiv-v2")
    
    sentences = [
        "married woman cheats with a low life creep",
        "Real Documentary: The Moment When A Divorce Wanting Wife Cheats categorized as Nymphomaniac, Married Woman",
        "Hot Married Woman Athlete categorized as Mature Woman, Married Woman, Sports",
        "This Married Woman Was Betrayed By Her Friends And Got Creampie Fucked By A Fucking Low Life Creep Yuko Shiraki categorized as Mature Woman, Married Woman, Cheating Wife"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
intfloat-e5-large-v2-jaiv-v2
1.34 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
dostoevskyIdiot's picture
dostoevskyIdiot
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  • 1_Pooling
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  • eval
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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
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    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
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  • tokenizer.json
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  • tokenizer_config.json
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