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cisco-ai
/
SecureBERT2.0-biencoder

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
securebert
IR
docembedding
Generated from Trainer
dataset_size:35705
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use cisco-ai/SecureBERT2.0-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use cisco-ai/SecureBERT2.0-biencoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("cisco-ai/SecureBERT2.0-biencoder")
    
    sentences = [
        "What is the primary responsibility of the Information Security Oversight Committee in an organization?",
        "Least privilege",
        "By searching for repeating ciphertext sequences at fixed displacements.",
        "Ensuring and supporting information protection awareness and training programs"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
SecureBERT2.0-biencoder
600 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 17 commits
cisco-ehsan's picture
cisco-ehsan
Update README.md
ef2b2f1 verified 6 months ago
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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
    596 MB
    xet
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  • modules.json
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
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