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pratikmurali
/
pratik_cybersecurity_emb_ft

Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • sentence-transformers

    How to use pratikmurali/pratik_cybersecurity_emb_ft with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("pratikmurali/pratik_cybersecurity_emb_ft")
    
    sentences = [
        "What are the key cybersecurity challenges in healthcare?",
        "Healthcare organizations face numerous security threats.",
        "Improving digital hygiene is important for medical devices.",
        "IoT security is critical for medical equipment.",
        "HIPAA regulations require strong data protection measures.",
        "Security breaches can lead to patient data exposure."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [6, 6]
  • Transformers

    How to use pratikmurali/pratik_cybersecurity_emb_ft with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("pratikmurali/pratik_cybersecurity_emb_ft")
    model = AutoModel.from_pretrained("pratikmurali/pratik_cybersecurity_emb_ft")
  • Notebooks
  • Google Colab
  • Kaggle
pratik_cybersecurity_emb_ft
1.34 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
pratikmurali's picture
pratikmurali
Upload 12 files
6dffe4a verified about 1 year ago
  • 1_Pooling
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  • eval
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  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    1.4 kB
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  • config.json
    584 Bytes
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  • config_sentence_transformers.json
    275 Bytes
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  • model.safetensors
    1.34 GB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    53 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
    1.41 kB
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  • vocab.txt
    232 kB
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