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jobby32
/
ms-marco-cybersecurity-MiniLM-L6-v2

Text Ranking
sentence-transformers
Safetensors
English
bert
cross-encoder
reranking
information-retrieval
finetuned
cybersecurity
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use jobby32/ms-marco-cybersecurity-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use jobby32/ms-marco-cybersecurity-MiniLM-L6-v2 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("jobby32/ms-marco-cybersecurity-MiniLM-L6-v2")
    
    query = "Which planet is known as the Red Planet?"
    passages = [
    	"Venus is often called Earth's twin because of its similar size and proximity.",
    	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
    	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
    ]
    
    scores = model.predict([(query, passage) for passage in passages])
    print(scores)
  • Notebooks
  • Google Colab
  • Kaggle
ms-marco-cybersecurity-MiniLM-L6-v2
91.8 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
jobby32's picture
jobby32
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  • .gitattributes
    1.52 kB
    initial commit 25 days ago
  • README.md
    2.05 kB
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  • config.json
    920 Bytes
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  • model.safetensors
    90.9 MB
    xet
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    443 Bytes
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  • vocab.txt
    232 kB
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