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TyrusRC
/
haruspex

Text Classification
GLiNER2
Safetensors
English
decision-model
calibration
selective-prediction
conformal-prediction
abstention
risk-aware
security
cybersecurity
security-operations
soc
pentest
red-team
blue-team
Eval Results (legacy)
Model card Files Files and versions
xet
Community

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

  • Libraries
  • GLiNER2

    How to use TyrusRC/haruspex with GLiNER2:

    from gliner2 import AutoExtractor
    
    extractor = AutoExtractor.from_pretrained("TyrusRC/haruspex")
    
    # Extract entities
    text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."
    result = extractor.extract_entities(text, ["company", "person", "product", "location"])
    
    print(result)
  • Notebooks
  • Google Colab
  • Kaggle
haruspex / network
31.8 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
TyrusRC's picture
TyrusRC
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ad98006 verified 3 days ago
  • final
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  • calibrator.json
    98 Bytes
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  • eval.json
    100 Bytes
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  • haruspex.json
    80 Bytes
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