Instructions to use davidmcmahon/omega_guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use davidmcmahon/omega_guard with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("davidmcmahon/omega_guard", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Upload performance_report.txt with huggingface_hub
Browse files- performance_report.txt +9 -15
performance_report.txt
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===================================
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Total examples: 267
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- Malicious examples: 159
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- Normal examples: 108
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Classification Report:
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precision recall f1-score support
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0 1.00
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1
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accuracy
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macro avg
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weighted avg
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Confusion Matrix:
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[[
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[ 0
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Model Performance:
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precision recall f1-score support
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0 1.00 0.90 0.95 39
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1 0.91 1.00 0.96 43
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accuracy 0.95 82
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macro avg 0.96 0.95 0.95 82
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weighted avg 0.96 0.95 0.95 82
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Confusion Matrix:
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[[35 4]
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[ 0 43]]
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