Text Classification
sentence-transformers
Joblib
Scikit-learn
safety
malware
code
multilingual
red-team
Instructions to use NecroMOnk/malicious-coding-intent-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NecroMOnk/malicious-coding-intent-v6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NecroMOnk/malicious-coding-intent-v6") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Scikit-learn
How to use NecroMOnk/malicious-coding-intent-v6 with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("NecroMOnk/malicious-coding-intent-v6", "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
Update risk routing model card
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README.md
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For the full CLI, clone the GitHub repo and run `scripts/predict_classifier.py`.
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```
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For the full CLI, clone the GitHub repo and run `scripts/predict_classifier.py`.
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The CLI reports the binary label, raw malicious-intent score, top category
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scores, and a derived routing tier:
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- `low`: normal downstream route
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- `suspicious`: pass with safety context / constrained route
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- `high`: malicious-intent route
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The routing tier is a policy layer over the binary score, not a separately
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trained three-class model. Use `--jsonl` for structured gateway output.
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