Text Classification
Transformers
Safetensors
English
roberta
security
vulnerability
cve
mitre-attack
cti
multi-label-classification
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-classification-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-classification-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-attack-technique-classification-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update citation: paper published on arXiv (2607.25572)
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Rewrite model card: usage, intended use, data, protocol, artifacts
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Update README.md
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End of training
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Complete the model card: evaluation vs zero-shot baseline, intended use, next steps
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initial commit
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