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")# 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
File size: 1,028 Bytes
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2026-07-17T08:37:48,VulnTrain,b65fe9a7-039a-42b3-bf76-12cbf8128517,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,282.42941046506166,0.005960472299614022,2.1104290412953895e-05,70.00064626457315,587.2460854916695,70.0,0.005298393513150807,0.046028169044717515,0.005298009225322553,0.05662457178319088,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-106-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1327,49.6098,2015.336296081543,machine,1.2448028673835125,71.81182795698925,2.2225806451612904,44.61768197500577,N,1.0,0.0
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