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
- Xet hash:
- ab7f6be7c375db0a6f2eb8809cfd7c9b442489477a970fe0ebf3252fa0e2f599
- Size of remote file:
- 5.27 kB
- SHA256:
- 7975c81f9f47f068395171f133a5a5ce558aa14b792a7d89f0f1f8a960380b70
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