Text Classification
Transformers
Safetensors
English
roberta
vulnerability
cybersecurity
security
cve
mitre-attack
attack-techniques
Generated from Trainer
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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57282aa73f4e8a85e035937ef081bbac60bd0a35643e61721faf81e132798f81
- Size of remote file:
- 5.27 kB
- SHA256:
- db0dc8f71629bbe3179b8ee7446ca7380373dc2885ee224a5385938421e73c4e
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