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:
- bef5a9ffe400cdbc057d329a7787512817bdfe43a6547f31b30777cf4e76e2f7
- Size of remote file:
- 499 MB
- SHA256:
- 89b72f8dd4ec1ae890a5177916efd824618cb34fb1958d8d043405f5f8d0b039
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