Text Classification
Transformers
Safetensors
English
roberta
security
vulnerability
cve
mitre-attack
cti
multi-label-classification
negative-result
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded 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-llm-expanded 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-llm-expanded")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 063dca9c6f46f8b5cd93102d9cacd5400d13cef6704014a5ffd717bd49a5ce6e
- Size of remote file:
- 5.27 kB
- SHA256:
- e38d2d38e85f26a9668445c4c4e1c3c7fa695479f21edfe6241d5a83a21434c9
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