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:
- 87f47ed8ff001c8ca4e49d02b1b7556fc2081a294ba4af5e3f016af916dc276a
- Size of remote file:
- 499 MB
- SHA256:
- f968954d26d8284398ee60d387e3d162ec4e28b1beb1542bc754ea43bcff1a50
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