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
| { | |
| "eval_loss": 0.620545506477356, | |
| "eval_f1_micro": 0.4171494785631518, | |
| "eval_f1_macro": 0.2027471380403296, | |
| "eval_precision_micro": 0.3005008347245409, | |
| "eval_recall_micro": 0.6818181818181818, | |
| "eval_recall_at_3": 0.4821628651460584, | |
| "eval_recall_at_5": 0.6858743497398959, | |
| "eval_runtime": 0.1973, | |
| "eval_samples_per_second": 603.064, | |
| "eval_steps_per_second": 10.136, | |
| "epoch": 40.0 | |
| } |