Text Classification
Transformers
Safetensors
English
roberta
security
vulnerability
cve
mitre-attack
cti
multi-label-classification
Generated from Trainer
Eval Results (legacy)
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_loss": 0.6409852504730225, | |
| "eval_f1_micro": 0.3898678414096916, | |
| "eval_f1_macro": 0.19101586412044438, | |
| "eval_precision_micro": 0.2739938080495356, | |
| "eval_recall_micro": 0.6755725190839694, | |
| "eval_recall_at_3": 0.5181497175141243, | |
| "eval_recall_at_5": 0.6439971751412429, | |
| "eval_runtime": 0.2952, | |
| "eval_samples_per_second": 399.752, | |
| "eval_steps_per_second": 13.551, | |
| "epoch": 40.0 | |
| } |