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
File size: 427 Bytes
40ad73e 3b98a1d 40ad73e | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |