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
| { | |
| "eval_loss": 0.5858569145202637, | |
| "eval_f1_micro": 0.379041248606466, | |
| "eval_f1_macro": 0.14800403050048708, | |
| "eval_precision_micro": 0.2689873417721519, | |
| "eval_recall_micro": 0.6415094339622641, | |
| "eval_recall_at_3": 0.5336834733893557, | |
| "eval_recall_at_5": 0.6156162464985994, | |
| "eval_runtime": 0.2473, | |
| "eval_samples_per_second": 481.127, | |
| "eval_steps_per_second": 16.172, | |
| "epoch": 40.0 | |
| } |