Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-classification-pilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-classification-pilot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-attack-technique-classification-pilot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-classification-pilot") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-attack-technique-classification-pilot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_loss": 0.6123166680335999, | |
| "eval_f1_micro": 0.3952380952380952, | |
| "eval_f1_macro": 0.1641093244848836, | |
| "eval_precision_micro": 0.288695652173913, | |
| "eval_recall_micro": 0.6264150943396226, | |
| "eval_recall_at_3": 0.4911904761904761, | |
| "eval_recall_at_5": 0.6327876984126984, | |
| "eval_runtime": 0.2477, | |
| "eval_samples_per_second": 484.423, | |
| "eval_steps_per_second": 16.147, | |
| "epoch": 40.0 | |
| } |