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
File size: 425 Bytes
a6f297f | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |