Instructions to use neuralsentry/vulnfixClassification-StarEncoder-DCMB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnfixClassification-StarEncoder-DCMB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnfixClassification-StarEncoder-DCMB")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCMB") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCMB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.9769967138162595, | |
| "eval_f1": 0.9777347531461762, | |
| "eval_loss": 0.17970030009746552, | |
| "eval_precision": 0.98413140311804, | |
| "eval_recall": 0.9714207199780159, | |
| "eval_roc_auc": 0.977228217131865, | |
| "eval_runtime": 7.2505, | |
| "eval_samples": 6999, | |
| "eval_samples_per_second": 965.318, | |
| "eval_steps_per_second": 7.586 | |
| } |