Instructions to use neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9258982035928144, | |
| "eval_f1": 0.9438457175269428, | |
| "eval_loss": 0.22528469562530518, | |
| "eval_precision": 0.9486887115165337, | |
| "eval_recall": 0.9390519187358917, | |
| "eval_roc_auc": 0.9195259593679458, | |
| "eval_runtime": 1.9774, | |
| "eval_samples": 1336, | |
| "eval_samples_per_second": 675.629, | |
| "eval_steps_per_second": 5.563 | |
| } |