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
File size: 385 Bytes
692cf65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |