Instructions to use neuralsentry/vulnerabilityDetection-StarEncoder-Devign with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnerabilityDetection-StarEncoder-Devign with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnerabilityDetection-StarEncoder-Devign")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnerabilityDetection-StarEncoder-Devign") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnerabilityDetection-StarEncoder-Devign", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from neuralsentry/vulnerabilityDetection-StarEncoder-Devign: direct link, hf CLI and curl.
- Browser
- Download file 386 Bytes
-
https://huggingface.co/neuralsentry/vulnerabilityDetection-StarEncoder-Devign/resolve/main/eval_results.json
- Command line
-
hf download hf://neuralsentry/vulnerabilityDetection-StarEncoder-Devign/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/neuralsentry/vulnerabilityDetection-StarEncoder-Devign/resolve/main/eval_results.json
386 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.7019009565322678, | |
| "eval_f1": 0.6654891304347826, | |
| "eval_loss": 0.7599468231201172, | |
| "eval_precision": 0.7660306537378793, | |
| "eval_recall": 0.5882776843622388, | |
| "eval_roc_auc": 0.7028302484311194, | |
| "eval_runtime": 28.5103, | |
| "eval_samples": 8259, | |
| "eval_samples_per_second": 289.685, | |
| "eval_steps_per_second": 6.454 | |
| } |