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 train_results.json from neuralsentry/vulnerabilityDetection-StarEncoder-Devign: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/neuralsentry/vulnerabilityDetection-StarEncoder-Devign/resolve/main/train_results.json
- Command line
-
hf download hf://neuralsentry/vulnerabilityDetection-StarEncoder-Devign/train_results.json
-
curl -L -o train_results.json https://huggingface.co/neuralsentry/vulnerabilityDetection-StarEncoder-Devign/resolve/main/train_results.json
195 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.4179859999087242, | |
| "train_runtime": 3050.636, | |
| "train_samples": 24774, | |
| "train_samples_per_second": 81.209, | |
| "train_steps_per_second": 1.806 | |
| } |