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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad2e90d918949d68a5e9139f4fae70010e4e33cc30d7a4f9f2aadeed9d715abb
- Size of remote file:
- 497 MB
- SHA256:
- 589db65adf81554c4c8aea71f0f55bfb9ee0eb4bf3d62cfb12d474f4ea2a5e32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.