Text Classification
Transformers
Safetensors
multi_task_unixcoder
feature-extraction
Code
Vulnerability
Detection
custom_code
Instructions to use mahdin70/UnixCoder-Primevul-BigVul with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahdin70/UnixCoder-Primevul-BigVul with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mahdin70/UnixCoder-Primevul-BigVul", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mahdin70/UnixCoder-Primevul-BigVul", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f02eb4ec83aa4bd9dfbc91b836ebd84ff1c898bd515617170ee2a12626d12fb
- Size of remote file:
- 5.24 kB
- SHA256:
- 76f33bf8de6f74384eb3e6a6f29a043aa91b89951dceb8400511a56fee263b52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.