Feature Extraction
Transformers
static_canary
ctf
model-scanning
supply-chain-canary
static-analysis
custom_code
Instructions to use WWTCyberLab/static-canary-pt-loader-risk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WWTCyberLab/static-canary-pt-loader-risk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="WWTCyberLab/static-canary-pt-loader-risk", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WWTCyberLab/static-canary-pt-loader-risk", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9819b92882ac8bc0d329d27c98f8d8e93eee1889033cddcfba0361e3c381bbf
- Size of remote file:
- 172 MB
- SHA256:
- 793870f9c93b3acb2bb4429fd24d6cb928b4830d25be7b804240b7c7fbb401c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.