Instructions to use davidmcmahon/omega_guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use davidmcmahon/omega_guard with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("davidmcmahon/omega_guard", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download vectorizer.joblib from davidmcmahon/omega_guard: direct link, hf CLI and curl.
- Browser
- Download file 31.1 kB
-
https://huggingface.co/davidmcmahon/omega_guard/resolve/main/vectorizer.joblib
- Command line
-
hf download hf://davidmcmahon/omega_guard/vectorizer.joblib
-
curl -L -o vectorizer.joblib https://huggingface.co/davidmcmahon/omega_guard/resolve/main/vectorizer.joblib
31.1 kB
- Xet hash:
- 87e8b934ecb4423afea6b4fd325d56561963a841926d7a1b62db5907a525c398
- Size of remote file:
- 31.1 kB
- SHA256:
- 815624d432e42b98aba7fe79ff8220e03aa4c43802e40b8ed4f7e6f6a99839b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.