Instructions to use muthuk1/fairrelay-workload-scoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use muthuk1/fairrelay-workload-scoring with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("muthuk1/fairrelay-workload-scoring", "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
- Xet hash:
- 23d0daabd9baa4bacc2f2b4eceec5b6e59b8ae8662121b22f22c19908ce45c46
- Size of remote file:
- 614 kB
- SHA256:
- 6492deea502e95385d355ca28e76f614cdbf527a701872b76ec731a761dc6f46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.