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
Commit History
-
v2-final: model card
Verified signature381338c -
v2-final: metrics
Verified signaturec9e74a0 -
v2-final: regularized, no overfitting (gap=0.016)
Verified signature461e949 -
v2: updated model card
Verified signaturef45970c -
v2: updated metrics
Verified signatured8f5f2e -
v2: retrained with realistic data
Verified signatured2425db -
Add model card for Workload Scoring Model
Verified signature0c0a658 -
Add metrics for Workload Scoring Model
Verified signature7327b7b -
Add Workload Scoring Model model
Verified signature426ee51 -
initial commit
Verified signature8836003