Instructions to use sandhyaaaaaaaaa/mlops-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use sandhyaaaaaaaaa/mlops-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("sandhyaaaaaaaaa/mlops-model", "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 README.md from sandhyaaaaaaaaa/mlops-model: direct link, hf CLI and curl.
- Browser
- Download file 247 Bytes
-
https://huggingface.co/sandhyaaaaaaaaa/mlops-model/resolve/main/README.md
- Command line
-
hf download hf://sandhyaaaaaaaaa/mlops-model/README.md
-
curl -L -o README.md https://huggingface.co/sandhyaaaaaaaaa/mlops-model/resolve/main/README.md
247 Bytes
metadata
tags:
- sklearn
- random-forest
- mlops-pipeline
Model
Trained automatically via GitHub Actions CI/CD. Data and pipeline versioned with DVC.
Metrics
- accuracy: 0.9474
- precision: 0.9583
- recall: 0.9583
- f1: 0.9583