Instructions to use CircuitNotion/africa-crop-yield-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use CircuitNotion/africa-crop-yield-predictor with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("CircuitNotion/africa-crop-yield-predictor", "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
Africa Crop Yield Predictor
scikit-learn HistGradientBoostingRegressor pipeline that predicts crop
yield (metric tonnes per hectare) for Sub-Saharan African countries/regions.
Files
crop_yield_model.joblibโ trained sklearn pipelinemodel_meta.jsonโ metrics, countries, crops, seasonsconfig.yamlโ training/inference config
Load
import joblib
from huggingface_hub import hf_hub_download
path = hf_hub_download("CircuitNotion/africa-crop-yield-predictor", "crop_yield_model.joblib")
model = joblib.load(path)
Train/inference code lives in the companion project repository.
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