Instructions to use herbetdesign/foodintent-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use herbetdesign/foodintent-ml with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("herbetdesign/foodintent-ml", "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
FoodIntent ML
Experimental TF-IDF intent classifier. Every prediction requires human review.
Evaluation
- Selected model: multinomial_nb
- Curated test macro F1: 1.0000
- SID test macro F1: 0.9580
Data and limitations
Both the SID source and the project-authored delivery supplement are synthetic. These metrics do not estimate performance on real customers. Confidence values are not calibrated. Do not automate customer-impacting decisions.
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