Instructions to use andersoncda77/bella-tavola-sobremesa-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use andersoncda77/bella-tavola-sobremesa-v1 with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("andersoncda77/bella-tavola-sobremesa-v1", "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
| language: pt | |
| tags: | |
| - sklearn | |
| - classification | |
| - restaurant | |
| - mlops | |
| - fastapi | |
| # bella-tavola-sobremesa-v1 | |
| Modelo de classificação binária treinado para prever **se o cliente vai pedir sobremesa** no restaurante Bella Tavola. | |
| Desenvolvido como parte do curso de MLOps (Bloco 4). | |
| ## Como usar | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| model = joblib.load( | |
| hf_hub_download("andersoncda77/bella-tavola-sobremesa-v1", "model.pkl") | |
| ) |