Instructions to use ridhimalawade/spam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ridhimalawade/spam-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("ridhimalawade/spam-classifier", "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
| license: mit | |
| tags: | |
| - text-classification | |
| - spam-detection | |
| - sklearn | |
| # Spam Classifier (Synthetic) | |
| A Logistic Regression model trained on synthetic spam vs ham data. | |
| ## Performance | |
| - Test accuracy: **100.00%** | |
| - Dataset: 200 synthetic samples | |