Instructions to use ModelForge/spam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ModelForge/spam-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("ModelForge/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
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Download README.md from ModelForge/spam-classifier: direct link, hf CLI and curl.
- Browser
- Download file 333 Bytes
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https://huggingface.co/ModelForge/spam-classifier/resolve/369ff90bfa0f8bbdbfbb45f82c3a384df708f3a8/README.md
- Command line
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hf download hf://ModelForge/spam-classifier@369ff90bfa0f8bbdbfbb45f82c3a384df708f3a8/README.md
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curl -L -o README.md https://huggingface.co/ModelForge/spam-classifier/resolve/369ff90bfa0f8bbdbfbb45f82c3a384df708f3a8/README.md
333 Bytes
| license: mit | |
| language: | |
| - en | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| pipeline_tag: text-classification | |
| library_name: sklearn | |
| tags: | |
| - spam | |
| - text-classification | |
| - binary-classification | |
| - scikit-learn | |
| - english | |
| - joblib | |
| # SMS Spam Classifier | |
| A simple SMS spam classifier trained on real-world data (UCI SMS Spam dataset). |