ModSSC is a modular framework for semi-supervised classification across heterogeneous modalities (text, vision, tabular, graph). It is designed for academic research with reproducible pipelines and extensible method registries.
Pick the path that fits your goal: learn the concepts, run examples, or dive into the research.
- Documentation: overview and concepts.
- Getting started: install and first run.
- CLI and API reference: full command and API list.
- Examples: small scripts in examples/.
- Notebooks: interactive demos in notebooks/.
- Paper (arXiv): research reference.
- Articles (Medium): deeper explanations.
If you use ModSSC in research, please cite:
@misc{barbaux2025modsscmodularframeworksemisupervised,
title={ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data},
author={Melvin Barbaux},
year={2025},
eprint={2512.13228},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2512.13228},
}If this work resonates with you, feel free to give the project a star on GitHub, fork it to experiment on your own data, or jump in and contribute. Issues, discussions, and pull requests are more than welcome.
You can also start a discussion on GitHub Discussions.
MIT License