modssc 1.2.1

ModSSC: a modular framework for semi-supervised classification on heterogeneous data

ModSSC

ModSSC is a modular framework for semi-supervised classification across heterogeneous modalities (text, vision, tabular, graph, audio). It is designed for academic research with reproducible pipelines and extensible method registries.

Resources

Pick the path that fits your goal: learn the concepts, run examples, or dive into the research.

Docs and reference

• Documentation: overview and concepts.
• Choose your path: pick the right entrypoint.
• Getting started: install and first run.
• Extras and platforms: choose optional dependencies.
• CLI and API reference: full command and API list.
• Troubleshooting: common failures and fixes.

If you use benchmark configs with environment placeholders, set MODSSC_OUTPUT_DIR, MODSSC_DATASET_CACHE_DIR, and MODSSC_PREPROCESS_CACHE_DIR before running. See the Configuration reference for examples.

Examples

• Examples: small scripts in examples/.
• Notebooks: interactive demos in notebooks/.
• Examples guide: script index and recommendations.
• Notebook tour: interactive entrypoints by topic.

Research and articles

• Paper (arXiv): research reference.
• Articles (Medium): deeper explanations.

Citation

If you use ModSSC in research, please cite:

@misc{barbaux2026modsscmodularframeworksemisupervised,
      title={ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data},
      author={Melvin Barbaux and Samia Boukir},
      year={2026},
      eprint={2512.13228},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2512.13228},
}

Contributing

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.

License

MIT License