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ModSSC

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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

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

Research and articles

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

Metadata

Release files for modssc 1.2.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for modssc 1.2.2
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modssc-1.2.2.tar.gz 684.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for modssc 1.2.2
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modssc-1.2.2-py3-none-any.whl Python 3 none any Details

Total release size: 1.2 MB

Release files / modssc-1.2.2.tar.gz

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