ModSSC documentation¶

Start here for a quick tour of ModSSC, its core capabilities, and the recommended next steps. If you're new, begin with Installation or Quickstart.

What is ModSSC¶

ModSSC is a modular framework for semi-supervised classification across heterogeneous modalities (audio, text, vision, tabular, graph) with research-focused abstractions and reproducible pipelines. ^[1]

Key features¶

Dataset catalog with curated keys for tabular, text, vision, audio, and graph datasets. ^[7]
Optional provider backends for OpenML, Hugging Face datasets, TFDS, torchvision, torchaudio, and PyG. ^[8]
Deterministic sampling plans (holdout/k-fold + labeling strategies) with cached split artifacts. ^[5][9][10]
Deterministic preprocessing plans with step registry and optional pretrained encoders. ^[6][11][12]
Graph construction (kNN/epsilon/anchor) and graph-derived views (attribute, diffusion, structural). ^[13][14]
Inductive and transductive method registries with method IDs. ^[15][16]
CLI tools for datasets, sampling, preprocessing, graphs, augmentation, and evaluation. ^[2][3]
Benchmark runner with YAML experiment configs (repository-only, not shipped to PyPI). ^[17][2]

Quickstart links¶

Hands-on resources¶

Version¶

Current version is 1.0.1, sourced from src/modssc/__about__.py and referenced by the Hatch version config in pyproject.toml. ^[18][2]

Project status and support¶

Status is production/stable (Development Status 5) in project metadata. ^[2]
Report issues via the GitHub tracker. ^[2]
Contributor guidance lives in the docs. ^[19]
Citation metadata is provided in CITATION.cff. ^[20]

Sources