Semi-supervised classification leverages both labeled and unlabeled data to improve predictive performance, but existing software support remains fragmented across methods, learning settings, and data modalities. We introduce ModSSC, an open source Python framework for inductive and transductive semi-supervised classification designed to support reproducible and controlled experimentation. ModSSC provides a modular and extensible software architecture centered on reusable semi-supervised learning components, stable abstractions, and fully declarative experiment specification. Experiments are defined through configuration files, enabling systematic comparison across heterogeneous datasets and model backbones without modifying algorithmic code. ModSSC 1.0.0 is released under the MIT license with full documentation and automated tests, and is available at https://github.com/ModSSC/ModSSC. The framework is validated through controlled experiments reproducing established semi-supervised learning baselines across multiple data modalities.

翻译：半监督分类通过同时利用有标签和无标签数据来提升预测性能，但现有软件支持在方法、学习设置和数据模态方面仍然较为分散。本文介绍ModSSC，一个用于归纳式与直推式半监督分类的开源Python框架，旨在支持可复现且受控的实验研究。ModSSC提供以可复用半监督学习组件、稳定抽象层和完全声明式实验配置为核心的模块化可扩展软件架构。实验通过配置文件定义，无需修改算法代码即可在异构数据集和模型主干之间进行系统性比较。ModSSC 1.0.0版本采用MIT许可证发布，提供完整文档和自动化测试，可通过https://github.com/ModSSC/ModSSC获取。该框架通过在多种数据模态上复现经典半监督学习基线的受控实验进行了验证。