Before revision:  In this work, we propose a software framework Knodle that provides a modularization for separating weak data annotations, powerful deep learning models, and methods for improving weakly supervised training . This modularization gives the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction.
After revision:  In this work, we propose a software framework Knodle that provides a modularization for separating weak data annotations, powerful deep learning models, and methods for improving weakly supervised training as separate, modular components. The standardized interfaces between these independent parts account for data- and model-agnostic weak supervision method development, but still allow the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction.