Software framework for hyperparameters optimization of models with additive regularization

The processing of unstructured data, such as natural language texts, is one of the urgent tasks in the development of intelligent products. In turn, topic modeling as a method of working with unmarked and partially marked text data is a natural choice for analyzing document bodies and creating vector representations. In this regard, it is especially important to train high-quality thematic models in a short time which is possible with the help of the proposed framework. The developed framework implements an evolutionary approach to optimizing hyperparameters of models with additive regularization and high results on quality metrics (coherence, NPMI). To reduce the computational time, a mode of working with surrogate models is presented which provides acceleration of calculations up to 1.8 times without loss of quality. The effectiveness of the framework is demonstrated on three datasets with different statistical characteristics. The results obtained exceed similar solutions by an average of 20 % in coherence and 5 % in classification quality for two of the three datasets. A distributed version of the framework has been developed for conducting experimental studies of topic models. The developed framework can be used by users without special knowledge in the field of topic modeling due to the default data processing pipeline. The results of the work can be used by researchers to analyze topic models and expand functionality.