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Generating Documentation to Source Code in Python
Novosád, Juraj ; Nosko, Svetozár (oponent) ; Smrž, Pavel (vedoucí práce)
The aim of this work is to adapt selected language models on domain data and to develop a system that would allow their use on commonly available hardware. The models have been adapted to generate documentation for undocumented source code in the Python progra- mming language to follow the Google Style convention. A prerequisite of model adaptation was to obtain domain data and process it appropriately for the purpose of model fine-tuning. This work focuses on fine-tuning models with fewer than one billion parameters, for the sake of enabling inference even on commonly available hardware. Part of the work was to objectively evaluate the quality of the adapted models. For this reason, I developed a tool that evaluates the quality of the generated documentation on a selected corpus of models. The evaluation of the adapted models showed that they achieve comparable performance to multiply larger models for general tasks, such as gpt-3.5-turbo-0125. The result of this work is a server capable of horizontal scaling that integrates the capabilities of more than just the adapted models through an easy-to-use API.

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