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The evaluation of scientific publications is a cornerstone of scholarly research, providing essential insights into the impact, significance, and intellectual contributions of research outputs.  Traditional bibliometric indicators, including Impact Factor (IF), h-index, and citation counts, have historically been the dominant measures to assess research quality.  However, with the rapid evolution of Artificial Intelligence (AI) and its increasing integration into various scientific disciplines, these conventional evaluation methodologies are being reevaluated due to the

This article presents the mathematical modeling of the multi-label classification task of job description texts aimed at the automatic detection of working conditions and social benefits, which can enhance communication efficiency between employers and job seekers.  The proposed approach is based on the use of the transformer-based BERT neural network, pre-trained on a multilingual corpus.  The dataset was constructed by collecting job postings from the three largest Ukrainian job search platforms: Work.ua, Robota.ua, and Jooble.org.  The collected texts were augmented