The continuous improvement of artificial intelligence/machine learning is leading to an increasing search for the wider application of these technological solutions not only to structured data but also to unstructured ones. In order to apply data science to language processing, an area has emerged - natural language processing (NLP). Natural language processing is the computer analysis and processing of natural language (which can be spoken and written) using a variety of technologies aimed at adapting human language to various tasks or computer programs using linguistic methods.

At present, natural language processing is finding more and more different ways to adapt to real practical problems. These tasks can range from searching for meaningful information in unstructured data (Pande and Merchant, 2018), analyzing sentiments (Yang et al., 2020; Dang et al., 2020; Mishev et al., 2020), and translating the text into another language ( Xia et al., 2019; Gheini et al., 2021) to fully automated human-level text creation (Wolf et al., 2019; Topal et al., 2021). The data set for this study consists of 350,928 observations/business names (299,964 observations in the training sample and 50,964 observations in the test sample). These data were collected using the websites of start-ups from around the world. The aim of this study is to apply natural language modeling models of transformer architecture to generate high-quality business names.