Publikace
Abstrakt
Textual data is the basis for most of historical researches. This circumstance makes the development of methods and technologies of natural language processing especially significant for historical science.
In recent years, deep learning methods have dominated the field of natural language processing. Many variants of large pre-trained language models have emerged.
This article analyzes the experience of creating language models based on transformers for historical languages. Possible risks and prospects for their implementation are considered.