Practice Implementation of Neural Network Model BART-Large-CNN for Text Annotation

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dc.contributor.authorMazurets, O.
dc.contributor.authorMolchanova, M.
dc.contributor.authorKlimenko, V.
dc.contributor.authorProsvitliuk, M
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2024-12-10T18:18:16Z
dc.date.available2024-12-10T18:18:16Z
dc.date.issued2024
dc.description.abstractThe scheme of the method of annotating works of art was described, which works by converting input data in the form of text for annotating a work of art, a trained machine learning model, and desired annotation parameters into output data in the form of an annotation and a numerical evaluation of the quality of the annotation and is intended for automated annotation creation. We also present the neural network architecture of the machine learning model, which is the input to the proposed method of annotating works of art. This neural network model belongs to the «transformers» type and is currently one of the most powerful text generation models. A practical implementation of the method of annotating works of art has been created and the main purposes of the software components of the intelligent system for annotating works of art have been described.
dc.identifier.citationMazurets O., Molchanova M., Klimenko V., Prosvitliuk M. Practice Implementation of Neural Network Model BART-Large-CNN for Text Annotation. Prospects of Scientific Research in the Conditions of the Modern World. Proceedings of XXVII International scientific and practical conference. June 12-14, 2024. Rotterdam, Netherlands. 2024. Pp. 97-102
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/17211
dc.language.isoen
dc.titlePractice Implementation of Neural Network Model BART-Large-CNN for Text Annotation
dc.typeСтаття
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