BERT MODELINING QO‘LLANILISHI VA VAZIFALARI

Authors

  • Berdiyev Jahongir Botir o‘g‘li Author

Keywords:

BERT, classification, machine translation, machine learning.

Abstract

Language models are tools that assist machines in comprehending natural languages. Several language models have been created to date, each designed to perform a specific task. One such model is the BERT model, which is distinguished from others by its advantages, achievements, and capabilities. This language model is renowned for its ability to perform various operations on texts and provide highly accurate results. As an example, it is important to note that BERT is a valuable tool for various practices, including emotion detection in texts and media, text and image classification, machine translation, language and speech recognition, spam detection, question answering, and text creation. This article discusses the history of BERT’s creation, the need for its development, and its potential applications. Additionally, this text answers questions about the functionality, training, and purpose of the BERT model. It can be concluded that implementing the BERT model yields effective results in natural language processing.

References

Guo Z., Nguyen M.L. / Proceedings of the1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10 th International Joint Conferenceon Natural Language Processing: Student Research Workshop, December 4 - 7, 2020.- B.101–107

Berdiyev J.B. BERT va TensorFlowning matnlarni tasniflashdagi ahamiyati / J.B.Berdiyev. - Educational Research in Universal Sciences (ERUS) journal, 2024. – B.176.

David Samuel, Andrey Kutuzov, Lilja Øvrelid, and Erik Velldal.2023. Trained on 100 million words and still in shape: BERT meets British National Corpus. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1954–1974, Dubrovnik,

Croatia. Association for Computational Linguistics.

Patzak T. Automatische Klassifikation von Alltagsszenarien mit einem BERT-basierten Siamese-Netzwerk / T.Patzak. - Humboldt-Universität zu Berlin, Institut für Informatik, 2021.

https://dzone.com/articles/bert-transformers-how-do-they-work

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Published

2024-06-24

Issue

Section

SECTION 2. Computer technologies in language education.

How to Cite

BERT MODELINING QO‘LLANILISHI VA VAZIFALARI. (2024). «CONTEMPORARY TECHNOLOGIES OF COMPUTATIONAL LINGUISTICS», 2(22.04), 203-209. https://myscience.uz/index.php/linguistics/article/view/50