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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM19468

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM19468

Volume:

7 |

4 |

Apr 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

P

Prof.S.S.Vyavahare

OK

Omkar Kondhalkar

MC

Manish Chaudhari

MB

Mugdha Borse

RM

Rohit Malviya

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Abstract

In view of this review, we depict the application of individual of ultimate standard deep learning-located languagemodels – BERT. The paper characterizes the machine of operation concerning this model, the main regions of its request to the tasks of manual science of logical analysis, comparisons accompanying akin models in each task, in addition to a description of some proprietary models. In fitting this review, the dossier of various dozen original experimental items written over the past few years, that engaged ultimate attention in the experimental society, were systematized. This survey will be beneficial to all students and investigators the one want to be familiar with accompanying new advances in the field of naturallanguage manual interpretation.

KEYWORDS

Natural language processing review BERT language models machine learning deep learning transfer learning NPL applications

Cite This Article

Prof.S.S.Vyavahare, Omkar Kondhalkar, Manish Chaudhari, Mugdha Borse, Rohit Malviya, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 4, Apr 2023.

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Publication Date

4/28/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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