KOMPARATIVNA ANALIZA TEKSTUALNIH MODELA BERT, BART I XLNET ZA PREDIKCIJU POPULARNOSTI YOUTUBE SNIMKA
Ključne reči:
BERT, BART, XLNet, Fine-tuning, Youtube
Apstrakt
Ovaj rad se bavi komparativnom analizom tri tekstualna modela BERT, BART i XLNet. Podaci za treniranje i validaciju su preuzeti sa sajta kaggle.com. Postoje dva skupa podataka. Prvi inicijalni i drugi prošireni, koji je nastao zbog pokušaja poboljšanja recall metrike. Nakon preuzimanja urađeno je pretprocesiranje odnosno tokenizacija i stemovanje. Skup je podeljen u razmeri 80:20 za treniranje i validaciju. Isprobane su različite vrednosti za learning rate i batch size, a najbolji rezultat je dobijen korišćenjem BERT large modela kada je learning rate 1e-5, a batch size 8. Druga dva modela dala su nešto lošiji rezultat od BERT-a. Svi eksperimenti i rezultati biće predstavljeni u nastavku.
Reference
[1] Kumar, Ashok; Trueman, Tina Esther; Cambria, Erik. Fake news detection using XLNet fine-tuning model. In: 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA). IEEE, 2021. p. 1-4.
[2] W. Y. Wang, „Liar, liar, pants on fire“ : A new benchmark dataset for fake news detection, in Proceedings of 55th Annual Meeting of Association for Computational Linguistics 2017, pp 2931-2937
[3] Spristav, Gaurav; Kant, Shri; Spristava, Durgesh. Design of an AI-Driven Feedback and Decision Analysis in Online Learning with Google BERT. International Journal of Intelligent Systems and Applications in Engineering, 2024, 12.10s: 629–643-629–643
[4] Chae, Youngjin; Davidson, Thomas. Large language models for text classification: From zero-shot learning to fine-tuning. Open Science Foundation, 2023.
[2] W. Y. Wang, „Liar, liar, pants on fire“ : A new benchmark dataset for fake news detection, in Proceedings of 55th Annual Meeting of Association for Computational Linguistics 2017, pp 2931-2937
[3] Spristav, Gaurav; Kant, Shri; Spristava, Durgesh. Design of an AI-Driven Feedback and Decision Analysis in Online Learning with Google BERT. International Journal of Intelligent Systems and Applications in Engineering, 2024, 12.10s: 629–643-629–643
[4] Chae, Youngjin; Davidson, Thomas. Large language models for text classification: From zero-shot learning to fine-tuning. Open Science Foundation, 2023.
Objavljeno
2025-03-03
Sekcija
Elektrotehničko i računarsko inženjerstvo