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Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers.

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Title: Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers.
Authors: Sbei, Arafet1 (AUTHOR) sbeiiarafett@gmail.com, ElBedoui, Khaoula2,3 (AUTHOR), Barhoumi, Walid2,3 (AUTHOR)
Source: Vietnam Journal of Computer Science (World Scientific). Aug2025, Vol. 12 Issue 3, p301-328. 28p.
Database: Environment Complete
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  Data: Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers.
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  Data: <searchLink fieldCode="JN" term="%22Vietnam+Journal+of+Computer+Science+%28World+Scientific%29%22">Vietnam Journal of Computer Science (World Scientific)</searchLink>. Aug2025, Vol. 12 Issue 3, p301-328. 28p.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1142/S2196888824500209
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      – Code: eng
        Text: English
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        PageCount: 28
        StartPage: 301
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      – TitleFull: Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers.
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            NameFull: Sbei, Arafet
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            NameFull: ElBedoui, Khaoula
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              Text: Aug2025
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              Y: 2025
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              Value: 12
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            – TitleFull: Vietnam Journal of Computer Science (World Scientific)
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