Benchmarking BERT-based Models for Sentence-level Topic Classification in Nepali Language

• URL: http://arxiv.org/abs/2602.23940v1
• Date: Fri, 27 Feb 2026 11:42:38 GMT
• Title: Benchmarking BERT-based Models for Sentence-level Topic Classification in Nepali Language
• Authors: Nischal Karki, Bipesh Subedi, Prakash Poudyal, Rupak Raj Ghimire, Bal Krishna Bal,
• Abstract summary: This study benchmarks multilingual, Indic, Hindi, and Nepali BERT variants to evaluate their effectiveness in Nepali topic classification.<n>Ten pre-trained models, including mBERT, XLM-R, MuRIL, DevBERT, HindiBERT, IndicBERT, and NepBERTa, were fine-tuned and tested.<n>Indic models, particularly MuRIL-large, achieved the highest F1-score of 90.60%, outperforming multilingual and monolingual models.
• Score: 1.6474262142781433
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Transformer-based models such as BERT have significantly advanced Natural Language Processing (NLP) across many languages. However, Nepali, a low-resource language written in Devanagari script, remains relatively underexplored. This study benchmarks multilingual, Indic, Hindi, and Nepali BERT variants to evaluate their effectiveness in Nepali topic classification. Ten pre-trained models, including mBERT, XLM-R, MuRIL, DevBERT, HindiBERT, IndicBERT, and NepBERTa, were fine-tuned and tested on the balanced Nepali dataset containing 25,006 sentences across five conceptual domains and the performance was evaluated using accuracy, weighted precision, recall, F1-score, and AUROC metrics. The results reveal that Indic models, particularly MuRIL-large, achieved the highest F1-score of 90.60%, outperforming multilingual and monolingual models. NepBERTa also performed competitively with an F1-score of 88.26%. Overall, these findings establish a robust baseline for future document-level classification and broader Nepali NLP applications.

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