[2602.23940] Benchmarking BERT-based Models for Sentence-level Topic Classification in Nepali Language

Computer Science > Computation and Language arXiv:2602.23940 (cs) [Submitted on 27 Feb 2026] 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 View a PDF of the paper titled Benchmarking BERT-based Models for Sentence-level Topic Classification in Nepali Language, by Nischal Karki and 4 other authors View PDF HTML (experimental) 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 Nep...