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The monolingual Hindi BERT models currently available on the model hub do not perform better than the multi-lingual models on downstream tasks.
Akhtar, M.S., Kumar, A., Ekbal, A., Bhattacharyya, P.: A hybrid deep learning architecture for sentiment analysis. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. pp. 482–493 (2016)
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Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., Ji, H.: Cross-lingual name tagging and linking for 282 languages. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1946–1958 (2017)
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Joshi, R., Goel, P., Joshi, R.: Deep learning for hindi text classification: A comparison. In: International Conference on Intelligent Human Computer Interaction. pp. 94–101. Springer (2019)
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Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, É., Ott, M., Zettlemoyer, L., Stoyanov, V.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 8440–8451 (2020)
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Kakwani, D., Kunchukuttan, A., Golla, S., Gokul, N., Bhattacharyya, A., Khapra, M.M., Kumar, P.: Indicnlpsuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for indian languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 4948–4961 (2020)
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Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: A survey. Science China Technological Sciences 63
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2021
Kowsher, M., Sami, A.A., Prottasha, N.J., Arefin, M.S., Dhar, P.K., Koshiba, T.: Bangla-bert: transformer-based efficient model for transfer learning and language understanding. IEEE Access 10
2022
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Kulkarni, A., Mandhane, M., Likhitkar, M., Kshirsagar, G., Jagdale, J., Joshi, R.: Experimental evaluation of deep learning models for marathi text classification. In: Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. pp. 605–613. Springer (2022)
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Litake, O., Sabane, M.R., Patil, P.S., Ranade, A.A., Joshi, R.: L3cube-mahaner: A marathi named entity recognition dataset and bert models. In: Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. pp. 29–34 (2022)
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Bhattacharjee, A., Hasan, T., Uddin, W.A., Mubasshir, K., Islam, M.S., Iqbal, A., Rahman, M.S., Shahriyar, R.: Banglabert: Lagnuage model pretraining and benchmarks for low-resource language understanding evaluation in bangla. Findings of the North American Chapter of the Association for Computational Linguistics: NAACL (2022)
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Joshi, R.: L3Cube-MahaCorpus and MahaBERT: Marathi monolingual corpus, Marathi BERT language models, and resources. In: Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. pp. 97–101. European Language Resources Association, Marseille, France (Jun 2022), https://aclanthology.org/2022.wildre-1.17
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Marreddy, M., Oota, S.R., Vakada, L.S., Chinni, V.C., Mamidi, R.: Am i a resource-poor language? data sets, embeddings, models and analysis for four different nlp tasks in telugu language. Transactions on Asian and Low-Resource Language Information Processing (2022)
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Nayak, R., Joshi, R.: L3Cube-HingCorpus and HingBERT: A code mixed Hindi-English dataset and BERT language models. In: Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. pp. 7–12. European Language Resources Association, Marseille, France (Jun 2022), https://aclanthology.org/2022.wildre-1.2
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