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Machine learningDeep learning / NLP / CV

Uainishaji wa msingi wa RoBERTa wa lugha nyingi

Uainishaji wa msingi wa RoBERTa wa lugha nyingi hutumia XLM-RoBERTa — kibadilishaji kilichofunzwa awali kwa lugha zaidi ya 100 kupitia modeli ya lugha iliyofichwa — na kuiboresha kwa maandishi yenye lebo ili kupeana kategoria katika lugha nyingi. Kwa kushiriki modeli moja katika lugha zote, huwezesha uainishaji thabiti wa maandishi unaovuka lugha na bila mifano ya awali katika lugha mbalimbali bila kuhitaji viainishi tofauti kwa kila lugha.

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Vyanzo

  1. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 8440–8451. DOI: 10.18653/v1/2020.acl-main.747
  2. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Multilingual RoBERTa-based Text Classification (XLM-RoBERTa). ScholarGate. https://scholargate.app/sw/deep-learning/multilingual-roberta-based-classification

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Imerejelewa na

ScholarGateMultilingual RoBERTa-based Classification (Multilingual RoBERTa-based Text Classification (XLM-RoBERTa)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/multilingual-roberta-based-classification · Seti ya data: https://doi.org/10.5281/zenodo.20539026