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Machine learningDeep learning / NLP / CV

Maswali yanayojibiwa kwa usahihi

Maswali yanayojibiwa kwa usahihi hubadilisha mfumo mkuu wa lugha uliotangulizwa awali — kama vile BERT, RoBERTa, au mfumo wa familia ya GPT — kujibu maswali ya lugha asilia juu ya kifungu cha habari au hifadhidata iliyotolewa. Mfumo hujifunza kutafuta sehemu za majibu au kutoa majibu ya mtindo huru kwa kuendelea kufunzwa kwa jozi za QA zilizo na lebo baada ya mafunzo ya awali ya madhumuni ya jumla.

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Vyanzo

  1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI: 10.18653/v1/N19-1423
  2. Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of EMNLP 2016, 2383–2392. DOI: 10.18653/v1/D16-1264

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Fine-Tuned Pre-trained Language Model for Question Answering. ScholarGate. https://scholargate.app/sw/deep-learning/fine-tuned-question-answering

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Imerejelewa na

ScholarGateFine-Tuned Question Answering (Fine-Tuned Pre-trained Language Model for Question Answering). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/fine-tuned-question-answering · Seti ya data: https://doi.org/10.5281/zenodo.20539026