ScholarGate
Msaidizi
Machine learningDeep learning / NLP / CV

Transformer Iliyoboreshwa

Kuboresha Transformer huboresha modeli kubwa iliyofunzwa awali — kama vile BERT, GPT, au ViT — kwa kazi maalum inayofuata kwa kuendeleza mafunzo yanayotegemea mteremko kwenye seti ya data lengwa yenye lebo. Mfumo huu wa hatua mbili (funza awali kisha ubore) huendelea kufikia matokeo ya hali ya juu katika kazi za NLP na maono ya kompyuta kwa data maalum ya kazi kidogo sana kuliko kufunza kutoka mwanzo.

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Vyanzo

  1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link
  2. 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. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models). ScholarGate. https://scholargate.app/sw/deep-learning/fine-tuned-transformer

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateFine-Tuned Transformer (Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/fine-tuned-transformer · Seti ya data: https://doi.org/10.5281/zenodo.20539026