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Machine learningDeep learning / NLP / CV

Word2Vec Iliyoboreshwa

Fine-Tuned Word2Vec hubadilisha kielelezo kilichofunzwa awali cha Word2Vec kwa kikoa au kazi maalum kwa kuendeleza mafunzo yake kwenye maandishi maalum ya kikoa. Badala ya kufunza vibainishi kuanzia mwanzo, watendaji huweka vekta za matumizi ya jumla (k.m., vekta za Google News) na kuendesha vipindi vya ziada vya Skip-gram au CBOW kwenye makusanyo ya kikoa, wakisogeza uwakilishi wa maneno kuelekea ruwaza za matumizi maalum ya kikoa.

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Vyanzo

  1. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR 2013 Workshop. link
  2. Goldberg, Y., & Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Fine-Tuned Word2Vec (Domain-Adapted Word Embeddings via Continued Training). ScholarGate. https://scholargate.app/sw/deep-learning/fine-tuned-word2vec

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Imerejelewa na

ScholarGateFine-Tuned Word2Vec (Fine-Tuned Word2Vec (Domain-Adapted Word Embeddings via Continued Training)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/fine-tuned-word2vec · Seti ya data: https://doi.org/10.5281/zenodo.20539026