Machine learningDeep learning / NLP / CV

Objašnjiva klasifikacija zasnovana na RoBERTa modelu

Objašnjiva klasifikacija zasnovana na RoBERTa modelu (Explainable RoBERTa-based classification) fino podešava RoBERTa transformatorski model na obeleženim tekstualnim podacima, a zatim primenjuje post-hoc metode interpretacije — kao što su SHAP, LIME ili analiza pažnje — kako bi otkrila koji su tokeni ili karakteristike doveli do svake predikcije. Ovo premošćuje najsavremenije performanse obrade prirodnog jezika (NLP) sa ljudski razumljivim obrazloženjem, zadovoljavajući zahteve za tačnošću i transparentnošću.

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Izvori

  1. 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
  2. Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774. link

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Explainable RoBERTa-based Text Classification with Post-hoc Interpretation. ScholarGate. https://scholargate.app/sr/deep-learning/explainable-roberta-based-classification

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ScholarGateExplainable RoBERTa-based Classification (Explainable RoBERTa-based Text Classification with Post-hoc Interpretation). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/explainable-roberta-based-classification · Skup podataka: https://doi.org/10.5281/zenodo.20539026