مقایسهٔ روش‌ها

	ترنسفورمر قابل توضیح (Explainable Transformer)	طبقه‌بندی مبتنی بر برتِ قابل توضیح
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2017–2021	2019–2020
پدیدآور	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
نوع	Interpretable deep learning model	Pre-trained transformer classifier with post-hoc or intrinsic explainability
منبع بنیادین	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.	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, pp. 4171–4186.
نام‌های دیگر	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
مرتبط	4	6
خلاصه	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.