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	Wyjaśnialny Transformer	Samo-nadzorowany Transformer
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2017–2021	2017–2019
Twórca	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community	Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)
Typ	Interpretable deep learning model	Self-supervised deep learning model
Źródło pierwotne	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, 4171–4186.
Inne nazwy	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model	SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer
Pokrewne	4	5
Podsumowanie	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.	A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.