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Explainable Transformer

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.

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Explainable Transformer (Interpretability-Augmented Transformer Model)

Rekod kaedah taksonomik · ml-model / deep-learning

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