Bronze inscriptions from early China are often fragmentary, with missing or undeciphered characters and uncertain chronological assignments.To address this, we propose BIRD (Bronze Inscription Restoration and Dating), a dataset and framework that leverages pretrained language models (PLMs) tailored to the unique demands of ancient texts.By integrating domain-adaptive pretraining (DAPT) and task-adaptive pretraining (TAPT) techniques, along with a glyph net resource that links graphemes and allographs, our approach overcomes key challenges in low-resource settings and the prevalence of allography.Our results show marked improvements in both restoration and dating accuracy.