This paper presents machine translation from historical Japanese to contemporary Japanese using a Text-to-Text Transfer Transformer (T5). The result of the previous study that used neural machine translation (NMT), Long Short Term Memory (LSTM), could not outperform that of the work that used statistical machine translation (SMT). Because an NMT model tends to require more training data than an SMT model, the lack of parallel data of historical and contemporary Japanese could be the reason. Therefore, we used Japanese T5, a kind of large language model to compensate for the lack of data. Our experiments show that the translation with T5 is slightly lower than SMT. In addition, we added the title of the literature book from which the example sentence was extracted at the beginning of the input. Japanese historical corpus consists of a variety of texts ranging in periods when the texts were written and the writing styles. Therefore, we expected that the title gives information about the period and style, to the translation model. Additional experiments revealed that, with title information, the translation from historical Japanese to contemporary Japanese with T5 surpassed that with SMT.