In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.

翻译：在大语言模型快速发展的背景下，我们精心训练并推出了GujiBERT和GujiGPT语言模型，这些是专为古籍智能信息处理设计的基础模型。这些模型基于涵盖简体与繁体汉字的大规模数据集进行训练，能够有效处理古籍相关的各类自然语言处理任务，包括但不限于自动句读、标点、分词、词性标注、实体识别及自动翻译。值得注意的是，这些模型在使用公开数据集的多个验证任务中展现出卓越性能。我们的研究成果凸显了采用自监督方法在古典文本语料上进一步训练模型的有效性，从而增强其处理下游任务的能力。此外，需要强调的是，字体选择、语料规模及初始模型选择均对最终实验结果产生显著影响。为满足数字人文与语言学研究者多样化的文本处理偏好，我们开发了包含三类共九种模型变体。我们相信，通过共享这些古籍领域专用的基础语言模型，能够推动古代文学作品的智能处理与学术探索，进而为新时代中国优秀传统文化在全球范围内的传播作出贡献。