Large language models have gained tremendous popularity in domains such as e-commerce, finance, healthcare, and education. Fine-tuning is a common approach to customize an LLM on a domain-specific dataset for a desired downstream task. In this paper, we present a valuable resource for fine-tuning LLMs developed for the Spanish language to perform a variety of tasks such as classification, masked language modeling, clustering, and others. Our resource is a collection of handwritten notary records from the seventeenth century obtained from the National Archives of Argentina. This collection contains a combination of original images and transcribed text (and metadata) of 160+ pages that were handwritten by two notaries, namely, Estenban Agreda de Vergara and Nicolas de Valdivia y Brisuela nearly 400 years ago. Through empirical evaluation, we demonstrate that our collection can be used to fine-tune Spanish LLMs for tasks such as classification and masked language modeling, and can outperform pre-trained Spanish models and ChatGPT-3.5/ChatGPT-4o. Our resource will be an invaluable resource for historical text analysis and is publicly available on GitHub.

展示了如何利用17世纪阿根廷国家档案馆的手写公证记录来优化西班牙语言模型，以进行分类、遮蔽语言建模等任务，证明这个资源在历史文本分析领域非常有价值。

用于优化西班牙大型语言模型的17世纪西班牙美洲公证记录