This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work.

翻译：本文介绍了LatinCy，这是一组面向拉丁语的通用“核心”处理管线，可与spaCy自然语言处理框架配合使用。这些模型基于大量现有拉丁语数据进行训练，包括所有五个拉丁语通用依存树库（Universal Dependency treebanks），这些树库经过预处理以确保彼此兼容。最终形成一组性能良好的拉丁语通用模型，在多项自然语言处理任务中表现优异（例如，性能最佳的模型在词性标注上准确率达97.41%，词形还原准确率达94.66%，形态标注准确率达92.76%）。本文描述了模型训练过程，包括训练数据和参数化设置，并阐述了为拉丁语研究者提供spaCy模型进行自然语言处理工作的优势。