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.

本文介绍了 LatinCy，一组通用的、训练有素的拉丁语 “核心” 工作管道，适用于自然语言处理框架 spaCy。该模型是基于大量的可用拉丁语数据训练的，包括五个拉丁通用依赖树库，可与彼此兼容的预处理。结果是，对于许多自然语言处理任务，我们得到了一组具有良好性能的拉丁语通用模型。本文描述了模型训练及其训练数据和参数化，并介绍了拥有 spaCy 模型对于拉丁语研究人员的优势。