# LatinCy: Synthetic Trained Pipelines for Latin NLP

arXiv: https://arxiv.org/abs/2305.04365 · HTML: https://mlsanity.com/paper/2305.04365/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Latin BERT: A Contextual Language Model for Classical Philology](https://mlsanity.com/paper/2009.10053/) (2020)

## Built on by (most cited first, 5 of 5)

- [Graecia capta ferum victorem cepit. Detecting Latin Allusions to Ancient Greek Literature](https://mlsanity.com/paper/2308.12008/) (2023)
- [Latin Treebanks in Review: An Evaluation of Morphological Tagging Across Time](https://mlsanity.com/paper/2408.06675/) (2024)
- [Contextual morphologically-guided tokenization for Latin encoder models](https://mlsanity.com/paper/2511.09709/) (2025)
- [LiMe: a Latin Corpus of Late Medieval Criminal Sentences](https://mlsanity.com/paper/2404.12829/) (2024)
- [Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark](https://mlsanity.com/paper/2510.19585/) (2025)

## Abstract (arXiv)

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.