README.md

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title: README
emoji: 🏢
colorFrom: red
colorTo: blue
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---

# LatinCy

Synthetic trained spaCy pipelines for Latin NLP

Developed by [Patrick J. Burns](https://diyclassics.github.io/), 2023.

## Paper

Details about training, datasets, etc. can be found in the following paper:
Burns, P.J. 2023. “LatinCy: Synthetic Trained Pipelines for Latin NLP.” https://arxiv.org/abs/2305.04365v1.

### Citation
```
@misc{burns_latincy_2023,
	title = {{LatinCy}: Synthetic Trained Pipelines for Latin {NLP}},
	author = {Burns, Patrick J.},
	url = {https://arxiv.org/abs/2305.04365v1},
	shorttitle = {{LatinCy}},
	abstract = {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.},
	date = {2023-05-07},
	langid = {english},
}
```