Floret word vectors for Latin (medium, 50k hash buckets, 300 dimensions).
Part of the LatinCy project — pretrained NLP pipelines for Latin.
Overview
Feature
Value
Type
Floret (hash-based subword embeddings)
Dimensions
300
Hash buckets
50,000
Algorithm
CBOW
Language
Latin (la)
spaCy version
>=3.8.11,<3.9.0
License
MIT
Floret vectors use hash-based subword embeddings, meaning every word gets a vector — there are no out-of-vocabulary words. This is especially important for morphologically rich languages like Latin.
1import spacy
23nlp = spacy.load("la_vectors_floret_md")45# Get word vectors6doc = nlp("rex populum regit")7for token in doc:8print(token.text, token.vector[:5])910# Compute similarity11doc1 = nlp("bellum")12doc2 = nlp("pugna")13print(doc1.similarity(doc2))
These vectors are primarily intended as a component in LatinCy pipelines (la_core_web_md, la_core_web_lg), but can also be used standalone.
Evaluation
All methods were trained on the same v3.10 corpus and scored on the same benchmark (v0.2: 1,545 analogy items, 1,285 odd-one-out items).
Note: v0.2 is a development benchmark, not a finalized release benchmark — these numbers are for cross-method comparison and may change.
Model
Analogy Rank 1
Analogy Rank 5
Odd-One-Out
FastText CBOW-300-10
76.2%
91.5%
70.3%
Floret (lg)
74.3%
89.7%
68.5%
Floret (md)
73.4%
88.2%
65.7%
Word2Vec CBOW-300-10
42.3%
71.0%
77.3%
GloVe 300
16.3%
34.1%
66.4%
The md vectors (50k buckets) trade a small amount of accuracy for ~4x smaller size versus lg (200k buckets). Floret is competitive with FastText on analogies while being much smaller and supporting arbitrary vocabulary, which is why we have chosen to use the Floret vectors for pipeline training.
Training
Corpus
Trained on 14.3M sentences (278M tokens) from 15 sources:
Source
Description
CC100-Latin
Web-crawled Latin text (deduplicated and filtered)
Latin Wikisource
General Latin texts
Latin Wikipedia
Latin Wikipedia articles
The Latin Library
General Latin texts
Patrologia Latina
Patristic texts
CAMENA Neo-Latin
Early modern Latin texts
CLTK-Tesserae Latin
Classical texts
Perseus Digital Library
Classical texts
CSEL
Corpus Scriptorum Ecclesiasticorum Latinorum (patristic)
digilibLT
Late-antique Latin texts
UD + LASLA treebanks
Annotated treebank sentences
Formulae
Early medieval legal formulae
EDH
Epigraphic Database Heidelberg (inscriptions)
Epistolae
Medieval Latin letters
PTA
Patristic Text Archive
Parameters
Parameter
Value
Algorithm
CBOW
Dimensions
300
Subword n-gram range
3–6
Hash buckets
50,000
Epochs
15
Negative sampling
25
Min count
50
Learning rate
0.05
Training followed Sprugnoli et al. 2019 for epoch count and negative sampling parameters.
Citation
If you use these vectors, please cite this preprint:
bibtex
1@misc{burns2023latincy,
2 title = "{LatinCy}: Synthetic Trained Pipelines for {L}atin {NLP}",
3 author = "Burns, Patrick J.",
4 year = "2023",
5 eprint = "2305.04365",
6 archivePrefix = "arXiv",
7 primaryClass = "cs.CL",
8 url = "https://arxiv.org/abs/2305.04365"
9}
Sprugnoli, R., Passarotti, M., and Moretti, G. 2019. “Vir Is to Moderatus as Mulier Is to Intemperans Lemma Embeddings for Latin.” In Proceedings of the Sixth Italian Conference on Computational Linguistics. Bari, Italy. 1–7. http://ceur-ws.org/Vol-2481/paper69.pdf.
