latincy/la_vectors_floret_md

lavectorsfloret_md

Floret word vectors for Latin (medium, 50k hash buckets, 300 dimensions).

Part of the LatinCy project — pretrained NLP pipelines for Latin.

Overview

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.

Installation

pip install https://huggingface.co/latincy/la_vectors_floret_md/resolve/main/la_vectors_floret_md-3.10.0-py3-none-any.whl

Usage

import spacy

nlp = spacy.load("la_vectors_floret_md")

# Get word vectors
doc = nlp("rex populum regit")
for token in doc:
    print(token.text, token.vector[:5])

# Compute similarity
doc1 = nlp("bellum")
doc2 = nlp("pugna")
print(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.

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:

Parameters

Training followed Sprugnoli et al. 2019 for epoch count and negative sampling parameters.

Citation

If you use these vectors, please cite this preprint:

@misc{burns2023latincy,
    title = "{LatinCy}: Synthetic Trained Pipelines for {L}atin {NLP}",
    author = "Burns, Patrick J.",
    year = "2023",
    eprint = "2305.04365",
    archivePrefix = "arXiv",
    primaryClass = "cs.CL",
    url = "https://arxiv.org/abs/2305.04365"
}

See also

• —la_vectors_floret_lg — Large vectors (200k buckets)
• —LatinCy pipelines — Latin NLP pipelines for spaCy using these vectors

References

• —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.