latincy-vocab 0.1.0

Latin vocabulary list builder powered by LatinCy spaCy ecosystem

Latin vocabulary list builder powered by LatinCy models/tools/datasets.

latincy-vocab takes Latin text, runs it through a LatinCy spaCy model, and returns structured vocabulary lists. Citation forms (principal parts, gender, etc.), POS markers, and dictionary glosses are sourced from latincy-lexicon (Whitaker's Words); latincy-vocab is the formatting and aggregation layer over it. Output can be sorted by frequency, reading order, or alphabetically, and exported as JSON or Markdown.

Beta release (v0.1.0). The API is functional but may change.

Installation

pip install latincy-vocab

You also need a LatinCy spaCy model. latincy-vocab defaults to la_core_web_lg (best accuracy for citation forms and lemmatization):

pip install "https://huggingface.co/latincy/la_core_web_lg/resolve/main/la_core_web_lg-3.9.4-py3-none-any.whl"

A lighter la_core_web_sm is also available (swap lg→sm in the URL); set PipelineConfig(spacy_model="la_core_web_sm") to use it.

Glosses and citation forms come from latincy-lexicon (Whitaker's Words), which is installed automatically as a dependency — no extra data files, environment variables, or sibling directories required.

Usage

from vocabbuilder import VocabPipeline

pipeline = VocabPipeline()

text = "agricolae in villa laborant et aquam portant"
vocab = pipeline.process(text)

# Reading order (first occurrence)
for entry in vocab.by_first_occurrence():
    print(entry.formatted())

Example output:

agricola, agricolae, m., farmer, cultivator, gardener, agriculturist
in, prep., in, on, at (space)
villa, villae, f., farm/country home/estate
laboro, laborare, laboravi, laboratum, v., work, labor
et, conj., and, and even
aqua, aquae, f., water
porto, portare, portavi, portatum, v., carry, bring

Sorting

vocab.by_frequency()       # most common first
vocab.by_alpha()           # alphabetical by lemma
vocab.by_first_occurrence() # reading order

Filtering

vocab.filter_pos({"NOUN", "VERB"})   # nouns and verbs only
vocab.filter_min_frequency(2)         # words appearing ≥ 2 times

Export

# Markdown glossary
print(vocab.to_markdown())

# JSON (all fields)
print(vocab.to_json())

Entry fields

Each VocabEntry exposes:

Field	Description
headword	Citation form (principal parts / nom+gen+gender) or display lemma
pos_marker	Abbreviated POS tag (v., adj., adv., etc.) — empty for nouns (gender in citation)
short_gloss	Trimmed gloss (up to 3 senses)
full_gloss	All senses joined
frequency	Count across the input text
forms_seen	Set of inflected forms observed

Configuration

from vocabbuilder import VocabPipeline, PipelineConfig

config = PipelineConfig(
    spacy_model="la_core_web_lg",  # default; lighter: "la_core_web_sm"
    min_frequency=2,               # drop words seen only once
)
pipeline = VocabPipeline(config)

Related packages

• latincy-lexicon — Whitaker's Words lexical data for LatinCy
• latincy-preprocess — Latin text preprocessing utilities

License

MIT