Ericu950/oga-macronizer-char

grc-macronizer-char: a small char-level model for Ancient Greek vowel-length annotation

This model predicts the phonemic length (long/short/undetermined) of the three Ancient Greek "dichrona" -- alpha (α), iota (ι), and upsilon (υ) -- whose length is not disambiguated by the standard Greek script. It was trained on Ancient Greek text automatically macronized by the rule-based grc-macronizer, applied to the Opera Graeca Adnotata corpus (Celano 2024), and is intended as a complementary, corpus-general alternative to that rule-based system.

Architecture

A small transformer encoder (~0.9M parameters) operating over three character-level "planes" per input position:

• plane 1 (letter): which of the 24 Greek letters (final/medial sigma folded together), or space, or "other" (punctuation/digits/foreign chars)
• plane 2 (diacritic): the combination of accent/breathing/diaeresis marks on that letter, if any (a small vocabulary fit from the training data)
• target (macron): none / short / long -- predicted only at positions that are genuine ambiguous dichrona (diphthong members and circumflexed vowels are excluded, matching the rule-based system's own definition)

During training, the diacritic plane is randomly masked (accents stripped) for a fraction of characters, so the model also learns to macronize unaccented input.

Training data

Training labels come directly from the rule-based macronizer's own output: wherever it marks a dichronon (it does not guess -- see the macronizer paper), that becomes a training label; dichrona it left unmarked are excluded from the loss entirely (never treated as a negative "short" or "long" example), since the rule-based system is known to be incomplete rather than wrong where it commits.

Evaluation

Evaluated on Norma Syllabarum Graecarum, a manually-annotated benchmark spanning 15 works (prose and verse; two further works are held out of this HF version specifically to avoid overlap with a separate training corpus). Scored with the identical harness (macron_model/eval_norma.py) against the bundled rule-based macronizer, counting a left-unmarked dichronon as an implicit "short" guess for both systems (default_short_accuracy below) so abstention isn't unfairly penalized either way:

system	accuracy (unmarked defaults to short)	raw accuracy	unmarked / total
rule-based grc-macronizer	89.46%	57.57%	769 / 1916
this model	90.66%	90.55%	6 / 1916

The model modestly outperforms the rule-based system on the fair metric while resolving essentially every position (99.7% coverage vs. 60%), since it learned to generalize past the cases the rule-based system couldn't commit to.

Usage

import sys
sys.path.insert(0, "path/to/grc-macronizer/macron_model")  # for predict.py
from predict import MacronPredictor

predictor = MacronPredictor("path/to/downloaded/checkpoint")
macronized = predictor.macronize("ανθρωπος ανηρ")
# -> "α^νθρωπος α_νηρ"

MacronPredictor expects the checkpoint directory (containing config.json/model.safetensors) plus a sibling or parent diacritic_vocab.json (see predict.py for the exact lookup logic). The predict.py/tokenizer.py source lives in grc-macronizer/macron_model.

Citation

If you use this model, please cite:

Thörn Cleland, Albin and Eric Cullhed (forthcoming). Automatic Annotation of Ancient Greek Vowel Length.

License

GNU GPL v3, matching grc-macronizer (the training-data generator this model's code ships alongside).