Ericu950/Stoicheia-code

Stoicheia

A character-level masked-diffusion Transformer for Ancient Greek, pretrained on an open, revision-pinned corpus and released as eleven decontaminated checkpoints (ten rotated literary folds + one documentary-clean model), fine-tuned for restoration of damaged inscriptions/papyri, morphosyntactic tagging and dependency parsing, and macronization/metrical scansion.

This repository is the training/evaluation code. The pretrained and fine-tuned model weights are on the HuggingFace Hub — see MODEL_CARDS_INDEX.md for the full list, or jump straight to Ericu950/Stoicheia-doc_clean (the flagship backbone) or Ericu950/Stoicheia-restoration-test3 (or any of the ten digit-rotation checkpoints) / -tagger-parser for a ready-to-use downstream model (or -meter for macronization and scansion). All model repos are public: weights ship as model.safetensors with a config.json, loadable directly through AutoModel.from_pretrained(..., trust_remote_code=True).

Run everything in the browser: Stoicheia_demo.ipynb restores a lacuna of unknown width, picks the checkpoint that has provably never read your document, tags and parses a verse of Homer, macronizes and scans a line, and scores the macronizer on the benchmark.

Quickstart (no training required)

import torch
from transformers import AutoModel
from huggingface_hub import hf_hub_download

REPO = "Ericu950/Stoicheia-doc_clean"
model = AutoModel.from_pretrained(REPO, trust_remote_code=True).eval()

# `trust_remote_code` loads the model classes; the processor is a separate helper, so
# fetch it into the working directory before importing it.
hf_hub_download(repo_id=REPO, filename="processing_char_bert.py", local_dir=".")
from processing_char_bert import CharBertProcessor

processor = CharBertProcessor()

# a lacuna of UNCERTAIN width, in text that's ALSO fully bare scriptio continua (no
# spaces, no accents) -- the realistic case for damaged, unaccented primary sources.
# Write "[N±M]" for a best-guess width N and a plausible range N-M..N+M; every
# candidate width is scored by the model's own confidence, recovering both the
# width and the text while jointly restoring accents/word-boundaries throughout.
text = "εναρχηηνο[5±3]καιολογοςηνπροστονθεον"
best_text, best_width, candidates = processor.restore_elastic(model, text, mask_dia_boundary=True)
print(best_text)  # -> ἐν ἀρχῇ ἦν ὁ λόγος, καὶ ὁ λόγος ἦν πρὸς τὸν θεόν.

A damaged inscription, unaccented and unspaced where the break falls:

print(processor.restore_respaced(model, "ἔδοξεν τηβου-- καὶ τῷ δήμῳ"))
# -> ἔδοξεν τῇ βουλῇ καὶ τῷ δήμῳ

Accents and word division are predictions, not requirements: a bare majuscule transcript is as readable to this model as a modern critical text, and the gap is filled in the same pass that decides where the words end.

What's here

• model/, data/, train/, eval/ — the pretraining architecture (CharBertEncoder, a five-plane character-level masked-diffusion Transformer) and training loop.
• insc/ — restoration fine-tuning (inscriptions + papyri) and strict-protocol evaluation (same-harness comparison against DeepMind's Ithaca).
• tagger/, parser/ — morphosyntactic tagging (factored XPOS, edit-script lemma, UPOS) and biaffine dependency parsing, plus a joint multi-task model and a pluggable HuggingFace-encoder bridge for cross-encoder ablations.
• meter/ — macronization (vowel length) and metrical scansion, including the Norma benchmark protocol and rule-based silver-data mining pipeline.
• tests/ — CPU-only pytest suite.
• scripts/fetch_dbbe.py — refetches the Database of Byzantine Book Epigrams, which the released corpus omits: DBBE is CC BY-NC-SA, whose non-commercial clause a CC BY-SA compilation cannot carry. Run it to reconstruct the pretraining corpus exactly (5,476 records, ~0.2M words, 0.1% of the total); what you build then inherits DBBE's terms.

See REPRODUCING.md for the full environment setup and end-to-end reproduction walkthrough.

Citation

@misc{stoicheia2026,
  title  = {Stoicheia: Character-Level Masked Diffusion for Ancient Greek Textual
            Restoration, Parsing, and Metrical Scansion},
  author = {Cullhed, Eric and Th\"orn Cleland, Albin},
  year   = {2026},
  eprint = {2608.XXXXX},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL}
}

License

Apache 2.0 (see LICENSE). External baselines (DeepMind's Ithaca and predictingthepast releases) are downloaded separately from their own repositories and retain their own licenses — see NOTICE and REPRODUCING.md.