---
license: apache-2.0
language:
- grc
library_name: transformers
tags:
- ancient-greek
- classical-philology
- character-level
- masked-diffusion
- macronization
- metrical-scansion
pipeline_tag: token-classification
---

# Stoicheia -- macronization and metrical scansion (joint)

**Stoicheia** is a 405M-parameter character-level masked-diffusion encoder for Ancient Greek
(`d_model` 1024, depth 32, banded attention: three of every four blocks attend within a
256-character window, the fourth globally). Its input is factored into five aligned planes --
letters, word/sentence boundaries, diacritics, capitalization, punctuation -- each of which can
be masked independently to an explicit *unknown* state at inference. That is what lets one model
read an edited text, *scriptio continua*, and a lacuna of unknown length without changing
anything but its input.

Anonymous release accompanying a paper under review.

Two per-letter heads on `Stoicheia-doc_clean`, trained jointly: **macronization** (long versus
short at ambiguous bare α/ι/υ -- Greek orthography never marks vowel length) and **scansion**
(none / heavy-end / light-end / verse-end). An optional Viterbi decoder constrains the scansion
output to valid paths through a set of metre automata.

Use this checkpoint when you want both tasks from one model; use `Stoicheia-macronizer` when you
want vowel length alone, where a dedicated head does better.

## Usage

```python
import torch
from transformers import AutoModel
from huggingface_hub import hf_hub_download

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

hf_hub_download(repo_id=REPO, filename="processing_char_bert_meter.py", local_dir=".")
from processing_char_bert_meter import CharBertMeterProcessor

proc = CharBertMeterProcessor()
batch = proc("ἄνδρα μοι ἔννεπε, μοῦσα, πολύτροπον, ὃς μάλα πολλὰ")
with torch.no_grad():
    out = model(**{k: v for k, v in batch.items() if not k.startswith("_")})
print(proc.decode_macronization(out, batch))   # _ long, ^ short
print(proc.decode_scansion(out, batch))        # [heavy] {light}
```