Ericu950/Stoicheia-meter

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

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}