Ericu950/Stoicheia-doc_clean

Stoicheia -- documentary-clean backbone

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.

The documentary-clean backbone: zero exposure to inscriptions or papyri of any kind. Every downstream model in this release is fine-tuned from it, so no epigraphic or papyrological result can be contaminated by pretraining. It is the flagship checkpoint -- use this one unless you specifically need a literary fold.

What this checkpoint has not read

Ten literary folds rotate an 80/10/10 split built by a 13-stage pipeline that clusters records into editions of the same work -- by canonical identifier where one exists, by duplicate-aware content matching where it does not -- and then excises from each fold's training data every sentence colliding with its held-out zones (exact and reordered duplicates, word 5-grams, document-level near-duplicates). The guarantee is exact at the fold level: for any passage of the open corpus, at least one released checkpoint has provably never seen it, which is what makes it possible to interrogate a transmitted text with a model that cannot merely be recalling it.

Stoicheia-doc_clean extends the same discipline to documentary text: every inscription and papyrus is excluded, along with anything a contamination screen flags as quoting one.

Usage

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, but the processor is a separate helper:
# 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

proc = CharBertProcessor()

# a gap of uncertain width in unaccented scriptio continua: "[N±M]" scores every width
# in N-M..N+M by the model's own confidence, and restores accents and word division too
text = "εναρχηηνο[5±3]καιολογοςηνπροστονθεον"
best, width, candidates = proc.restore_elastic(model, text, mask_dia_boundary=True)
print(best)     # ἐν ἀρχῇ ἦν ὁ λόγος, καὶ ὁ λόγος ἦν πρὸς τὸν θεόν.
print(width)    # 5 -- the width the model judged most likely