MaximEremeev/DualEmb-slav

DualEmbLM

A masked language model trained from scratch on Old East Slavic and Old Church Slavonic texts, with dual character-level + word-level embeddings.

Architecture

DualEmbLM combines:

• Character-level tokenisation (1 character = 1 token) — enables precise lacuna restoration at the character level
• Word-level context embeddings — provides morphological and lexical context via a 50k word vocabulary
• Transformer encoder (BERT architecture, trained from scratch) — 6 layers, hidden size 512, 8 attention heads

The dual embeddings are concatenated and projected into the shared hidden space before being passed to the transformer encoder.

Training

The model was trained on a corpus of Old Russian and Church Slavonic texts assembled from the following sources:

Source	Language	Word Tokens	Link
Birchbark manuscripts	Old Novgorodian (mostly)	21,464	gramoty.ru
Epigraphy	Old Church Slavonic (mostly)	8,102	epigraphica.ru
DIACU	Old Church Slavonic; Church Slavonic (Old Russian, Middle Bulgarian, Serbian, Resava recensions); Middle Russian	1,683,307	ACL Anthology
TOROT	Old Russian; Church Slavonic	682,430	torottreebank.github.io
Bible (Ponomar)	Church Slavonic	603,047	GitHub
Byliny	Old Russian (XI–XVII c.)	430,103	rusneb.ru
Pushkin House	Old Russian	256,503	lib2.pushkinskijdom.ru
Military Statute (Part 2)	Old Russian	49,787	rusneb.ru
NKRYA (historical)	Old Russian; Old Rus (XI–XVIII c.)	42,412	ruscorpora.ru

Masking details: MLM probability 8%, span masking, edge masking, random gap augmentation.

Usage

from transformers import AutoModelForMaskedLM

model = AutoModelForMaskedLM.from_pretrained(
    "MaximEremeev/DualEmb-slav",
    trust_remote_code=True,
)

Tasks

• Generated lacunae restoration (Test A Hit@1: 0.817, CER: 0.183)
• Real lacunae restoration (Test B char Hit@1: 0.466, span Hit@1: 0.222)

Contact

Maxim Eremeev, maeremeev@edu.hse.ru