The tokenizer has been expanded with 122 Ancient Greek characters (Polytonic) that were missing from the original M2M100 vocabulary and are essential for handling the source text correctly.

This model was trained by Spyridon Mavromatis at the Institute for Language and Speech Processing (ILSP), "Athena" RC, and the National and Kapodistrian University of Athens (NKUA) as part of an M.Sc. thesis.

Model Details

Base Model: facebook/m2m100_1.2B
Method: Full Fine-Tuning
Vocabulary: Expanded with 122 Polytonic Greek characters.
Training Data: ~130k sentence pairs from the AG-MG Corpus.

Usage

Since this is a full model, you load it directly with AutoModelForSeq2SeqLM.

python

import torch

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "ilsp/m2m100-1.2B-ag-mg-full-ft"

# 1. Load Tokenizer & Model

tokenizer = AutoTokenizer.from_pretrained(model_id, src_lang="el")

model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map="auto")

# 2. Inference

text = "Ὦ ξεῖν', ἀγγέλλειν Λακεδαιμονίοις ὅτι τῇδε κείμεθα."

inputs = tokenizer(text, return_tensors="pt").to(model.device)

# Force target language to Modern Greek ('el')

forced_bos_token_id = tokenizer.get_lang_id("el")

translated_tokens = model.generate(

    **inputs, 

    forced_bos_token_id=forced_bos_token_id, 

    max_length=128

)

print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0])

Performance

Main Test Set Results

Evaluated on the 2,000 sentence-pairs Test Set (Attic & Koine Hellenistic dialects).

Model	Method	BLEU ↑	chrF++ ↑	TER ↓	BERTScore F1 ↑	COMET ↑	ΔBLEU
NLLB-600M	Base	1.55	16.86	106.80	0.880	0.539	-
	LoRA	7.43	29.31	88.32	0.903	0.667	+5.88
NLLB-1.3B	Base	2.15	17.78	106.41	0.885	0.573	-
	LoRA	8.01	30.02	87.74	0.905	0.687	+5.86
M2M100-1.2B	Base	0.62	10.70	100.50	0.858	0.475	-
	QLoRA	10.96	33.09	82.99	0.911	0.710	+10.34
👉	Full FT	9.60	31.16	83.43	0.908	0.692	+8.98
Krikri-8B-Instruct	Base	8.29	29.87	88.13	0.895	0.695	-
	QLoRA	11.90	34.07	84.16	0.906	0.713	+3.60
	Full FT	13.16	34.71	83.68	0.848	0.702	+4.45

Stress Set Results (Rare Dialects)

Evaluated on the 250 sentence-pairs Stress Set (Ionic, Doric, Homeric dialects).

Model	Method	BLEU ↑	chrF++ ↑	TER ↓	BERTScore F1 ↑	COMET ↑	ΔBLEU
NLLB-600M	Base	0.77	14.40	118.13	0.866	0.484	-
	LoRA	5.65	28.74	88.01	0.900	0.638	+4.89
NLLB-1.3B	Base	1.25	16.15	107.03	0.873	0.525	-
	LoRA	5.68	28.94	88.24	0.900	0.656	+4.43
M2M100-1.2B	Base	0.07	9.37	100.34	0.840	0.427	-
	QLoRA	9.52	33.30	81.95	0.911	0.691	+9.45
👉	Full FT	8.16	31.12	83.11	0.907	0.664	+8.09
Krikri-8B-Instruct	Base	6.55	28.98	87.38	0.900	0.675	-
	QLoRA	10.37	34.09	82.28	0.911	0.717	+3.82
	Full FT	12.80	35.90	81.40	0.884	0.716	+6.11

Citation

If you use this model, please cite our LREC 2026 paper:

Mavromatis, S., Sofianopoulos, S., Prokopidis, P., & Giagkou, M. (2026). Ancient Greek to Modern Greek Machine Translation: A Novel Benchmark and Fine-Tuning Experiments on LLMs and NMT Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026) (pp. 8685–8698). European Language Resources Association (ELRA). https://doi.org/10.63317/4cdk64dgm2w9

bibtex

@inproceedings{mavromatis-etal-2026-ancient,
  title     = {Ancient Greek to Modern Greek Machine Translation: A Novel Benchmark and Fine-Tuning Experiments on LLMs and NMT Models},
  author    = {Mavromatis, Spyridon and Sofianopoulos, Sokratis and Prokopidis, Prokopis and Giagkou, Maria},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  month     = {May},
  year      = {2026},
  pages     = {8685--8698},
  address   = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor    = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
  doi       = {10.63317/4cdk64dgm2w9}
}

Note on resources: The fine-tuned models are publicly released. The accompanying AG-MG Parallel Corpus is not publicly distributed due to the complex and uncertain copyright status of the source materials.