opus-mt-tc-big-zle-de

Neural machine translation model for translating from East Slavic languages (zle) to German (de).

This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train.

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}

Model info

Release: 2022-03-19
source language(s): bel rus ukr
target language(s): deu
model: transformer-big
data: opusTCv20210807 (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807_transformer-big_2022-03-19.zip
more information released models: OPUS-MT zle-deu README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Это был по-настоящему прекрасный день.",
    "Дождь кончился?"
]

model_name = "pytorch-models/opus-mt-tc-big-zle-de"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# expected output:
#     Es war ein wirklich schöner Tag.
#     Ist der Regen vorbei?

You can also use OPUS-MT models with the transformers pipelines, for example:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-de")
print(pipe("Это был по-настоящему прекрасный день."))

# expected output: Es war ein wirklich schöner Tag.

Benchmarks

test set translations: opusTCv20210807_transformer-big_2022-03-19.test.txt
test set scores: opusTCv20210807_transformer-big_2022-03-19.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
bel-deu	tatoeba-test-v2021-08-07	0.63720	44.8	551	4182
rus-deu	tatoeba-test-v2021-08-07	0.69768	51.8	12800	98842
ukr-deu	tatoeba-test-v2021-08-07	0.70860	54.7	10319	64646
bel-deu	flores101-devtest	0.47052	12.9	1012	25094
rus-deu	flores101-devtest	0.56159	26.1	1012	25094
ukr-deu	flores101-devtest	0.57251	28.1	1012	25094
rus-deu	newstest2012	0.49257	19.8	3003	72886
rus-deu	newstest2013	0.54015	25.2	3000	63737

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

transformers version: 4.16.2
OPUS-MT git hash: 1bdabf7
port time: Wed Mar 23 22:16:45 EET 2022
port machine: LM0-400-22516.local

Helsinki-NLP
/

opus-mt-tc-big-zle-de