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: 2022-03-14
source language(s): rus ukr
target language(s): dan nob nor swe
valid target language labels: >>dan<< >>nob<< >>nor<< >>swe<<
model: transformer-big
data: opusTCv20210807+pft (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+pft_transformer-big_2022-03-14.zip
more information released models: OPUS-MT zle-gmq README
more information about the model: MarianMT

This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of >>id<< (id = valid target language ID), e.g. >>dan<<

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>dan<< Заўтра ўжо чацвер.",
    ">>swe<< Том грав з Мері в кішки-мишки."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-gmq"
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:
#     I morgen er det torsdag.
#     Tom lekte med Mary i katt-möss.

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

python

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-gmq")
print(pipe(">>dan<< Заўтра ўжо чацвер."))

# expected output: I morgen er det torsdag.

Benchmarks

test set translations: opusTCv20210807+pft_transformer-big_2022-03-14.test.txt
test set scores: opusTCv20210807+pft_transformer-big_2022-03-14.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
rus-dan	tatoeba-test-v2021-08-07	0.74307	59.6	1713	11746
rus-nob	tatoeba-test-v2021-08-07	0.66376	46.1	1277	11672
rus-swe	tatoeba-test-v2021-08-07	0.69608	53.3	1282	8449
bel-dan	flores101-devtest	0.47621	13.9	1012	24638
bel-nob	flores101-devtest	0.44966	10.8	1012	23873
bel-swe	flores101-devtest	0.47274	13.2	1012	23121
rus-dan	flores101-devtest	0.55917	28.0	1012	24638
rus-nob	flores101-devtest	0.50724	20.6	1012	23873
rus-swe	flores101-devtest	0.55812	26.4	1012	23121
ukr-dan	flores101-devtest	0.57829	30.3	1012	24638
ukr-nob	flores101-devtest	0.52271	21.1	1012	23873
ukr-swe	flores101-devtest	0.57499	28.8	1012	23121

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 23:13:54 EET 2022
port machine: LM0-400-22516.local