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: 2022-03-13
source language(s): eng
target language(s): bel rus ukr
valid target language labels: >>bel<< >>rus<< >>ukr<<
model: transformer-big
data: opusTCv20210807+bt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+bt_transformer-big_2022-03-13.zip
more information released models: OPUS-MT eng-zle 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. >>bel<<

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>rus<< Are they coming as well?",
    ">>rus<< I didn't let Tom do what he wanted to do."
]

model_name = "pytorch-models/opus-mt-tc-big-en-zle"
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:
#     Они тоже приедут?
#     Я не позволил Тому сделать то, что он хотел.

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-en-zle")
print(pipe(">>rus<< Are they coming as well?"))

# expected output: Они тоже приедут?

Benchmarks

test set translations: opusTCv20210807+bt_transformer-big_2022-03-13.test.txt
test set scores: opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
eng-bel	tatoeba-test-v2021-08-07	0.50345	24.9	2500	16237
eng-rus	tatoeba-test-v2021-08-07	0.66182	45.5	19425	134296
eng-ukr	tatoeba-test-v2021-08-07	0.60175	37.7	13127	80998
eng-bel	flores101-devtest	0.42078	11.2	1012	24829
eng-rus	flores101-devtest	0.59654	32.7	1012	23295
eng-ukr	flores101-devtest	0.60131	32.1	1012	22810
eng-rus	newstest2012	0.62842	36.8	3003	64790
eng-rus	newstest2013	0.54627	26.9	3000	58560
eng-rus	newstest2014	0.68348	43.5	3003	61603
eng-rus	newstest2015	0.62621	34.9	2818	55915
eng-rus	newstest2016	0.60595	33.1	2998	62014
eng-rus	newstest2017	0.64249	37.3	3001	60253
eng-rus	newstest2018	0.61219	32.9	3000	61907
eng-rus	newstest2019	0.57902	31.8	1997	48147
eng-rus	newstest2020	0.52939	25.5	2002	47083
eng-rus	tico19-test	0.59314	33.7	2100	55843

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: Thu Mar 24 01:58:40 EET 2022
port machine: LM0-400-22516.local