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opus-mt-tc-big-en-zle

Neural machine translation model for translating from English (en) to East Slavic languages (zle).

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.

@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

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:

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:

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

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
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