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opus-mt-tc-big-en-fr

Neural machine translation model for translating from English (en) to French (fr).

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

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "The Portuguese teacher is very demanding.",
    "When was your last hearing test?"
]

model_name = "pytorch-models/opus-mt-tc-big-en-fr"
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:
#     Le professeur de portugais est très exigeant.
#     Quand a eu lieu votre dernier test auditif ?

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-fr")
print(pipe("The Portuguese teacher is very demanding."))

# expected output: Le professeur de portugais est très exigeant.

Benchmarks

langpair testset chr-F BLEU #sent #words
eng-fra tatoeba-test-v2021-08-07 0.69621 53.2 12681 106378
eng-fra flores101-devtest 0.72494 52.2 1012 28343
eng-fra multi30k_test_2016_flickr 0.72361 52.4 1000 13505
eng-fra multi30k_test_2017_flickr 0.72826 52.8 1000 12118
eng-fra multi30k_test_2017_mscoco 0.73547 54.7 461 5484
eng-fra multi30k_test_2018_flickr 0.66723 43.7 1071 15867
eng-fra newsdiscussdev2015 0.60471 33.4 1500 27940
eng-fra newsdiscusstest2015 0.64915 40.3 1500 27975
eng-fra newssyscomb2009 0.58903 30.7 502 12331
eng-fra news-test2008 0.55516 27.6 2051 52685
eng-fra newstest2009 0.57907 30.0 2525 69263
eng-fra newstest2010 0.60156 33.5 2489 66022
eng-fra newstest2011 0.61632 35.0 3003 80626
eng-fra newstest2012 0.59736 32.8 3003 78011
eng-fra newstest2013 0.59700 34.6 3000 70037
eng-fra newstest2014 0.66686 41.9 3003 77306
eng-fra tico19-test 0.63022 40.6 2100 64661

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: 3405783
  • port time: Wed Apr 13 17:07:05 EEST 2022
  • port machine: LM0-400-22516.local
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