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opus-mt-tc-big-fr-en

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

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 = [
    "J'ai adoré l'Angleterre.",
    "C'était la seule chose à faire."
]

model_name = "pytorch-models/opus-mt-tc-big-fr-en"
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 loved England.
#     It was the only thing to do.

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-fr-en")
print(pipe("J'ai adoré l'Angleterre."))

# expected output: I loved England.

Benchmarks

langpair testset chr-F BLEU #sent #words
fra-eng tatoeba-test-v2021-08-07 0.73772 59.8 12681 101754
fra-eng flores101-devtest 0.69350 46.0 1012 24721
fra-eng multi30k_test_2016_flickr 0.68005 49.7 1000 12955
fra-eng multi30k_test_2017_flickr 0.70596 52.0 1000 11374
fra-eng multi30k_test_2017_mscoco 0.69356 50.6 461 5231
fra-eng multi30k_test_2018_flickr 0.65751 44.9 1071 14689
fra-eng newsdiscussdev2015 0.59008 34.4 1500 27759
fra-eng newsdiscusstest2015 0.62603 40.2 1500 26982
fra-eng newssyscomb2009 0.57488 31.1 502 11818
fra-eng news-test2008 0.54316 26.5 2051 49380
fra-eng newstest2009 0.56959 30.4 2525 65399
fra-eng newstest2010 0.59561 33.4 2489 61711
fra-eng newstest2011 0.60271 33.8 3003 74681
fra-eng newstest2012 0.59507 33.6 3003 72812
fra-eng newstest2013 0.59691 34.8 3000 64505
fra-eng newstest2014 0.64533 39.4 3003 70708
fra-eng tico19-test 0.63326 41.3 2100 56323

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 19:02:28 EEST 2022
  • port machine: LM0-400-22516.local
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