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opus-mt-tc-big-zle-fr

Neural machine translation model for translating from East Slavic languages (zle) 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 = [
    "Подавай блюдо на тарелке.",
    "Операція не може чекати."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-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:
#     Servez le plat dans l'assiette.
#     L'opération ne peut pas attendre.

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-zle-fr")
print(pipe("Подавай блюдо на тарелке."))

# expected output: Servez le plat dans l'assiette.

Benchmarks

langpair testset chr-F BLEU #sent #words
bel-fra tatoeba-test-v2020-07-28-v2021-08-07 0.65415 46.4 283 2005
multi-fra tatoeba-test-v2020-07-28-v2021-08-07 0.68422 52.4 10000 66671
rus-fra tatoeba-test-v2020-07-28-v2021-08-07 0.68699 51.8 11490 80573
ukr-fra tatoeba-test-v2020-07-28-v2021-08-07 0.67887 50.7 10035 63222
rus-fra newstest2012 0.53679 25.3 3003 78011
rus-fra newstest2013 0.56211 29.7 3000 70037

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 22:45:20 EET 2022
  • port machine: LM0-400-22516.local
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