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opus-mt-tc-big-en-es

Neural machine translation model for translating from English (en) to Spanish (es).

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 = [
    "A wasp stung him and he had an allergic reaction.",
    "I love nature."
]

model_name = "pytorch-models/opus-mt-tc-big-en-es"
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:
#     Una avispa lo picó y tuvo una reacción alérgica.
#     Me encanta la naturaleza.

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-es")
print(pipe("A wasp stung him and he had an allergic reaction."))

# expected output: Una avispa lo picó y tuvo una reacción alérgica.

Benchmarks

langpair testset chr-F BLEU #sent #words
eng-spa tatoeba-test-v2021-08-07 0.73863 57.2 16583 134710
eng-spa flores101-devtest 0.56440 28.5 1012 29199
eng-spa newssyscomb2009 0.58415 31.5 502 12503
eng-spa news-test2008 0.56707 30.1 2051 52586
eng-spa newstest2009 0.57836 30.2 2525 68111
eng-spa newstest2010 0.62357 37.6 2489 65480
eng-spa newstest2011 0.62415 38.9 3003 79476
eng-spa newstest2012 0.63031 39.5 3003 79006
eng-spa newstest2013 0.60354 35.9 3000 70528
eng-spa tico19-test 0.73554 53.0 2100 66563

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 18:03:53 EEST 2022
  • port machine: LM0-400-22516.local
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