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opus-mt-tc-big-cat_oci_spa-en

Neural machine translation model for translating from Catalan, Occitan and Spanish (cat+oci+spa) 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 = [
    "¿Puedo hacerte una pregunta?",
    "Toca algo de música."
]

model_name = "pytorch-models/opus-mt-tc-big-cat_oci_spa-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:
#     Can I ask you a question?
#     He plays some music.

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-cat_oci_spa-en")
print(pipe("¿Puedo hacerte una pregunta?"))

# expected output: Can I ask you a question?

Benchmarks

langpair testset chr-F BLEU #sent #words
cat-eng tatoeba-test-v2021-08-07 0.72019 57.3 1631 12627
spa-eng tatoeba-test-v2021-08-07 0.76017 62.3 16583 138123
cat-eng flores101-devtest 0.69572 45.4 1012 24721
oci-eng flores101-devtest 0.63347 37.5 1012 24721
spa-eng flores101-devtest 0.59696 29.9 1012 24721
spa-eng newssyscomb2009 0.57104 30.8 502 11818
spa-eng news-test2008 0.55440 27.9 2051 49380
spa-eng newstest2009 0.57153 30.2 2525 65399
spa-eng newstest2010 0.61890 36.8 2489 61711
spa-eng newstest2011 0.60278 34.7 3003 74681
spa-eng newstest2012 0.62760 38.6 3003 72812
spa-eng newstest2013 0.60994 35.3 3000 64505
spa-eng tico19-test 0.74033 51.8 2100 56315

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 18:30:38 EEST 2022
  • port machine: LM0-400-22516.local
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