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opus-mt-tc-big-zlw-en

Neural machine translation model for translating from West Slavic languages (zlw) 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 = [
    "Aoi'ego hobby to tańczenie.",
    "Myślisz, że Tom planuje to zrobić?"
]

model_name = "pytorch-models/opus-mt-tc-big-zlw-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:
#     Aoi's hobby is dancing.
#     You think Tom's planning on doing that?

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-zlw-en")
print(pipe("Aoi'ego hobby to tańczenie."))

# expected output: Aoi's hobby is dancing.

Benchmarks

langpair testset chr-F BLEU #sent #words
ces-eng tatoeba-test-v2021-08-07 0.71861 57.4 13824 105010
pol-eng tatoeba-test-v2021-08-07 0.70544 55.7 10099 75766
ces-eng flores101-devtest 0.66444 41.2 1012 24721
pol-eng flores101-devtest 0.58301 29.6 1012 24721
slk-eng flores101-devtest 0.66103 40.0 1012 24721
ces-eng multi30k_test_2016_flickr 0.61482 37.6 1000 12955
ces-eng multi30k_test_2018_flickr 0.61405 37.4 1071 14689
pol-eng newsdev2020 0.60478 32.7 2000 46654
ces-eng newssyscomb2009 0.56495 30.2 502 11818
ces-eng news-test2008 0.54300 26.3 2051 49380
ces-eng newstest2009 0.56309 29.5 2525 65399
ces-eng newstest2010 0.57778 30.7 2489 61711
ces-eng newstest2011 0.57336 30.9 3003 74681
ces-eng newstest2012 0.56761 29.4 3003 72812
ces-eng newstest2013 0.58809 32.8 3000 64505
ces-eng newstest2014 0.64401 38.7 3003 68065
ces-eng newstest2015 0.58607 33.4 2656 53569
ces-eng newstest2016 0.61780 37.1 2999 64670
ces-eng newstest2017 0.58259 32.5 3005 61721
ces-eng newstest2018 0.58677 33.1 2983 63495
pol-eng newstest2020 0.60047 32.6 1001 21755

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 20:19:48 EEST 2022
  • port machine: LM0-400-22516.local
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