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opus-mt-tc-big-zle-gmq

Neural machine translation model for translating from East Slavic languages (zle) to North Germanic languages (gmq).

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

This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of >>id<< (id = valid target language ID), e.g. >>dan<<

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>dan<< Заўтра ўжо чацвер.",
    ">>swe<< Том грав з Мері в кішки-мишки."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-gmq"
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 morgen er det torsdag.
#     Tom lekte med Mary i katt-möss.

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-gmq")
print(pipe(">>dan<< Заўтра ўжо чацвер."))

# expected output: I morgen er det torsdag.

Benchmarks

langpair testset chr-F BLEU #sent #words
rus-dan tatoeba-test-v2021-08-07 0.74307 59.6 1713 11746
rus-nob tatoeba-test-v2021-08-07 0.66376 46.1 1277 11672
rus-swe tatoeba-test-v2021-08-07 0.69608 53.3 1282 8449
bel-dan flores101-devtest 0.47621 13.9 1012 24638
bel-nob flores101-devtest 0.44966 10.8 1012 23873
bel-swe flores101-devtest 0.47274 13.2 1012 23121
rus-dan flores101-devtest 0.55917 28.0 1012 24638
rus-nob flores101-devtest 0.50724 20.6 1012 23873
rus-swe flores101-devtest 0.55812 26.4 1012 23121
ukr-dan flores101-devtest 0.57829 30.3 1012 24638
ukr-nob flores101-devtest 0.52271 21.1 1012 23873
ukr-swe flores101-devtest 0.57499 28.8 1012 23121

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 23:13:54 EET 2022
  • port machine: LM0-400-22516.local
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