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Neural machine translation model for translating from East Slavic languages (zle) to German (de).

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.

    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",

    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


A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Это был по-настоящему прекрасный день.",
    "Дождь кончился?"

model_name = "pytorch-models/opus-mt-tc-big-zle-de"
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:
#     Es war ein wirklich schöner Tag.
#     Ist der Regen vorbei?

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-de")
print(pipe("Это был по-настоящему прекрасный день."))

# expected output: Es war ein wirklich schöner Tag.


langpair testset chr-F BLEU #sent #words
bel-deu tatoeba-test-v2021-08-07 0.63720 44.8 551 4182
rus-deu tatoeba-test-v2021-08-07 0.69768 51.8 12800 98842
ukr-deu tatoeba-test-v2021-08-07 0.70860 54.7 10319 64646
bel-deu flores101-devtest 0.47052 12.9 1012 25094
rus-deu flores101-devtest 0.56159 26.1 1012 25094
ukr-deu flores101-devtest 0.57251 28.1 1012 25094
rus-deu newstest2012 0.49257 19.8 3003 72886
rus-deu newstest2013 0.54015 25.2 3000 63737


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 22:16:45 EET 2022
  • port machine: LM0-400-22516.local
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