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opus-mt-tc-big-zle-es

Neural machine translation model for translating from East Slavic languages (zle) 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 = [
    "Том був п'яничкою.",
    "Он достаточно взрослый, чтобы путешествовать одному."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-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:
#     Tom era un borracho.
#     Es lo suficientemente mayor como para viajar solo.

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-es")
print(pipe("Том був п'яничкою."))

# expected output: Tom era un borracho.

Benchmarks

langpair testset chr-F BLEU #sent #words
bel-spa tatoeba-test-v2021-08-07 0.65523 46.3 205 1412
rus-spa tatoeba-test-v2021-08-07 0.69933 52.3 10506 75246
ukr-spa tatoeba-test-v2021-08-07 0.68862 51.6 10115 59284
bel-spa flores101-devtest 0.44744 14.1 1012 29199
rus-spa flores101-devtest 0.50880 22.5 1012 29199
ukr-spa flores101-devtest 0.50943 22.7 1012 29199
rus-spa newstest2012 0.55185 29.0 3003 79006
rus-spa newstest2013 0.56826 31.7 3000 70528

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: Thu Mar 24 00:12:49 EET 2022
  • port machine: LM0-400-22516.local
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