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opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa

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opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa

Table of Contents

Model Details

Neural machine translation model for translating from Baltic languages (bat) to unknown (deu+eng+fra+por+spa).

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. Model Description:

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. >>deu<<

Uses

This model can be used for translation and text-to-text generation.

Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

How to Get Started With the Model

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>deu<< Replace this with text in an accepted source language.",
    ">>spa<< This is the second sentence."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa"
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) )

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-bible-big-bat-deu_eng_fra_por_spa")
print(pipe(">>deu<< Replace this with text in an accepted source language."))

Training

Evaluation

langpair testset chr-F BLEU #sent #words
lav-eng tatoeba-test-v2021-08-07 0.63015 21.5 1631 11213
lit-deu tatoeba-test-v2021-08-07 0.66527 47.5 1115 8531
lit-eng tatoeba-test-v2021-08-07 0.72975 58.9 2528 17855
lit-spa tatoeba-test-v2021-08-07 0.67956 49.9 454 2751
lav-deu flores101-devtest 0.54001 23.8 1012 25094
lav-fra flores101-devtest 0.57002 29.4 1012 28343
lav-por flores101-devtest 0.55155 26.7 1012 26519
lav-spa flores101-devtest 0.49259 20.8 1012 29199
lit-eng flores101-devtest 0.59073 32.1 1012 24721
lit-por flores101-devtest 0.55106 27.8 1012 26519
lit-deu flores200-devtest 0.53223 23.7 1012 25094
lit-eng flores200-devtest 0.59361 32.6 1012 24721
lit-fra flores200-devtest 0.56786 30.0 1012 28343
lit-por flores200-devtest 0.55393 28.2 1012 26519
lit-spa flores200-devtest 0.49041 20.9 1012 29199
lav-eng newstest2017 0.49729 22.0 2001 47511
lit-eng newstest2019 0.59971 31.2 1000 25878
lav-deu ntrex128 0.47317 18.5 1997 48761
lav-eng ntrex128 0.53734 19.7 1997 47673
lav-fra ntrex128 0.47843 19.6 1997 53481
lav-por ntrex128 0.47027 19.3 1997 51631
lav-spa ntrex128 0.49428 22.7 1997 54107
lit-deu ntrex128 0.50279 19.4 1997 48761
lit-eng ntrex128 0.56642 28.1 1997 47673
lit-fra ntrex128 0.51276 22.6 1997 53481
lit-por ntrex128 0.50864 22.6 1997 51631
lit-spa ntrex128 0.53105 25.9 1997 54107

Citation Information 

@article{tiedemann2023democratizing,
  title={Democratizing neural machine translation with {OPUS-MT}},
  author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
  journal={Language Resources and Evaluation},
  number={58},
  pages={713--755},
  year={2023},
  publisher={Springer Nature},
  issn={1574-0218},
  doi={10.1007/s10579-023-09704-w}
}

@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",
}

Acknowledgements

The work is supported by the HPLT project, funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland, and the EuroHPC supercomputer LUMI.

Model conversion info

  • transformers version: 4.45.1
  • OPUS-MT git hash: a0ea3b3
  • port time: Mon Oct 7 17:27:51 EEST 2024
  • port machine: LM0-400-22516.local
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