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

Table of Contents

Model Details

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

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. >>lav<<

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

model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat"
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-deu_eng_fra_por_spa-bat")
print(pipe(">>lav<< Replace this with text in an accepted source language."))

Training

Evaluation

langpair testset chr-F BLEU #sent #words
deu-lit tatoeba-test-v2021-08-07 0.65379 39.8 1115 7091
eng-lav tatoeba-test-v2021-08-07 0.68823 46.4 1631 9932
eng-lit tatoeba-test-v2021-08-07 0.67792 39.8 2528 14942
spa-lit tatoeba-test-v2021-08-07 0.68133 43.3 454 2352
deu-lav flores101-devtest 0.54724 24.4 1012 22092
eng-lav flores101-devtest 0.59955 31.0 1012 22092
eng-lit flores101-devtest 0.58961 27.2 1012 20695
fra-lav flores101-devtest 0.54276 24.2 1012 22092
fra-lit flores101-devtest 0.54665 22.4 1012 20695
spa-lav flores101-devtest 0.50131 17.8 1012 22092
deu-lit flores200-devtest 0.54957 22.6 1012 20695
eng-lit flores200-devtest 0.59338 27.7 1012 20695
fra-lit flores200-devtest 0.54683 22.3 1012 20695
por-lit flores200-devtest 0.55033 22.6 1012 20695
spa-lit flores200-devtest 0.50725 16.9 1012 20695
eng-lav newstest2017 0.53192 21.5 2001 39392
eng-lit newstest2019 0.51714 18.3 998 19711
deu-lav ntrex128 0.47980 16.8 1997 44709
deu-lit ntrex128 0.50645 17.6 1997 41189
eng-lav ntrex128 0.51026 20.6 1997 44709
eng-lit ntrex128 0.54187 21.5 1997 41189
fra-lav ntrex128 0.45346 15.5 1997 44709
fra-lit ntrex128 0.48870 16.2 1997 41189
por-lav ntrex128 0.47809 17.3 1997 44709
por-lit ntrex128 0.50653 17.5 1997 41189
spa-lav ntrex128 0.47690 17.1 1997 44709
spa-lit ntrex128 0.50412 17.1 1997 41189

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: 0882077
  • port time: Tue Oct 8 08:59:36 EEST 2024
  • port machine: LM0-400-22516.local
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Collection including Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat

Evaluation results