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opus-mt-tc-bible-big-gmw-deu_eng_nld

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library_name: transformers
language:
  - af
  - ang
  - bar
  - bi
  - bzj
  - de
  - djk
  - drt
  - en
  - enm
  - frr
  - fy
  - gos
  - gsw
  - hrx
  - hwc
  - icr
  - jam
  - kri
  - ksh
  - lb
  - li
  - nds
  - nl
  - ofs
  - pcm
  - pdc
  - pfl
  - pih
  - pis
  - rop
  - sco
  - srm
  - srn
  - stq
  - swg
  - tcs
  - tpi
  - vls
  - wae
  - yi
  - zea
tags:
  - translation
  - opus-mt-tc-bible
license: apache-2.0
model-index:
  - name: opus-mt-tc-bible-big-gmw-deu_eng_nld
    results:
      - task:
          name: Translation multi-multi
          type: translation
          args: multi-multi
        dataset:
          name: tatoeba-test-v2020-07-28-v2023-09-26
          type: tatoeba_mt
          args: multi-multi
        metrics:
          - name: BLEU
            type: bleu
            value: 48.3
          - name: chr-F
            type: chrf
            value: 0.6699

opus-mt-tc-bible-big-gmw-deu_eng_nld

Table of Contents

Model Details

Neural machine translation model for translating from West Germanic languages (gmw) to unknown (deu+eng+nld).

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 = [
    ">>eng<< Wer ist der Herr, mit dem er spricht?",
    ">>eng<< Bel ons."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-gmw-deu_eng_nld"
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:
#     Who is the Lord to whom He speaks?
#     Call us.

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-gmw-deu_eng_nld")
print(pipe(">>eng<< Wer ist der Herr, mit dem er spricht?"))

# expected output: Who is the Lord to whom He speaks?

Training

Evaluation

langpair testset chr-F BLEU #sent #words
multi-multi tatoeba-test-v2020-07-28-v2023-09-26 0.66990 48.3 10000 81301

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 11:22:20 EEST 2024
  • port machine: LM0-400-22516.local