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opus-mt-tc-bible-big-gmw-fra_ita_por_spa

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metadata
library_name: transformers
language:
  - af
  - ang
  - bar
  - bi
  - bzj
  - de
  - djk
  - drt
  - en
  - enm
  - es
  - fr
  - frr
  - fy
  - gos
  - gsw
  - hrx
  - hwc
  - icr
  - it
  - jam
  - kri
  - ksh
  - lb
  - li
  - nds
  - nl
  - pcm
  - pis
  - pt
  - rop
  - sco
  - srm
  - srn
  - stq
  - swg
  - tcs
  - tpi
  - yi
  - zea
tags:
  - translation
  - opus-mt-tc-bible
license: apache-2.0
model-index:
  - name: opus-mt-tc-bible-big-gmw-fra_ita_por_spa
    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: 40.9
          - name: chr-F
            type: chrf
            value: 0.68712

opus-mt-tc-bible-big-gmw-fra_ita_por_spa

Table of Contents

Model Details

Neural machine translation model for translating from West Germanic languages (gmw) to unknown (fra+ita+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. >>fra<<

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 = [
    ">>fra<< Ich möchte diesen Tag nie vergessen.",
    ">>ita<< Chess is an amusing activity."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-gmw-fra_ita_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) )

# expected output:
#     Je ne veux jamais oublier cette journée.
#     Gli scacchi sono un'attività divertente.

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-fra_ita_por_spa")
print(pipe(">>fra<< Ich möchte diesen Tag nie vergessen."))

# expected output: Je ne veux jamais oublier cette journée.

Training

Evaluation

langpair testset chr-F BLEU #sent #words
multi-multi tatoeba-test-v2020-07-28-v2023-09-26 0.68712 40.9 10000 85215

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 11:29:19 EEST 2024
  • port machine: LM0-400-22516.local