Hugging Face's logo

Helsinki-NLP
/
opus-mt-tc-bible-big-ira-deu_eng_fra_por_spa

Translation
Transformers
PyTorch
Safetensors
marian
text2text-generation
opus-mt-tc-bible
Eval Results
Inference Endpoints
Model card Files Community
Edit model card

opus-mt-tc-bible-big-ira-deu_eng_fra_por_spa

Table of Contents

Model Details

Neural machine translation model for translating from Iranian languages (ira) 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-ira-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-ira-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
fas-deu tatoeba-test-v2021-08-07 0.59737 36.1 3185 25590
fas-eng tatoeba-test-v2021-08-07 0.59871 35.8 3762 31480
fas-fra tatoeba-test-v2021-08-07 0.58095 36.3 376 3377
kur_Latn-deu tatoeba-test-v2021-08-07 0.40276 24.9 223 1323
pes-eng tatoeba-test-v2021-08-07 0.60717 42.3 3757 31411
ckb-deu flores101-devtest 0.40117 11.6 1012 25094
ckb-eng flores101-devtest 0.48321 21.6 1012 24721
ckb-fra flores101-devtest 0.44260 17.2 1012 28343
ckb-por flores101-devtest 0.43179 16.2 1012 26519
fas-eng flores101-devtest 0.61134 34.4 1012 24721
pus-eng flores101-devtest 0.49556 22.7 1012 24721
pus-fra flores101-devtest 0.45248 17.8 1012 28343
tgk-eng flores101-devtest 0.53630 25.4 1012 24721
tgk-fra flores101-devtest 0.49084 21.0 1012 28343
tgk-spa flores101-devtest 0.43524 15.5 1012 29199
ckb-deu flores200-devtest 0.40369 11.7 1012 25094
ckb-eng flores200-devtest 0.48447 21.5 1012 24721
ckb-fra flores200-devtest 0.44026 17.1 1012 28343
ckb-por flores200-devtest 0.43192 16.4 1012 26519
pes-deu flores200-devtest 0.51542 21.5 1012 25094
pes-eng flores200-devtest 0.61372 34.9 1012 24721
pes-fra flores200-devtest 0.56347 29.2 1012 28343
pes-por flores200-devtest 0.55676 28.5 1012 26519
pes-spa flores200-devtest 0.48334 19.8 1012 29199
prs-deu flores200-devtest 0.50562 21.2 1012 25094
prs-eng flores200-devtest 0.60716 35.1 1012 24721
prs-fra flores200-devtest 0.54769 27.8 1012 28343
prs-por flores200-devtest 0.54073 27.2 1012 26519
prs-spa flores200-devtest 0.46850 18.6 1012 29199
tgk-deu flores200-devtest 0.43115 14.2 1012 25094
tgk-eng flores200-devtest 0.53705 25.6 1012 24721
tgk-fra flores200-devtest 0.48902 20.7 1012 28343
tgk-por flores200-devtest 0.48519 20.7 1012 26519
tgk-spa flores200-devtest 0.43563 15.7 1012 29199
fas-deu ntrex128 0.47408 16.7 1997 48761
fas-eng ntrex128 0.55350 26.4 1997 47673
fas-fra ntrex128 0.50311 22.1 1997 53481
fas-por ntrex128 0.48005 19.1 1997 51631
fas-spa ntrex128 0.50973 23.6 1997 54107
prs-deu ntrex128 0.45191 14.9 1997 48761
prs-eng ntrex128 0.54761 26.6 1997 47673
prs-fra ntrex128 0.47819 19.9 1997 53481
prs-por ntrex128 0.46241 17.4 1997 51631
prs-spa ntrex128 0.48712 21.4 1997 54107
pus-eng ntrex128 0.43901 17.4 1997 47673
pus-spa ntrex128 0.40812 14.1 1997 54107
tgk_Cyrl-eng ntrex128 0.46839 18.6 1997 47673
tgk_Cyrl-fra ntrex128 0.42569 15.1 1997 53481
tgk_Cyrl-por ntrex128 0.41632 13.7 1997 51631
tgk_Cyrl-spa ntrex128 0.43763 16.8 1997 54107
ckb-eng tico19-test 0.61905 40.1 2100 56315
ckb-fra tico19-test 0.45070 19.7 2100 64661
ckb-por tico19-test 0.49617 22.9 2100 62729
ckb-spa tico19-test 0.50543 24.9 2100 66563
fas-eng tico19-test 0.64016 37.3 2100 56315
fas-fra tico19-test 0.53319 26.1 2100 64661
fas-por tico19-test 0.58008 30.6 2100 62729
fas-spa tico19-test 0.59239 33.3 2100 66563
prs-eng tico19-test 0.61702 34.8 2100 56824
prs-fra tico19-test 0.51218 24.0 2100 64661
prs-por tico19-test 0.55888 28.6 2100 62729
prs-spa tico19-test 0.57494 31.1 2100 66563
pus-eng tico19-test 0.57586 32.1 2100 56315
pus-fra tico19-test 0.46091 19.2 2100 64661
pus-por tico19-test 0.51033 24.1 2100 62729
pus-spa tico19-test 0.51857 25.9 2100 66563

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 11:54:09 EEST 2024
  • port machine: LM0-400-22516.local
Downloads last month
17
Safetensors
Model size
239M params
Tensor type
F32
·
Inference Examples
Translation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Collection including Helsinki-NLP/opus-mt-tc-bible-big-ira-deu_eng_fra_por_spa

Evaluation results

View on Papers With Code