WindstormLabs/origin-Helsinki-NLP-opus-mt-tc-bible-big-ine-deu_eng_fra_por_spa

opus-mt-tc-bible-big-ine-deuengfraporspa

Table of Contents

• —Model Details
• —Uses
• —Risks, Limitations and Biases
• —How to Get Started With the Model
• —Training
• —Evaluation
• —Citation Information
• —Acknowledgements

Model Details

Neural machine translation model for translating from Indo-European languages (ine) 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:

• —Developed by: Language Technology Research Group at the University of Helsinki
• —Model Type: Translation (transformer-big)
• —Release: 2024-05-30
• —License: Apache-2.0
• —Language(s):
• —Source Language(s): acf afr aln ang anp arg asm ast awa bal bar bel ben bho bis bos bpy bre bul bzj cat cbk ces chu ckb cnr cor cos crs csb cym dan deu diq div djk drt dsb dty egl ell eng enm ext fao fas fra frm fro frp frr fry fur gbm gcf gla gle glg glk glv gos got grc gsw guj hat hbs hif hin hne hns hrv hrx hsb hwc hye hyw icr isl ita jam jdt kas kea kmr kok kri ksh kur lad lah lat lav lij lim lit lld lmo lou lrc ltz mag mai mar mfe mkd mol mwl mzn nap nds nep nld nno nob non nor npi oci ofs ori orv osp oss pal pan pap pcd pcm pdc pes pfl pih pis pli pms pnt pol por prg prs pus rhg rmy roh rom ron rop rue rup rus san scn sco sdh sgs sin skr slk slv snd spa sqi srd srm srn srp stq swe swg syl szl tcs tgk tly tpi ukr urd vec vls wae wln xcl yid zea zza
• —Target Language(s): deu eng fra por spa
• —Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
• —Original Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip
• —Resources for more information:
• —OPUS-MT dashboard
• —OPUS-MT-train GitHub Repo
• —More information about MarianNMT models in the transformers library
• —Tatoeba Translation Challenge
• —HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)
• —A massively parallel Bible corpus

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

Training

• —Data: opusTCv20230926max50+bt+jhubc (source)
• —Pre-processing: SentencePiece (spm32k,spm32k)
• —Model Type: transformer-big
• —Original MarianNMT Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip
• —Training Scripts: GitHub Repo

Evaluation

• —Model scores at the OPUS-MT dashboard
• —test set translations: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt
• —test set scores: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt
• —benchmark results: benchmark_results.txt
• —benchmark output: benchmark_translations.zip

Citation Information

• —Publications: Democratizing neural machine translation with OPUS-MT and OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@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 11:49:33 EEST 2024
• —port machine: LM0-400-22516.local