origin-Helsinki-NLP-opus-mt-tc-bible-big-sem-en

opus-mt-tc-bible-big-sem-en

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 Semitic languages (sem) to English (en).

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: 2024-08-17
• License: Apache-2.0
• Language(s):
  • Source Language(s): acm afb amh apc ara arc arq arz hbo heb jpa mlt oar phn sgw syc syr tig tir tmr
  • Target Language(s): eng
• Original Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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

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 = [
    "تعال لتأخذنا.",
    "ربما سيغير رأيه."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-sem-en"
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:
#     Come and get us.
#     Maybe he'll change his mind.

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-sem-en")
print(pipe("تعال لتأخذنا."))

# expected output: Come and get us.

Training

• Data: opusTCv20230926max50+bt+jhubc (source)
• Pre-processing: SentencePiece (spm32k,spm32k)
• Model Type: transformer-big
• Original MarianNMT Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip
• Training Scripts: GitHub Repo

Evaluation

• Model scores at the OPUS-MT dashboard
• test set translations: opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt
• test set scores: opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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 22:15:32 EEST 2024
• port machine: LM0-400-22516.local