Opus Mt Tc Bible Big Mul Mul

🚀 opus-mt-tc-bible-big-mul-mul

A neural machine translation model for translating between multiple languages.

🚀 Quick Start

This model can be used for translation and text - to - text generation. Here are some short example codes to get you started:

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>rus<< You'd better not speak to Tom about that.",
    ">>ceb<< How are you?"
]

model_name = "pytorch-models/opus-mt-tc-bible-big-mul-mul"
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:
#     Лучше бы не поговорить с Томом об этом.
#     Sa unsang paagi ikaw?

Advanced Usage

You can also use OPUS - MT models with the transformers pipelines:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-mul-mul")
print(pipe(">>rus<< You'd better not speak to Tom about that."))

# expected output: Лучше бы не поговорить с Томом об этом.

✨ Features

• It is a multilingual translation model that supports translation between multiple languages.
• It is part of the OPUS - MT project, aiming to make neural machine translation models widely available for many languages in the world.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Model Details

Neural machine translation model for translating from Multiple languages (mul) to Multiple languages (mul). Note that many of the listed languages will not be well supported by the model as the training data is very limited for the majority of the languages. Translation performance varies a lot and for a large number of language pairs it will not work at all.

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., >>aar<<

🔧 Technical Details

Training

• Data: opusTCv20230926+bt+jhubc (source)
• Pre - processing: SentencePiece (spm64k,spm64k)
• Model Type: transformer - big
• Original MarianNMT Model: opusTCv20230926+bt+jhubc_transformer-big_2024-08-17.zip
• Training Scripts: GitHub Repo

Evaluation

• Model scores at the OPUS - MT dashboard
• test set translations: opusTCv20230926+bt+jhubc_transformer-big_2024-08-17.test.txt
• test set scores: opusTCv20230926+bt+jhubc_transformer-big_2024-08-17.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

📄 License

The model is released under the Apache - 2.0 license.

📚 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](https://www.lumi - supercomputer.eu/).

Model conversion info

• transformers version: 4.45.1
• OPUS - MT git hash: 0882077
• port time: Wed Oct 9 19:20:34 EEST 2024
• port machine: LM0 - 400 - 22516.local

⚠️ Important Note

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. Also, many of the listed languages will not be well supported by the model as the training data is very limited for the majority of the languages. Translation performance varies a lot and for a large number of language pairs it will not work at all.