Opus Mt Tc Bible Big Roa En

🚀 opus-mt-tc-bible-big-roa-en

A neural machine translation model for translating from Romance languages (roa) to English (en)

🚀 Quick Start

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "É caro demais.",
    "Estamos muertos."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-roa-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:
#     It's too expensive.
#     We're dead.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-roa-en")
print(pipe("É caro demais."))

# expected output: It's too expensive.

✨ Features

• Multilingual Support: This model supports translation from multiple Romance languages (acf, an, ast, etc.) to English.
• High Performance: Achieves a BLEU score of 62.8 and a chr-F score of 0.76737 on the tatoeba-test-v2020-07-28-v2023-09-26 dataset.

📦 Installation

The installation steps are not provided in the original document, so this section is skipped.

💻 Usage Examples

The usage examples are already shown in the "Quick Start" section.

📚 Documentation

Model Details

Neural machine translation model for translating from Romance languages (roa) 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.

Uses

This model can be used for translation and text-to-text generation.

Risks, Limitations and Biases

⚠️ 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.

💡 Usage Tip

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

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 15:26:36 EEST 2024
• port machine: LM0-400-22516.local

📄 License

This model is licensed under the Apache-2.0 license.