Opus Mt Tc Big En Fr

🚀 opus-mt-tc-big-en-fr

A neural machine translation model designed for translating English (en) to French (fr), offering a practical solution for cross - language communication.

🚀 Quick Start

The opus-mt-tc-big-en-fr is a neural machine translation model that enables seamless translation from English to French. It's part of the OPUS - MT project, which aims to make neural machine translation models accessible across multiple languages.

✨ Features

• Multilingual Accessibility: As part of the OPUS - MT project, it contributes to making neural machine translation available for a wide range of languages.
• Efficient Training: Trained using the Marian NMT framework, an efficient NMT implementation in pure C++.
• Publication - Backed: Supported by relevant publications, ensuring its academic and practical credibility.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "The Portuguese teacher is very demanding.",
    "When was your last hearing test?"
]

model_name = "pytorch-models/opus-mt-tc-big-en-fr"
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:
#     Le professeur de portugais est très exigeant.
#     Quand a eu lieu votre dernier test auditif ?

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-fr")
print(pipe("The Portuguese teacher is very demanding."))

# expected output: Le professeur de portugais est très exigeant.

📚 Documentation

Model info

Benchmarks

• Test set translations: opusTCv20210807+bt_transformer - big_2022 - 03 - 09.test.txt
• Test set scores: opusTCv20210807+bt_transformer - big_2022 - 03 - 09.eval.txt
• Benchmark results: benchmark_results.txt
• Benchmark output: benchmark_translations.zip

Model conversion info

🔧 Technical Details

This model is part of the OPUS - MT project. It was originally trained using the Marian NMT framework and then converted to pyTorch using the transformers library by huggingface. The training data is sourced from OPUS, and the training pipelines follow the procedures of OPUS - MT - train.

• Publications: 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.)

@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",
}

📄 License

This model is released under the cc - by - 4.0 license.

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.