Opus Mt Tc Big Bg En

🚀 opus-mt-tc-big-bg-en

A neural machine translation model for translating from Bulgarian (bg) to English (en). This model is part of the OPUS-MT project, aiming to make neural machine translation models widely available and accessible for many languages.

🚀 Quick Start

This model is designed to translate text from Bulgarian to English. It's part of the OPUS - MT project, leveraging the Marian NMT framework and converted to pyTorch using the transformers library.

✨ Features

• Multilingual Accessibility: Part of the OPUS - MT project, making translation models available for many languages.
• Efficient Training: Originally trained using the Marian NMT framework, an efficient NMT implementation in pure C++.
• Converted to PyTorch: Converted to pyTorch using the transformers library by huggingface for easy integration.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "2001 е годината, с която започва 21-ви век.",
    "Това е Copacabana!"
]

model_name = "pytorch-models/opus-mt-tc-big-bg-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:
#     2001 was the year the 21st century began.
#     It's Copacabana!

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-bg-en")
print(pipe("2001 е годината, с която започва 21-ви век."))

# expected output: 2001 was the year the 21st century began.

📚 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

Publications

Please cite the following publications if you use this model:

• OPUS - MT – Building open translation services for the World
• The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT

@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",
}

🔧 Technical Details

The model is originally trained using the Marian NMT framework, an efficient NMT implementation written in pure C++. It has been converted to pyTorch using the transformers library by huggingface. Training data is sourced from OPUS and the training pipelines follow the procedures of OPUS - MT - train.

📄 License

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

📋 Model Conversion Info

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.