Opus Mt Tc Big En Bg

🚀 opus-mt-tc-big-en-bg

A neural machine translation model designed to translate from English (en) to Bulgarian (bg). This model is part of the OPUS-MT project, aiming to make NMT models accessible for various languages.

🚀 Quick Start

This model is part of the OPUS-MT project, which aims to make neural machine translation models widely available for many languages. It's initially trained with Marian NMT, an efficient NMT implementation in pure C++, and then converted to pyTorch using the transformers library by huggingface. The training data comes 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",
}

✨ Features

• Language Support: Supports translation from English (en) to Bulgarian (bg).
• Open Source: Part of the OPUS-MT project, making it accessible and open for many languages.
• High - performance Training: Trained with Marian NMT, an efficient NMT implementation in pure C++.

📦 Installation

This README doesn't provide specific installation steps. You can refer to the official documentation of the OPUS - MT project for installation guidance.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "2001 is the year when the 21st century begins.",
    "This is Copacabana!"
]

model_name = "pytorch-models/opus-mt-tc-big-en-bg"
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 е годината, в която започва 21-ви век.
#     Това е Копакабана!

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-big-en-bg")
print(pipe("2001 is the year when the 21st century begins."))

# expected output: 2001 е годината, в която започва 21-ви век.

📚 Documentation

Model info

Benchmarks

• test set translations: opusTCv20210807+bt_transformer-big_2022-02-25.test.txt
• test set scores: opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

🔧 Technical Details

• Model Conversion:
  • Transformers version: 4.16.2
  • OPUS - MT git hash: 3405783
  • Port time: Wed Apr 13 16:29:32 EEST 2022
  • Port machine: LM0 - 400 - 22516.local

📄 License

The 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.