Opus Mt Tc Big En Zle

🚀 opus-mt-tc-big-en-zle

A neural machine translation model designed to translate from English (en) to East Slavic languages (zle), facilitating seamless cross - language communication.

🚀 Quick Start

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., >>bel<<.

Here is a short example code to get you started:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>rus<< Are they coming as well?",
    ">>rus<< I didn't let Tom do what he wanted to do."
]

model_name = "pytorch-models/opus-mt-tc-big-en-zle"
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:
#     Они тоже приедут?
#     Я не позволил Тому сделать то, что он хотел.

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

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-zle")
print(pipe(">>rus<< Are they coming as well?"))

# expected output: Они тоже приедут?

✨ Features

• Multilingual Support: Capable of translating from English to multiple East Slavic languages, including Belarusian (bel), Russian (rus), and Ukrainian (ukr).
• Part of OPUS - MT Project: It is part of the [OPUS - MT project](https://github.com/Helsinki - NLP/Opus - MT), which aims to make neural machine translation models widely available for many languages.
• Powered by Marian NMT: Originally trained using the [Marian NMT](https://marian - nmt.github.io/) framework, an efficient NMT implementation in pure C++.
• Converted to PyTorch: The models have been converted to pyTorch using the transformers library by huggingface.

📚 Documentation

Model Info

Benchmarks

• Test Set Translations: [opusTCv20210807+bt_transformer - big_2022 - 03 - 13.test.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/eng - zle/opusTCv20210807+bt_transformer - big_2022 - 03 - 13.test.txt)
• Test Set Scores: [opusTCv20210807+bt_transformer - big_2022 - 03 - 13.eval.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/eng - zle/opusTCv20210807+bt_transformer - big_2022 - 03 - 13.eval.txt)
• Benchmark Results: benchmark_results.txt
• Benchmark Output: benchmark_translations.zip

Publications

Please cite the following publications 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",
}

🔧 Technical Details

• Model Conversion: The model has been converted to pyTorch using the transformers library by huggingface.
• Training Framework: Originally trained using the [Marian NMT](https://marian - nmt.github.io/) framework, an efficient NMT implementation written in pure C++.
• Training Data: The training data is taken from OPUS, and the training pipelines use the procedures of [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train).

Model Conversion Info

📄 License

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

Acknowledgements

The work is supported by the [European Language Grid](https://www.european - language - grid.eu/) as [pilot project 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding), 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.