Opus Mt Tc Big Zle En

🚀 opus-mt-tc-big-zle-en

This is a neural machine translation model designed for translating from East Slavic languages (zle) to English (en). It's part of the OPUS-MT project, aiming to make neural machine translation models widely accessible for numerous languages globally.

🚀 Quick Start

If you want to use this model for translation, here are some simple code examples to get you started.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Скільки мені слід купити пива?",
    "Я клієнтка."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-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:
#     How much beer should I buy?
#     I'm a client.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-en")
print(pipe("Скільки мені слід купити пива?"))

# expected output: How much beer should I buy?

✨ Features

• Multilingual Support: Capable of translating from multiple East Slavic languages (Belarusian, Russian, Ukrainian) to English.
• Part of OPUS-MT Project: Leverages the resources and framework of the OPUS-MT project, ensuring high - quality training and wide availability.
• Converted to PyTorch: The model has been converted to PyTorch using the transformers library by Hugging Face, making it more accessible for Python developers.

📦 Installation

The installation process mainly involves setting up the necessary Python libraries. You need to install the transformers library. You can use the following command:

pip install transformers

📚 Documentation

Model Info

Benchmarks

• Test Set Translations: opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
• Test Set Scores: opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt
• Benchmark Results: benchmark_results.txt
• Benchmark Output: benchmark_translations.zip

🔧 Technical Details

This model is originally trained using the Marian NMT framework, an efficient NMT implementation written in pure C++. The 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.

Acknowledgements

The development of this model is supported by multiple projects and institutions:

• European Language Grid as pilot project 2866.
• FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113).
• MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069.

We also appreciate the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

• Transformers Version: 4.16.2
• OPUS-MT Git Hash: 1bdabf7
• Port Time: Wed Mar 23 22:17:11 EET 2022
• Port Machine: LM0-400-22516.local

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",
}