Opus Mt Tc Big En Et

🚀 opus-mt-tc-big-en-et

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

🚀 Quick Start

This model is designed for English-to-Estonian translation. It's part of the OPUS-MT project, which uses the Marian NMT framework for training and is converted to pyTorch via the transformers library by huggingface.

✨ Features

• Multilingual Accessibility: Part of a project that makes neural machine translation models available for many languages.
• Efficient Training: Trained using the efficient Marian NMT framework written in pure C++.
• Data Sourced from OPUS: Utilizes data from OPUS for training.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>est<< A cab is waiting.",
    ">>vro<< Where do you live?"
]

model_name = "pytorch-models/opus-mt-tc-big-en-et"
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:
#     Takso ootab.
#     Kus sa elad?

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-et")
print(pipe(">>est<< A cab is waiting."))

# expected output: Takso ootab.

📚 Documentation

Model Info

Benchmarks

• Test Set Translations: opusTCv20210807+bt_transformer-big_2022-03-13.test.txt
• Test Set Scores: 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

• Transformers Version: 4.16.2
• OPUS-MT Git Hash: 3405783
• Port Time: Wed Apr 13 17:00:19 EEST 2022
• Port Machine: LM0-400-22516.local

📄 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.