Opus Mt Tc Big El En

🚀 opus-mt-tc-big-el-en

A neural machine translation model designed to translate from Modern Greek (1453-) (el) to English (en).

This model is part of the OPUS-MT project, an initiative aimed at making neural machine translation models widely available and accessible for numerous languages worldwide. All models are initially trained using the remarkable framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is sourced from OPUS, and 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

• This is a neural machine translation model for translating from Modern Greek (1453-) (el) to English (en).
• It is part of the OPUS - MT project, which aims to make NMT models widely available.
• Trained using the Marian NMT framework and converted to pyTorch with the transformers library.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Το σχολείο μας έχει εννιά τάξεις.",
    "Άρχισε να τρέχει."
]

model_name = "pytorch-models/opus-mt-tc-big-el-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:
#     Our school has nine classes.
#     He started running.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-el-en")
print(pipe("Το σχολείο μας έχει εννιά τάξεις."))

# expected output: Our school has nine classes.

📚 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

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.

Model Conversion Info

🔧 Technical Details

No technical details (more than 50 - word specific technical descriptions) are provided in the original document, so this section is skipped.

📄 License

The model is licensed under cc - by - 4.0.