Opus Mt Tc Big En Lt

🚀 opus-mt-tc-big-en-lt

A neural machine translation model designed for translating from English (en) to Lithuanian (lt). This model is part of the OPUS-MT project, aiming to make neural machine translation models accessible for numerous languages worldwide.

🚀 Quick Start

This model is part of the OPUS-MT project, an initiative to make neural machine translation models widely available for many languages globally. All models are initially trained using the Marian NMT framework, an efficient NMT implementation in pure C++. The models are then 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

• Supports English to Lithuanian translation.
• Part of the OPUS - MT project with a wide - reaching language support initiative.
• Trained using the efficient Marian NMT framework and converted to pyTorch.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "A cat was sitting on the chair.",
    "Yukiko likes potatoes."
]

model_name = "pytorch-models/opus-mt-tc-big-en-lt"
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:
#     Katė sėdėjo ant kėdės.
#     Jukiko mėgsta bulves.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lt")
print(pipe("A cat was sitting on the chair."))

# expected output: Katė sėdėjo ant kėdės.

📚 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

📄 License

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