Opus Mt Tc Big Ar En

🚀 opus-mt-tc-big-ar-en

A neural machine translation model designed for translating from Arabic (ar) 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 outstanding 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",
}

🚀 Quick Start

This is a neural machine translation model for translating from Arabic to English. You can quickly start using it through the following steps.

✨ Features

• Multilingual Support: Supports translation from Arabic to English.
• Open Source Project: Part of the OPUS - MT project, with open - source training data and pipelines.
• High - performance Framework: Trained using the Marian NMT framework and converted to pyTorch.

📦 Installation

The README does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "اتبع قلبك فحسب.",
    "وين راهي دّوش؟"
]

model_name = "pytorch-models/opus-mt-tc-big-ar-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:
#     Just follow your heart.
#     Wayne Rahi Dosh?

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-ar-en")
print(pipe("اتبع قلبك فحسب."))

# expected output: Just follow your heart.

📚 Documentation

Model Info

Benchmarks

• test set translations: opusTCv20210807+bt_transformer-big_2022-03-09.test.txt
• test set scores: opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

Model Conversion Info

🔧 Technical Details

The README does not provide specific technical details, so this section is skipped.

📄 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.