Opus Mt Tc Big Tr En

🚀 opus-mt-tc-big-tr-en

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

🚀 Quick Start

This model is a neural machine translation solution for Turkish to English translation. It's part of the OPUS - MT project, leveraging the Marian NMT framework and converted to pyTorch using the transformers library by huggingface.

✨ Features

• Multilingual Support: Supports Turkish to English translation.
• Open - Source: Part of an open - source project, making it accessible to the community.
• Benchmarked Performance: Demonstrates performance on multiple datasets with BLEU scores provided.

📚 Documentation

Model Info

Publications

Please cite the following papers if you use this model:

• OPUS - MT – Building open translation services for the World
• The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT

@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",
}

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Allahsızlığı Yayma Kürsüsü başkanıydı.",
    "Tom'a ne olduğunu öğrenin."
]

model_name = "pytorch-models/opus-mt-tc-big-tr-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:
#     He was the president of the Curse of Spreading Godlessness.
#     Find out what happened to Tom.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-tr-en")
print(pipe("Allahsızlığı Yayma Kürsüsü başkanıydı."))

# expected output: He was the president of the Curse of Spreading Godlessness.

🔧 Technical Details

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

Model Conversion Info

• Transformers Version: 4.16.2
• OPUS - MT Git Hash: 3405783
• Port Time: Wed Apr 13 20:02:48 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.