Opus Mt Tc Big En Tr

🚀 opus-mt-tc-big-en-tr

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

🚀 Quick Start

This model is a neural machine translation solution for English to Turkish translation. It's part of the OPUS - MT project, leveraging the Marian NMT framework and trained on data from OPUS.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "I know Tom didn't want to eat that.",
    "On Sundays, we would get up early and go fishing."
]

model_name = "pytorch-models/opus-mt-tc-big-en-tr"
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:
#     Tom'un bunu yemek istemediğini biliyorum.
#     Pazar günleri erkenden kalkıp balık tutmaya giderdik.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-tr")
print(pipe("I know Tom didn't want to eat that."))

# expected output: Tom'un bunu yemek istemediğini biliyorum.

✨ Features

• Multilingual Support: Capable of translating from English to Turkish.
• Open - Source Project: Part of the OPUS - MT project, promoting wide availability of NMT models.
• Efficient Training: Trained using the Marian NMT framework, written in pure C++.

📦 Installation

The installation process is mainly about setting up the necessary Python libraries. You can install the transformers library via pip:

pip install transformers

📚 Documentation

Model info

Benchmarks

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

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

Publications

Please cite the following papers 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",
}