Opus Mt Tc Big En Cat Oci Spa

🚀 opus-mt-tc-big-en-cat_oci_spa

A neural machine translation model designed to translate from English (en) to Catalan, Occitan, and Spanish (cat+oci+spa).

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 globally. All models are initially trained using the remarkable framework of Marian NMT, an efficient NMT implementation written in pure C++. These models have been converted to pyTorch using the transformers library by huggingface. The training data is sourced from OPUS, and the 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

• Multilingual Support: Capable of translating from English to Catalan, Occitan, and Spanish.
• Part of OPUS-MT Project: Leverages the resources and framework of the OPUS-MT initiative.
• Converted to pyTorch: Using the transformers library by huggingface for broader compatibility.

📦 Installation

The provided README does not contain installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>spa<< Why do you want Tom to go there with me?",
    ">>spa<< She forced him to eat spinach."
]

model_name = "pytorch-models/opus-mt-tc-big-en-cat_oci_spa"
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:
#     ¿Por qué quieres que Tom vaya conmigo?
#     Ella lo obligó a comer espinacas.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa")
print(pipe(">>spa<< Why do you want Tom to go there with me?"))

# expected output: ¿Por qué quieres que Tom vaya conmigo?

📚 Documentation

Model info

This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of >>id<< (id = valid target language ID), e.g., >>cat<<

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 project is licensed under the cc-by-4.0 license.