Opus Mt Tc Big Fi En

🚀 opus-mt-tc-big-fi-en

A neural machine translation model designed to translate text from Finnish (fi) to English (en). This model is part of a broader initiative to make high - quality translation accessible across multiple languages.

🚀 Quick Start

This model is a neural machine translation solution for Finnish to English translation. It's part of the [OPUS - MT project](https://github.com/Helsinki - NLP/Opus - MT), aiming to provide accessible NMT models for various languages. The models are initially trained with [Marian NMT](https://marian - nmt.github.io/), a C++ - based NMT framework, and then converted to pyTorch using the Hugging Face transformers library. The training data comes from OPUS, and the training pipelines follow the procedures of [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train).

• Publications: [OPUS - MT – Building open translation services for the World](https://aclanthology.org/2020.eamt - 1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt - 1.139/) (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 Initiative: Part of the OPUS - MT project, offering translation solutions for a wide range of languages.
• Efficient Training: Trained with the Marian NMT framework, known for its efficiency and written in pure C++.
• PyTorch Compatibility: Converted to PyTorch using the Hugging Face transformers library, facilitating easy integration.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Kolme kolmanteen on kaksikymmentäseitsemän.",
    "Heille syntyi poikavauva."
]

model_name = "pytorch - models/opus - mt - tc - big - fi - 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) )

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki - NLP/opus - mt - tc - big - fi - en")
print(pipe("Kolme kolmanteen on kaksikymmentäseitsemän."))

📚 Documentation

Model info

Benchmarks

Acknowledgements

The work is supported by the [European Language Grid](https://www.european - language - grid.eu/) as [pilot project 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding), 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.