Opus Mt Tc Big Sh En

🚀 opus-mt-tc-big-sh-en

A neural machine translation model designed to translate from Serbo - Croatian (sh) to English (en), offering a practical solution for cross - language communication.

🚀 Quick Start

This model is part of the OPUS - MT project, which aims to make neural machine translation models widely available and accessible for many languages around the world. All models are initially trained using the Marian NMT framework, an efficient NMT implementation written in pure C++. The models are then 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

• Supports multiple source languages including bos_Latn, hrv, srp_Cyrl, srp_Latn, and targets English.
• Based on the transformer - big architecture, providing high - quality translation results.
• Utilizes data from opusTCv20210807+bt for training, ensuring a rich and diverse language corpus.

📦 Installation

This model can be used via the transformers library. You can install it using pip:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Ispostavilo se da je istina.",
    "Ovaj vikend imamo besplatne pozive."
]

model_name = "pytorch-models/opus-mt-tc-big-sh-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:
#     Turns out it's true.
#     We got free calls this weekend.

Advanced Usage

You can also use OPUS - MT models with the transformers pipelines:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-sh-en")
print(pipe("Ispostavilo se da je istina."))

# expected output: Turns out it's true.

📚 Documentation

Model info

Benchmarks

• Test set translations: opusTCv20210807+bt_transformer - big_2022 - 02 - 25.test.txt
• Test set scores: opusTCv20210807+bt_transformer - big_2022 - 02 - 25.eval.txt
• Benchmark results: benchmark_results.txt
• Benchmark output: benchmark_translations.zip

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

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