Opus Mt Tc Big De Es

🚀 opus-mt-tc-big-de-es

This is a neural machine translation model designed to translate from German (de) to Spanish (es), which is part of the OPUS - MT project, making neural machine translation models more accessible.

🚀 Quick Start

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Ich verstehe nicht, worüber ihr redet.",
    "Die Vögel singen in den Bäumen."
]

model_name = "pytorch-models/opus-mt-tc-big-de-es"
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:
#     No entiendo de qué están hablando.
#     Los pájaros cantan en los árboles.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-de-es")
print(pipe("Ich verstehe nicht, worüber ihr redet."))

# expected output: No entiendo de qué están hablando.

✨ Features

This model can be used for translation and text - to - text generation.

📦 Installation

No installation steps provided in the original document.

📚 Documentation

Model Details

Neural machine translation model for translating from German (de) to Spanish (es).

This model is part of the OPUS - MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS - MT - train.

Model Description:

• Developed by: Language Technology Research Group at the University of Helsinki
• Model Type: Translation (transformer - big)
• Release: 2022 - 07 - 26
• License: CC - BY - 4.0
• Language(s):
  • Source Language(s): deu
  • Target Language(s): spa
  • Language Pair(s): deu - spa
  • Valid Target Language Labels:
• Original Model: opusTCv20210807_transformer - big_2022 - 07 - 26.zip
• Resources for more information:
  • OPUS - MT - train GitHub Repo
  • More information about released models for this language pair: OPUS - MT deu - spa README
  • More information about MarianNMT models in the transformers library
  • [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/

Training

• Data: opusTCv20210807 (source)
• Pre - processing: SentencePiece (spm32k,spm32k)
• Model Type: transformer - big
• Original MarianNMT Model: opusTCv20210807_transformer - big_2022 - 07 - 26.zip
• Training Scripts: GitHub Repo

Evaluation

• test set translations: opusTCv20210807_transformer - big_2022 - 07 - 26.test.txt
• test set scores: opusTCv20210807_transformer - big_2022 - 07 - 26.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

Citation Information

• 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",
}

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

• transformers version: 4.16.2
• OPUS - MT git hash: 8b9f0b0
• port time: Sat Aug 13 00:06:19 EEST 2022
• port machine: LM0 - 400 - 22516.local

🔧 Technical Details

No technical details provided in the original document.

📄 License

The model is under the CC - BY - 4.0 license.

⚠️ Important Note

Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

💡 Usage Tip

If you use this model, please cite the relevant 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/).