language:

• en
• ro tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-en-ro results:
  • task: name: Translation eng-ron type: translation args: eng-ron dataset: name: flores101-devtest type: flores_101 args: eng ron devtest metrics:
    • name: BLEU type: bleu value: 40.4
  • task: name: Translation eng-ron type: translation args: eng-ron dataset: name: newsdev2016 type: newsdev2016 args: eng-ron metrics:
    • name: BLEU type: bleu value: 36.4
  • task: name: Translation eng-ron type: translation args: eng-ron dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-ron metrics:
    • name: BLEU type: bleu value: 48.6
  • task: name: Translation eng-ron type: translation args: eng-ron dataset: name: newstest2016 type: wmt-2016-news args: eng-ron metrics:
    • name: BLEU type: bleu value: 34.0

opus-mt-tc-big-en-ro

Neural machine translation model for translating from English (en) to Romanian (ro).

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.

• 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",
}

Model info

• Release: 2022-02-25
• source language(s): eng
• target language(s): ron
• model: transformer-big
• data: opusTCv20210807+bt (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807+bt_transformer-big_2022-02-25.zip
• more information released models: OPUS-MT eng-ron README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>ron<< A bad writer's prose is full of hackneyed phrases.",
    ">>ron<< Zero is a special number."
]

model_name = "pytorch-models/opus-mt-tc-big-en-ro"
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:
#     Proza unui scriitor prost este plină de fraze tocite.
#     Zero este un număr special.

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

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ro")
print(pipe(">>ron<< A bad writer's prose is full of hackneyed phrases."))

# expected output: Proza unui scriitor prost este plină de fraze tocite.

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 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

• transformers version: 4.16.2
• OPUS-MT git hash: 3405783
• port time: Wed Apr 13 17:55:46 EEST 2022
• port machine: LM0-400-22516.local