language:

• da
• en
• fo
• gmq
• is
• nb
• nn
• false
• sv tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-en-gmq results:
  • task: name: Translation eng-dan type: translation args: eng-dan dataset: name: flores101-devtest type: flores_101 args: eng dan devtest metrics:
    • name: BLEU type: bleu value: 47.7
  • task: name: Translation eng-isl type: translation args: eng-isl dataset: name: flores101-devtest type: flores_101 args: eng isl devtest metrics:
    • name: BLEU type: bleu value: 24.1
  • task: name: Translation eng-nob type: translation args: eng-nob dataset: name: flores101-devtest type: flores_101 args: eng nob devtest metrics:
    • name: BLEU type: bleu value: 34.5
  • task: name: Translation eng-swe type: translation args: eng-swe dataset: name: flores101-devtest type: flores_101 args: eng swe devtest metrics:
    • name: BLEU type: bleu value: 46.9
  • task: name: Translation eng-isl type: translation args: eng-isl dataset: name: newsdev2021.en-is type: newsdev2021.en-is args: eng-isl metrics:
    • name: BLEU type: bleu value: 22.6
  • task: name: Translation eng-dan type: translation args: eng-dan dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-dan metrics:
    • name: BLEU type: bleu value: 61.6
  • task: name: Translation eng-isl type: translation args: eng-isl dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-isl metrics:
    • name: BLEU type: bleu value: 39.9
  • task: name: Translation eng-nno type: translation args: eng-nno dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-nno metrics:
    • name: BLEU type: bleu value: 40.1
  • task: name: Translation eng-nob type: translation args: eng-nob dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-nob metrics:
    • name: BLEU type: bleu value: 57.3
  • task: name: Translation eng-swe type: translation args: eng-swe dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-swe metrics:
    • name: BLEU type: bleu value: 60.9
  • task: name: Translation eng-isl type: translation args: eng-isl dataset: name: newstest2021.en-is type: wmt-2021-news args: eng-isl metrics:
    • name: BLEU type: bleu value: 21.5

opus-mt-tc-big-en-gmq

Neural machine translation model for translating from English (en) to North Germanic languages (gmq).

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: 2022-03-17
• source language(s): eng
• target language(s): dan fao isl nno nob nor swe
• valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
• model: transformer-big
• data: opusTCv20210807+bt (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807+bt_transformer-big_2022-03-17.zip
• more information released models: OPUS-MT eng-gmq README
• more information about the model: MarianMT

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. >>dan<<

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>nno<< The United States borders Canada.",
    ">>nob<< This is the biggest hotel in this city."
]

model_name = "pytorch-models/opus-mt-tc-big-en-gmq"
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:
#     USA grensar til Canada.
#     Dette er det største hotellet i denne byen.

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-gmq")
print(pipe(">>nno<< The United States borders Canada."))

# expected output: USA grensar til Canada.

Benchmarks

• test set translations: opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
• test set scores: opusTCv20210807+bt_transformer-big_2022-03-17.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 17:14:46 EEST 2022
• port machine: LM0-400-22516.local