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opus-mt-tc-big-gmq-en

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

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.

@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

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Han var synligt nervøs.",
    "Inte ens Tom själv var övertygad."
]

model_name = "pytorch-models/opus-mt-tc-big-gmq-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:
#     He was visibly nervous.
#     Even Tom was not convinced.

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-gmq-en")
print(pipe("Han var synligt nervøs."))

# expected output: He was visibly nervous.

Benchmarks

langpair testset chr-F BLEU #sent #words
dan-eng tatoeba-test-v2021-08-07 0.78292 65.9 10795 79684
fao-eng tatoeba-test-v2021-08-07 0.47467 30.1 294 1984
isl-eng tatoeba-test-v2021-08-07 0.68346 53.3 2503 19788
nno-eng tatoeba-test-v2021-08-07 0.69788 56.1 460 3524
nob-eng tatoeba-test-v2021-08-07 0.73524 60.2 4539 36823
swe-eng tatoeba-test-v2021-08-07 0.77665 66.4 10362 68513
dan-eng flores101-devtest 0.72322 49.3 1012 24721
isl-eng flores101-devtest 0.59616 34.2 1012 24721
nob-eng flores101-devtest 0.68224 44.2 1012 24721
swe-eng flores101-devtest 0.72042 49.8 1012 24721
isl-eng newsdev2021.is-en 0.56709 30.4 2004 46383
isl-eng newstest2021.is-en 0.57756 34.4 1000 22529

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 19:13:11 EEST 2022
  • port machine: LM0-400-22516.local
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