Edit model card
YAML Metadata Error: "language[7]" must be a string

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.

@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

langpair testset chr-F BLEU #sent #words
eng-dan tatoeba-test-v2021-08-07 0.75165 61.6 10795 79385
eng-fao tatoeba-test-v2021-08-07 0.40395 18.3 294 1933
eng-isl tatoeba-test-v2021-08-07 0.59731 39.9 2503 19023
eng-nno tatoeba-test-v2021-08-07 0.61271 40.1 460 3428
eng-nob tatoeba-test-v2021-08-07 0.72380 57.3 4539 36119
eng-swe tatoeba-test-v2021-08-07 0.74197 60.9 10362 68067
eng-dan flores101-devtest 0.70810 47.7 1012 24638
eng-isl flores101-devtest 0.52076 24.1 1012 22834
eng-nob flores101-devtest 0.62760 34.5 1012 23873
eng-swe flores101-devtest 0.70129 46.9 1012 23121
eng-isl newsdev2021.en-is 0.50376 22.6 2004 43721
eng-isl newstest2021.en-is 0.50516 21.5 1000 25233

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
Downloads last month
1,093
Safetensors
Model size
232M params
Tensor type
FP16
·

Spaces using Helsinki-NLP/opus-mt-tc-big-en-gmq 2

Evaluation results