readme: ---
  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-gmq-en
    results:
    - task:
        name: Translation dan-eng
        type: translation
        args: dan-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: dan eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 49.3
    - task:
        name: Translation isl-eng
        type: translation
        args: isl-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: isl eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 34.2
    - task:
        name: Translation nob-eng
        type: translation
        args: nob-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: nob eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 44.2
    - task:
        name: Translation swe-eng
        type: translation
        args: swe-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: swe eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 49.8
    - task:
        name: Translation isl-eng
        type: translation
        args: isl-eng
      dataset:
        name: newsdev2021.is-en
        type: newsdev2021.is-en
        args: isl-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 30.4
    - task:
        name: Translation dan-eng
        type: translation
        args: dan-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: dan-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 65.9
    - task:
        name: Translation fao-eng
        type: translation
        args: fao-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: fao-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 30.1
    - task:
        name: Translation isl-eng
        type: translation
        args: isl-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: isl-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 53.3
    - task:
        name: Translation nno-eng
        type: translation
        args: nno-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: nno-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 56.1
    - task:
        name: Translation nob-eng
        type: translation
        args: nob-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: nob-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 60.2
    - task:
        name: Translation swe-eng
        type: translation
        args: swe-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: swe-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 66.4
    - task:
        name: Translation isl-eng
        type: translation
        args: isl-eng
      dataset:
        name: newstest2021.is-en
        type: wmt-2021-news
        args: isl-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 34.4
  ---
  # 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](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
  
  * 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",
   