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-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](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",
      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 n