readme: ---
  language:
  - en
  - de
  tags:
  - translation
  - opus-mt
  license: cc-by-4.0
  model-index:
  - name: opus-mt-eng-deu
    results:
    - task:
        name: Translation eng-deu
        type: translation
        args: eng-deu
      dataset:
        name: Tatoeba-test.eng-deu
        type: tatoeba_mt
        args: eng-deu
      metrics:
      - name: BLEU
        type: bleu
        value: 45.8
  ---
  
  # Opus Tatoeba English-German
  
  *This model was obtained by running the script [convert_marian_to_pytorch.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/marian/convert_marian_to_pytorch.py) - [Instruction available here](https://github.com/huggingface/transformers/tree/main/scripts/tatoeba). The original models were trained by [Jörg Tiedemann](https://blogs.helsinki.fi/tiedeman/) using the [MarianNMT](https://marian-nmt.github.io/) library. See all available `MarianMTModel` models on the profile of the [Helsinki NLP](https://huggingface.co/Helsinki-NLP) group.
  
  This is the conversion of checkpoint [opus-2021-02-22.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus-2021-02-22.zip/eng-deu/opus-2021-02-22.zip)
  *
  
  
  ---
  
  ### eng-deu
  
  * source language name: English
  * target language name: German
  * OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/README.md)
  
  * model: transformer
  * source language code: en
  * target language code: de
  * dataset: opus 
  * release date: 2021-02-22
  * pre-processing: normalization + SentencePiece (spm32k,spm32k)
  * download original weights: [opus-2021-02-22.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus-2021-02-22.zip/eng-deu/opus-2021-02-22.zip)
  * Training data: 
    * deu-eng: Tatoeba-train (86845165)
  * Validation data: 
    * deu-eng: Tatoeba-dev, 284809
    * total-size-shuffled: 284809
    * devset-selected: top 5000  lines of Tatoeba-dev.src.shuffled!
  * Test data: 
    * newssyscomb2009.eng-deu: 502/11271
    * news-test2008.eng-deu: 2051/47427
    * newstest2009.eng-deu: 2525/62816
    * newstest2010.eng-deu: 2489/61511
    * newstest2011.eng-deu: 3003/72981
    * newstest2012.eng-deu: 3003/72886
    * newstest2013.eng-deu: 3000/63737
    * newstest2014-deen.eng-deu: 3003/62964
    * newstest2015-ende.eng-deu: 2169/44260
    * newstest2016-ende.eng-deu: 2999/62670
    * newstest2017-ende.eng-deu: 3004/61291
    * newstest2018-ende.eng-deu: 2998/64276
    * newstest2019-ende.eng-deu: 1997/48969
    * Tatoeba-test.eng-deu: 10000/83347
  * test set translations file: [test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus-2021-02-22.zip/eng-deu/opus-2021-02-22.test.txt)
  * test set scores file: [eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus-2021-02-22.zip/eng-deu/opus-2021-02-22.eval.txt)
  * BLEU-scores
  |Test set|score|
  |---|---|
  |newstest2018-ende.eng-deu|46.4|
  |Tatoeba-test.eng-deu|45.8|
  |newstest2019-ende.eng-deu|42.4|
  |newstest2016-ende.eng-deu|37.9|
  |newstest2015-ende.eng-deu|32.0|
  |newstest2017-ende.eng-deu|30.6|
  |newstest2014-deen.eng-deu|29.6|
  |newstest2013.eng-deu|27.6|
  |newstest2010.eng-deu|25.9|
  |news-test2008.eng-deu|23.9|
  |newstest2012.eng-deu|23.8|
  |newssyscomb2009.eng-deu|23.3|
  |newstest2011.eng-deu|22.9|
  |newstest2009.eng-deu|22.7|
  * chr-F-scores
  |Test set|score|
  |---|---|
  |newstest2018-ende.eng-deu|0.697|
  |newstest2019-ende.eng-deu|0.664|
  |Tatoeba-test.eng-deu|0.655|
  |newstest2016-ende.eng-deu|0.644|
  |newstest2015-ende.eng-deu|0.601|
  |newstest2014-deen.eng-deu|0.595|
  |newstest2017-ende.eng-deu|0.593|
  |newstest2013.eng-deu|0.558|
  |newstest2010.eng-deu|0.55|
  |newssyscomb2009.eng-deu|0.539|
  |news-test2008.eng-deu|0.533|
  |newstest2009.eng-deu|0.533|
  |newstest2012.eng-deu|0.53|
  |newstest2011.eng-deu|0.528|