readme: ---
  language:
  - en
  - de
  tags:
  - translation
  - opus-mt
  license: cc-by-4.0
  model-index:
  - name: opus-mt-tc-big-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: 45.7	
  ---
  
  # 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+bt-2021-04-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus+bt-2021-04-13.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-align
  * source language code: en
  * target language code: de
  * dataset: opus+bt 
  * release date: 2021-02-22
  * pre-processing: normalization + SentencePiece (spm32k,spm32k)
  * download original weights: [opus+bt-2021-04-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus+bt-2021-04-13.zip)
  * Test set translations data: [opus+bt-2021-04-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus+bt-2021-04-13.test.txt)
  * test set scores file: [opus+bt-2021-04-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-deu/opus+bt-2021-04-13.eval.txt)
  * Benchmarks
  |Test set|BLEU|chr-F|
  |---|---|---|
  |newssyscomb2009.eng-deu|22.8|0.538|
  |news-test2008.eng-deu|23.7|0.533|	
  |newstest2009.eng-deu|22.6|0.532|	
  |newstest2010.eng-deu|25.5|0.552|	
  |newstest2011.eng-deu|22.6|0.527|	
  |newstest2012.eng-deu|23.4|0.530|	
  |newstest2013.eng-deu|27.1|0.556|	
  |newstest2014-deen.eng-deu|29.6|0.599|	
  |newstest2015-ende.eng-deu|31.6|0.600|	
  |newstest2016-ende.eng-deu|37.2|0.644|	
  |newstest2017-ende.eng-deu|30.6|0.595|	
  |newstest2018-ende.eng-deu|45.6|0.696|	
  |newstest2019-ende.eng-deu|41.3|0.659|	
  |Tatoeba-test.eng-deu|45.7|0.654|	
  