---
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|