teelinsan/opus-mt-eng-deu

Opus Tatoeba English-German

*This model was obtained by running the script convert_marian_to_pytorch.py - Instruction available here. The original models were trained by J�rg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group.

This is the conversion of checkpoint opus-2021-02-22.zip *

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eng-deu

• source language name: English

• target language name: German

• OPUS readme: 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

• 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

• test set scores file: 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