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 *

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|