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--- |
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language: |
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- ces |
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- slk |
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- cs |
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- sk |
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- en |
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tags: |
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- translation |
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- opus-mt-tc |
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-big-en-ces_slk |
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results: |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: eng ces devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 34.1 |
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- task: |
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name: Translation eng-slk |
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type: translation |
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args: eng-slk |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: eng slk devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 35.9 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: multi30k_test_2016_flickr |
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type: multi30k-2016_flickr |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 33.4 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: multi30k_test_2018_flickr |
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type: multi30k-2018_flickr |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 33.4 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: news-test2008 |
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type: news-test2008 |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.8 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 47.5 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2009 |
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type: wmt-2009-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.3 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2010 |
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type: wmt-2010-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.4 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2011 |
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type: wmt-2011-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 25.5 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2012 |
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type: wmt-2012-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.6 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2013 |
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type: wmt-2013-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 27.4 |
|
- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2014 |
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type: wmt-2014-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 31.4 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2015 |
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type: wmt-2015-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 27.0 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2016 |
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type: wmt-2016-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 29.9 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2017 |
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type: wmt-2017-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.9 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2018 |
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type: wmt-2018-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.6 |
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- task: |
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name: Translation eng-ces |
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type: translation |
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args: eng-ces |
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dataset: |
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name: newstest2019 |
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type: wmt-2019-news |
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args: eng-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 26.4 |
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--- |
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# opus-mt-tc-big-en-ces_slk |
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Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk). |
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This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). |
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* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) |
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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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## Model info |
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* Release: 2022-03-13 |
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* source language(s): eng |
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* target language(s): ces |
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* model: transformer-big |
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* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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* tokenization: SentencePiece (spm32k,spm32k) |
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* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.zip) |
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* more information released models: [OPUS-MT eng-ces+slk README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ces+slk/README.md) |
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## Usage |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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">>ces<< We were enemies.", |
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">>ces<< Do you think Tom knows what's going on?" |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-en-ces_slk" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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# expected output: |
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# Byli jsme nepřátelé. |
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# Myslíš, že Tom ví, co se děje? |
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``` |
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ces_slk") |
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print(pipe(">>ces<< We were enemies.")) |
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# expected output: Byli jsme nepřátelé. |
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``` |
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## Benchmarks |
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) |
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* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| eng-ces | tatoeba-test-v2021-08-07 | 0.66128 | 47.5 | 13824 | 91332 | |
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| eng-ces | flores101-devtest | 0.60411 | 34.1 | 1012 | 22101 | |
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| eng-slk | flores101-devtest | 0.62415 | 35.9 | 1012 | 22543 | |
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| eng-ces | multi30k_test_2016_flickr | 0.58547 | 33.4 | 1000 | 10503 | |
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| eng-ces | multi30k_test_2018_flickr | 0.59236 | 33.4 | 1071 | 11631 | |
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| eng-ces | newssyscomb2009 | 0.52702 | 25.3 | 502 | 10032 | |
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| eng-ces | news-test2008 | 0.50286 | 22.8 | 2051 | 42484 | |
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| eng-ces | newstest2009 | 0.52152 | 24.3 | 2525 | 55533 | |
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| eng-ces | newstest2010 | 0.52527 | 24.4 | 2489 | 52955 | |
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| eng-ces | newstest2011 | 0.52721 | 25.5 | 3003 | 65653 | |
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| eng-ces | newstest2012 | 0.50007 | 22.6 | 3003 | 65456 | |
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| eng-ces | newstest2013 | 0.53643 | 27.4 | 3000 | 57250 | |
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| eng-ces | newstest2014 | 0.58944 | 31.4 | 3003 | 59902 | |
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| eng-ces | newstest2015 | 0.55094 | 27.0 | 2656 | 45858 | |
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| eng-ces | newstest2016 | 0.56864 | 29.9 | 2999 | 56998 | |
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| eng-ces | newstest2017 | 0.52504 | 24.9 | 3005 | 54361 | |
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| eng-ces | newstest2018 | 0.52490 | 24.6 | 2983 | 54652 | |
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| eng-ces | newstest2019 | 0.53994 | 26.4 | 1997 | 43113 | |
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## Acknowledgements |
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The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. |
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## Model conversion info |
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 3405783 |
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* port time: Wed Apr 13 16:46:48 EEST 2022 |
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* port machine: LM0-400-22516.local |
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