--- language: - af - de - en - fy - gos - lb - nds - nl tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-gmw-gmw results: - task: name: Translation afr-deu type: translation args: afr-deu dataset: name: flores101-devtest type: flores_101 args: afr deu devtest metrics: - name: BLEU type: bleu value: 30.2 - name: chr-F type: chrf value: 0.58718 - task: name: Translation afr-eng type: translation args: afr-eng dataset: name: flores101-devtest type: flores_101 args: afr eng devtest metrics: - name: BLEU type: bleu value: 55.1 - name: chr-F type: chrf value: 0.74826 - task: name: Translation afr-ltz type: translation args: afr-ltz dataset: name: flores101-devtest type: flores_101 args: afr ltz devtest metrics: - name: BLEU type: bleu value: 15.7 - name: chr-F type: chrf value: 0.46826 - task: name: Translation afr-nld type: translation args: afr-nld dataset: name: flores101-devtest type: flores_101 args: afr nld devtest metrics: - name: BLEU type: bleu value: 22.5 - name: chr-F type: chrf value: 0.54441 - task: name: Translation deu-afr type: translation args: deu-afr dataset: name: flores101-devtest type: flores_101 args: deu afr devtest metrics: - name: BLEU type: bleu value: 26.4 - name: chr-F type: chrf value: 0.57835 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: flores101-devtest type: flores_101 args: deu eng devtest metrics: - name: BLEU type: bleu value: 41.8 - name: chr-F type: chrf value: 0.66990 - task: name: Translation deu-ltz type: translation args: deu-ltz dataset: name: flores101-devtest type: flores_101 args: deu ltz devtest metrics: - name: BLEU type: bleu value: 20.3 - name: chr-F type: chrf value: 0.52554 - task: name: Translation deu-nld type: translation args: deu-nld dataset: name: flores101-devtest type: flores_101 args: deu nld devtest metrics: - name: BLEU type: bleu value: 24.2 - name: chr-F type: chrf value: 0.55710 - task: name: Translation eng-afr type: translation args: eng-afr dataset: name: flores101-devtest type: flores_101 args: eng afr devtest metrics: - name: BLEU type: bleu value: 40.7 - name: chr-F type: chrf value: 0.68429 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: flores101-devtest type: flores_101 args: eng deu devtest metrics: - name: BLEU type: bleu value: 38.5 - name: chr-F type: chrf value: 0.64888 - task: name: Translation eng-ltz type: translation args: eng-ltz dataset: name: flores101-devtest type: flores_101 args: eng ltz devtest metrics: - name: BLEU type: bleu value: 18.4 - name: chr-F type: chrf value: 0.49231 - task: name: Translation eng-nld type: translation args: eng-nld dataset: name: flores101-devtest type: flores_101 args: eng nld devtest metrics: - name: BLEU type: bleu value: 26.8 - name: chr-F type: chrf value: 0.57984 - task: name: Translation ltz-afr type: translation args: ltz-afr dataset: name: flores101-devtest type: flores_101 args: ltz afr devtest metrics: - name: BLEU type: bleu value: 23.2 - name: chr-F type: chrf value: 0.53623 - task: name: Translation ltz-deu type: translation args: ltz-deu dataset: name: flores101-devtest type: flores_101 args: ltz deu devtest metrics: - name: BLEU type: bleu value: 30.0 - name: chr-F type: chrf value: 0.59122 - task: name: Translation ltz-eng type: translation args: ltz-eng dataset: name: flores101-devtest type: flores_101 args: ltz eng devtest metrics: - name: BLEU type: bleu value: 31.0 - name: chr-F type: chrf value: 0.57557 - task: name: Translation ltz-nld type: translation args: ltz-nld dataset: name: flores101-devtest type: flores_101 args: ltz nld devtest metrics: - name: BLEU type: bleu value: 18.6 - name: chr-F type: chrf value: 0.49312 - task: name: Translation nld-afr type: translation args: nld-afr dataset: name: flores101-devtest type: flores_101 args: nld afr devtest metrics: - name: BLEU type: bleu value: 20.0 - name: chr-F type: chrf value: 0.52409 - task: name: Translation nld-deu type: translation args: nld-deu dataset: name: flores101-devtest type: flores_101 args: nld deu devtest metrics: - name: BLEU type: bleu value: 22.6 - name: chr-F type: chrf value: 0.53898 - task: name: Translation nld-eng type: translation args: nld-eng dataset: name: flores101-devtest type: flores_101 args: nld eng devtest metrics: - name: BLEU type: bleu value: 30.7 - name: chr-F type: chrf value: 0.58970 - task: name: Translation nld-ltz type: translation args: nld-ltz dataset: name: flores101-devtest type: flores_101 args: nld ltz devtest metrics: - name: BLEU type: bleu value: 11.8 - name: chr-F type: chrf value: 0.42637 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: deu-eng metrics: - name: BLEU type: bleu value: 39.9 - name: chr-F type: chrf value: 0.60928 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: eng-deu metrics: - name: BLEU type: bleu value: 35.4 - name: chr-F type: chrf value: 0.64172 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: multi30k_test_2017_flickr type: multi30k-2017_flickr args: deu-eng metrics: - name: BLEU type: bleu value: 40.5 - name: chr-F type: chrf value: 0.63154 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: multi30k_test_2017_flickr type: multi30k-2017_flickr args: eng-deu metrics: - name: BLEU type: bleu value: 34.2 - name: chr-F type: chrf value: 0.63078 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: multi30k_test_2017_mscoco type: multi30k-2017_mscoco args: deu-eng metrics: - name: BLEU type: bleu value: 32.2 - name: chr-F type: chrf value: 0.55708 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: multi30k_test_2017_mscoco type: multi30k-2017_mscoco args: eng-deu metrics: - name: BLEU type: bleu value: 29.1 - name: chr-F type: chrf value: 0.57537 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: deu-eng metrics: - name: BLEU type: bleu value: 36.9 - name: chr-F type: chrf value: 0.59422 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: eng-deu metrics: - name: BLEU type: bleu value: 30.0 - name: chr-F type: chrf value: 0.59597 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: news-test2008 type: news-test2008 args: deu-eng metrics: - name: BLEU type: bleu value: 27.2 - name: chr-F type: chrf value: 0.54601 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: news-test2008 type: news-test2008 args: eng-deu metrics: - name: BLEU type: bleu value: 23.6 - name: chr-F type: chrf value: 0.53149 - task: name: Translation afr-deu type: translation args: afr-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: afr-deu metrics: - name: BLEU type: bleu value: 50.4 - name: chr-F type: chrf value: 0.68679 - task: name: Translation afr-eng type: translation args: afr-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: afr-eng metrics: - name: BLEU type: bleu value: 56.6 - name: chr-F type: chrf value: 0.70682 - task: name: Translation afr-nld type: translation args: afr-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: afr-nld metrics: - name: BLEU type: bleu value: 55.5 - name: chr-F type: chrf value: 0.71516 - task: name: Translation deu-afr type: translation args: deu-afr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-afr metrics: - name: BLEU type: bleu value: 54.3 - name: chr-F type: chrf value: 0.70274 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-eng metrics: - name: BLEU type: bleu value: 48.6 - name: chr-F type: chrf value: 0.66023 - task: name: Translation deu-nds type: translation args: deu-nds dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-nds metrics: - name: BLEU type: bleu value: 23.2 - name: chr-F type: chrf value: 0.48058 - task: name: Translation deu-nld type: translation args: deu-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-nld metrics: - name: BLEU type: bleu value: 54.6 - name: chr-F type: chrf value: 0.71440 - task: name: Translation eng-afr type: translation args: eng-afr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-afr metrics: - name: BLEU type: bleu value: 56.5 - name: chr-F type: chrf value: 0.71995 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-deu metrics: - name: BLEU type: bleu value: 42.0 - name: chr-F type: chrf value: 0.63103 - task: name: Translation eng-fry type: translation args: eng-fry dataset: name: tatoeba-test-v2021-03-30 type: tatoeba_mt args: eng-fry metrics: - name: BLEU type: bleu value: 21.3 - name: chr-F type: chrf value: 0.38580 - task: name: Translation eng-nld type: translation args: eng-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-nld metrics: - name: BLEU type: bleu value: 54.5 - name: chr-F type: chrf value: 0.71062 - task: name: Translation fry-eng type: translation args: fry-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fry-eng metrics: - name: BLEU type: bleu value: 25.1 - name: chr-F type: chrf value: 0.40545 - task: name: Translation fry-nld type: translation args: fry-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fry-nld metrics: - name: BLEU type: bleu value: 41.7 - name: chr-F type: chrf value: 0.55771 - task: name: Translation gos-deu type: translation args: gos-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: gos-deu metrics: - name: BLEU type: bleu value: 25.4 - name: chr-F type: chrf value: 0.45302 - task: name: Translation gos-eng type: translation args: gos-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: gos-eng metrics: - name: BLEU type: bleu value: 24.1 - name: chr-F type: chrf value: 0.37628 - task: name: Translation gos-nld type: translation args: gos-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: gos-nld metrics: - name: BLEU type: bleu value: 26.2 - name: chr-F type: chrf value: 0.45777 - task: name: Translation ltz-deu type: translation args: ltz-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ltz-deu metrics: - name: BLEU type: bleu value: 21.3 - name: chr-F type: chrf value: 0.37165 - task: name: Translation ltz-eng type: translation args: ltz-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ltz-eng metrics: - name: BLEU type: bleu value: 30.3 - name: chr-F type: chrf value: 0.37784 - task: name: Translation ltz-nld type: translation args: ltz-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ltz-nld metrics: - name: BLEU type: bleu value: 26.7 - name: chr-F type: chrf value: 0.32823 - task: name: Translation nds-deu type: translation args: nds-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nds-deu metrics: - name: BLEU type: bleu value: 45.4 - name: chr-F type: chrf value: 0.64008 - task: name: Translation nds-eng type: translation args: nds-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nds-eng metrics: - name: BLEU type: bleu value: 38.3 - name: chr-F type: chrf value: 0.55193 - task: name: Translation nds-nld type: translation args: nds-nld dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nds-nld metrics: - name: BLEU type: bleu value: 50.0 - name: chr-F type: chrf value: 0.66943 - task: name: Translation nld-afr type: translation args: nld-afr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nld-afr metrics: - name: BLEU type: bleu value: 62.3 - name: chr-F type: chrf value: 0.76610 - task: name: Translation nld-deu type: translation args: nld-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nld-deu metrics: - name: BLEU type: bleu value: 56.8 - name: chr-F type: chrf value: 0.73162 - task: name: Translation nld-eng type: translation args: nld-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nld-eng metrics: - name: BLEU type: bleu value: 60.5 - name: chr-F type: chrf value: 0.74088 - task: name: Translation nld-fry type: translation args: nld-fry dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nld-fry metrics: - name: BLEU type: bleu value: 31.4 - name: chr-F type: chrf value: 0.48460 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2009 type: wmt-2009-news args: deu-eng metrics: - name: BLEU type: bleu value: 25.9 - name: chr-F type: chrf value: 0.53747 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2009 type: wmt-2009-news args: eng-deu metrics: - name: BLEU type: bleu value: 22.9 - name: chr-F type: chrf value: 0.53283 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2010 type: wmt-2010-news args: deu-eng metrics: - name: BLEU type: bleu value: 30.6 - name: chr-F type: chrf value: 0.58355 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2010 type: wmt-2010-news args: eng-deu metrics: - name: BLEU type: bleu value: 25.8 - name: chr-F type: chrf value: 0.54885 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2011 type: wmt-2011-news args: deu-eng metrics: - name: BLEU type: bleu value: 26.3 - name: chr-F type: chrf value: 0.54883 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2011 type: wmt-2011-news args: eng-deu metrics: - name: BLEU type: bleu value: 23.1 - name: chr-F type: chrf value: 0.52712 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2012 type: wmt-2012-news args: deu-eng metrics: - name: BLEU type: bleu value: 28.5 - name: chr-F type: chrf value: 0.56153 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2012 type: wmt-2012-news args: eng-deu metrics: - name: BLEU type: bleu value: 23.3 - name: chr-F type: chrf value: 0.52662 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2013 type: wmt-2013-news args: deu-eng metrics: - name: BLEU type: bleu value: 31.4 - name: chr-F type: chrf value: 0.57770 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2013 type: wmt-2013-news args: eng-deu metrics: - name: BLEU type: bleu value: 27.8 - name: chr-F type: chrf value: 0.55774 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2014 type: wmt-2014-news args: deu-eng metrics: - name: BLEU type: bleu value: 33.2 - name: chr-F type: chrf value: 0.59826 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2014 type: wmt-2014-news args: eng-deu metrics: - name: BLEU type: bleu value: 29.0 - name: chr-F type: chrf value: 0.59301 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2015 type: wmt-2015-news args: deu-eng metrics: - name: BLEU type: bleu value: 33.4 - name: chr-F type: chrf value: 0.59660 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2015 type: wmt-2015-news args: eng-deu metrics: - name: BLEU type: bleu value: 32.3 - name: chr-F type: chrf value: 0.59889 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2016 type: wmt-2016-news args: deu-eng metrics: - name: BLEU type: bleu value: 39.8 - name: chr-F type: chrf value: 0.64736 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2016 type: wmt-2016-news args: eng-deu metrics: - name: BLEU type: bleu value: 38.3 - name: chr-F type: chrf value: 0.64427 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2017 type: wmt-2017-news args: deu-eng metrics: - name: BLEU type: bleu value: 35.2 - name: chr-F type: chrf value: 0.60933 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2017 type: wmt-2017-news args: eng-deu metrics: - name: BLEU type: bleu value: 30.7 - name: chr-F type: chrf value: 0.59257 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2018 type: wmt-2018-news args: deu-eng metrics: - name: BLEU type: bleu value: 42.6 - name: chr-F type: chrf value: 0.66797 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2018 type: wmt-2018-news args: eng-deu metrics: - name: BLEU type: bleu value: 46.5 - name: chr-F type: chrf value: 0.69605 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2019 type: wmt-2019-news args: deu-eng metrics: - name: BLEU type: bleu value: 39.7 - name: chr-F type: chrf value: 0.63749 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2019 type: wmt-2019-news args: eng-deu metrics: - name: BLEU type: bleu value: 42.9 - name: chr-F type: chrf value: 0.66751 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2020 type: wmt-2020-news args: deu-eng metrics: - name: BLEU type: bleu value: 35.0 - name: chr-F type: chrf value: 0.61200 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2020 type: wmt-2020-news args: eng-deu metrics: - name: BLEU type: bleu value: 32.3 - name: chr-F type: chrf value: 0.60411 --- # opus-mt-tc-big-gmw-gmw ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from West Germanic languages (gmw) to West Germanic languages (gmw). 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). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-08-11 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): afr deu eng enm fry gos gsw hrx ksh ltz nds nld pdc sco stq swg tpi yid - Target Language(s): afr ang deu eng enm fry gos ltz nds nld sco tpi yid - Language Pair(s): afr-deu afr-eng afr-nld deu-afr deu-eng deu-ltz deu-nds deu-nld eng-afr eng-deu eng-fry eng-nld fry-eng fry-nld gos-deu gos-eng gos-nld ltz-afr ltz-deu ltz-eng ltz-nld nds-deu nds-eng nds-nld nld-afr nld-deu nld-eng nld-fry - Valid Target Language Labels: >>act<< >>afr<< >>afs<< >>aig<< >>ang<< >>ang_Latn<< >>bah<< >>bar<< >>bis<< >>bjs<< >>brc<< >>bzj<< >>bzj_Latn<< >>bzk<< >>cim<< >>dcr<< >>deu<< >>djk<< >>djk_Latn<< >>drt<< >>drt_Latn<< >>dum<< >>eng<< >>enm<< >>enm_Latn<< >>fpe<< >>frk<< >>frr<< >>fry<< >>gcl<< >>gct<< >>geh<< >>gmh<< >>gml<< >>goh<< >>gos<< >>gpe<< >>gsw<< >>gul<< >>gyn<< >>hrx<< >>hrx_Latn<< >>hwc<< >>icr<< >>jam<< >>jvd<< >>kri<< >>ksh<< >>kww<< >>lim<< >>lng<< >>ltz<< >>mhn<< >>nds<< >>nld<< >>odt<< >>ofs<< >>ofs_Latn<< >>oor<< >>osx<< >>pcm<< >>pdc<< >>pdt<< >>pey<< >>pfl<< >>pih<< >>pih_Latn<< >>pis<< >>pis_Latn<< >>qlm<< >>rop<< >>sco<< >>sdz<< >>skw<< >>sli<< >>srm<< >>srm_Latn<< >>srn<< >>stl<< >>stq<< >>svc<< >>swg<< >>sxu<< >>tch<< >>tcs<< >>tgh<< >>tpi<< >>trf<< >>twd<< >>uln<< >>vel<< >>vic<< >>vls<< >>vmf<< >>wae<< >>wep<< >>wes<< >>wes_Latn<< >>wym<< >>ydd<< >>yec<< >>yid<< >>yih<< >>zea<< - **Original Model**: [opusTCv20210807_transformer-big_2022-08-11.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opusTCv20210807_transformer-big_2022-08-11.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT gmw-gmw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-gmw/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>afr<<` ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>nds<< Red keinen Quatsch.", ">>eng<< Findet ihr das nicht etwas übereilt?" ] model_name = "pytorch-models/opus-mt-tc-big-gmw-gmw" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Kiek ok bi: Rott. # Aren't you in a hurry? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmw-gmw") print(pipe(">>nds<< Red keinen Quatsch.")) # expected output: Kiek ok bi: Rott. ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-11.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opusTCv20210807_transformer-big_2022-08-11.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-11.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opusTCv20210807_transformer-big_2022-08-11.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-11.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opusTCv20210807_transformer-big_2022-08-11.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | afr-deu | tatoeba-test-v2021-08-07 | 0.68679 | 50.4 | 1583 | 9105 | | afr-eng | tatoeba-test-v2021-08-07 | 0.70682 | 56.6 | 1374 | 9622 | | afr-nld | tatoeba-test-v2021-08-07 | 0.71516 | 55.5 | 1056 | 6710 | | deu-afr | tatoeba-test-v2021-08-07 | 0.70274 | 54.3 | 1583 | 9507 | | deu-eng | tatoeba-test-v2021-08-07 | 0.66023 | 48.6 | 17565 | 149462 | | deu-nds | tatoeba-test-v2021-08-07 | 0.48058 | 23.2 | 9999 | 76137 | | deu-nld | tatoeba-test-v2021-08-07 | 0.71440 | 54.6 | 10218 | 75235 | | deu-yid | tatoeba-test-v2021-08-07 | 9.211 | 0.4 | 853 | 5355 | | eng-afr | tatoeba-test-v2021-08-07 | 0.71995 | 56.5 | 1374 | 10317 | | eng-deu | tatoeba-test-v2021-08-07 | 0.63103 | 42.0 | 17565 | 151568 | | eng-nld | tatoeba-test-v2021-08-07 | 0.71062 | 54.5 | 12696 | 91796 | | eng-yid | tatoeba-test-v2021-08-07 | 9.624 | 0.4 | 2483 | 16395 | | fry-eng | tatoeba-test-v2021-08-07 | 0.40545 | 25.1 | 220 | 1573 | | fry-nld | tatoeba-test-v2021-08-07 | 0.55771 | 41.7 | 260 | 1854 | | gos-deu | tatoeba-test-v2021-08-07 | 0.45302 | 25.4 | 207 | 1168 | | gos-eng | tatoeba-test-v2021-08-07 | 0.37628 | 24.1 | 1154 | 5635 | | gos-nld | tatoeba-test-v2021-08-07 | 0.45777 | 26.2 | 1852 | 9903 | | ltz-deu | tatoeba-test-v2021-08-07 | 0.37165 | 21.3 | 347 | 2208 | | ltz-eng | tatoeba-test-v2021-08-07 | 0.37784 | 30.3 | 293 | 1840 | | ltz-nld | tatoeba-test-v2021-08-07 | 0.32823 | 26.7 | 292 | 1685 | | nds-deu | tatoeba-test-v2021-08-07 | 0.64008 | 45.4 | 9999 | 74564 | | nds-eng | tatoeba-test-v2021-08-07 | 0.55193 | 38.3 | 2500 | 17589 | | nds-nld | tatoeba-test-v2021-08-07 | 0.66943 | 50.0 | 1657 | 11490 | | nld-afr | tatoeba-test-v2021-08-07 | 0.76610 | 62.3 | 1056 | 6823 | | nld-deu | tatoeba-test-v2021-08-07 | 0.73162 | 56.8 | 10218 | 74131 | | nld-eng | tatoeba-test-v2021-08-07 | 0.74088 | 60.5 | 12696 | 89978 | | nld-fry | tatoeba-test-v2021-08-07 | 0.48460 | 31.4 | 260 | 1857 | | nld-nds | tatoeba-test-v2021-08-07 | 0.43779 | 19.9 | 1657 | 11711 | | swg-deu | tatoeba-test-v2021-08-07 | 0.40348 | 16.1 | 1523 | 15632 | | yid-deu | tatoeba-test-v2021-08-07 | 6.305 | 0.1 | 853 | 5173 | | yid-eng | tatoeba-test-v2021-08-07 | 3.704 | 0.1 | 2483 | 15452 | | afr-deu | flores101-devtest | 0.58718 | 30.2 | 1012 | 25094 | | afr-eng | flores101-devtest | 0.74826 | 55.1 | 1012 | 24721 | | afr-ltz | flores101-devtest | 0.46826 | 15.7 | 1012 | 25087 | | afr-nld | flores101-devtest | 0.54441 | 22.5 | 1012 | 25467 | | deu-afr | flores101-devtest | 0.57835 | 26.4 | 1012 | 25740 | | deu-eng | flores101-devtest | 0.66990 | 41.8 | 1012 | 24721 | | deu-ltz | flores101-devtest | 0.52554 | 20.3 | 1012 | 25087 | | deu-nld | flores101-devtest | 0.55710 | 24.2 | 1012 | 25467 | | eng-afr | flores101-devtest | 0.68429 | 40.7 | 1012 | 25740 | | eng-deu | flores101-devtest | 0.64888 | 38.5 | 1012 | 25094 | | eng-ltz | flores101-devtest | 0.49231 | 18.4 | 1012 | 25087 | | eng-nld | flores101-devtest | 0.57984 | 26.8 | 1012 | 25467 | | ltz-afr | flores101-devtest | 0.53623 | 23.2 | 1012 | 25740 | | ltz-deu | flores101-devtest | 0.59122 | 30.0 | 1012 | 25094 | | ltz-eng | flores101-devtest | 0.57557 | 31.0 | 1012 | 24721 | | ltz-nld | flores101-devtest | 0.49312 | 18.6 | 1012 | 25467 | | nld-afr | flores101-devtest | 0.52409 | 20.0 | 1012 | 25740 | | nld-deu | flores101-devtest | 0.53898 | 22.6 | 1012 | 25094 | | nld-eng | flores101-devtest | 0.58970 | 30.7 | 1012 | 24721 | | nld-ltz | flores101-devtest | 0.42637 | 11.8 | 1012 | 25087 | | deu-eng | multi30k_test_2016_flickr | 0.60928 | 39.9 | 1000 | 12955 | | eng-deu | multi30k_test_2016_flickr | 0.64172 | 35.4 | 1000 | 12106 | | deu-eng | multi30k_test_2017_flickr | 0.63154 | 40.5 | 1000 | 11374 | | eng-deu | multi30k_test_2017_flickr | 0.63078 | 34.2 | 1000 | 10755 | | deu-eng | multi30k_test_2017_mscoco | 0.55708 | 32.2 | 461 | 5231 | | eng-deu | multi30k_test_2017_mscoco | 0.57537 | 29.1 | 461 | 5158 | | deu-eng | multi30k_test_2018_flickr | 0.59422 | 36.9 | 1071 | 14689 | | eng-deu | multi30k_test_2018_flickr | 0.59597 | 30.0 | 1071 | 13703 | | deu-eng | newssyscomb2009 | 0.54993 | 28.2 | 502 | 11818 | | eng-deu | newssyscomb2009 | 0.53867 | 23.2 | 502 | 11271 | | deu-eng | news-test2008 | 0.54601 | 27.2 | 2051 | 49380 | | eng-deu | news-test2008 | 0.53149 | 23.6 | 2051 | 47447 | | deu-eng | newstest2009 | 0.53747 | 25.9 | 2525 | 65399 | | eng-deu | newstest2009 | 0.53283 | 22.9 | 2525 | 62816 | | deu-eng | newstest2010 | 0.58355 | 30.6 | 2489 | 61711 | | eng-deu | newstest2010 | 0.54885 | 25.8 | 2489 | 61503 | | deu-eng | newstest2011 | 0.54883 | 26.3 | 3003 | 74681 | | eng-deu | newstest2011 | 0.52712 | 23.1 | 3003 | 72981 | | deu-eng | newstest2012 | 0.56153 | 28.5 | 3003 | 72812 | | eng-deu | newstest2012 | 0.52662 | 23.3 | 3003 | 72886 | | deu-eng | newstest2013 | 0.57770 | 31.4 | 3000 | 64505 | | eng-deu | newstest2013 | 0.55774 | 27.8 | 3000 | 63737 | | deu-eng | newstest2014 | 0.59826 | 33.2 | 3003 | 67337 | | eng-deu | newstest2014 | 0.59301 | 29.0 | 3003 | 62688 | | deu-eng | newstest2015 | 0.59660 | 33.4 | 2169 | 46443 | | eng-deu | newstest2015 | 0.59889 | 32.3 | 2169 | 44260 | | deu-eng | newstest2016 | 0.64736 | 39.8 | 2999 | 64119 | | eng-deu | newstest2016 | 0.64427 | 38.3 | 2999 | 62669 | | deu-eng | newstest2017 | 0.60933 | 35.2 | 3004 | 64399 | | eng-deu | newstest2017 | 0.59257 | 30.7 | 3004 | 61287 | | deu-eng | newstest2018 | 0.66797 | 42.6 | 2998 | 67012 | | eng-deu | newstest2018 | 0.69605 | 46.5 | 2998 | 64276 | | deu-eng | newstest2019 | 0.63749 | 39.7 | 2000 | 39227 | | eng-deu | newstest2019 | 0.66751 | 42.9 | 1997 | 48746 | | deu-eng | newstest2020 | 0.61200 | 35.0 | 785 | 38220 | | eng-deu | newstest2020 | 0.60411 | 32.3 | 1418 | 52383 | | deu-eng | newstestB2020 | 0.61255 | 35.1 | 785 | 37696 | | eng-deu | newstestB2020 | 0.59513 | 31.8 | 1418 | 53092 | ## Citation Information * 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.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements 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. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 8b9f0b0 * port time: Fri Aug 12 23:58:31 EEST 2022 * port machine: LM0-400-22516.local