--- language: - af - de - en - fy - gmw - gos - hrx - lb - nds - nl - pdc - yi tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-base-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: 21.6 - 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: 46.8 - 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: 21.4 - 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: 33.8 - 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: 33.8 - 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: 29.1 - 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: 21.0 - 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: 25.6 - 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: 32.2 - 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: 28.8 - 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: 32.7 - 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: 27.6 - 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: 25.5 - 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: 22.0 - 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: 30.0 - 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: 25.3 - 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: 23.8 - 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: 48.1 - 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: 58.8 - 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: 54.5 - 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: 52.4 - 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: 42.1 - 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: 48.7 - 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 - 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: 35.9 - 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: 48.3 - 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: 32.5 - 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: 43.1 - task: name: Translation hrx-deu type: translation args: hrx-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrx-deu metrics: - name: BLEU type: bleu value: 24.7 - task: name: Translation hrx-eng type: translation args: hrx-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrx-eng metrics: - name: BLEU type: bleu value: 20.4 - 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: 37.2 - 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: 32.4 - 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: 39.3 - 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: 34.5 - 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: 29.9 - 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: 42.3 - 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: 58.8 - 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: 50.4 - 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: 53.1 - 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: 25.1 - task: name: Translation nld-nds type: translation args: nld-nds dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nld-nds metrics: - name: BLEU type: bleu value: 21.4 - 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: 23.4 - 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: 25.8 - 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: 20.7 - 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: 23.7 - 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: 24.8 - 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: 27.7 - 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: 22.5 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2014-deen type: wmt-2014-news args: deu-eng metrics: - name: BLEU type: bleu value: 27.3 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2014-deen type: wmt-2014-news args: eng-deu metrics: - name: BLEU type: bleu value: 22.0 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2015-deen type: wmt-2015-news args: deu-eng metrics: - name: BLEU type: bleu value: 28.6 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2015-ende type: wmt-2015-news args: eng-deu metrics: - name: BLEU type: bleu value: 25.7 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2016-deen type: wmt-2016-news args: deu-eng metrics: - name: BLEU type: bleu value: 33.3 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2016-ende type: wmt-2016-news args: eng-deu metrics: - name: BLEU type: bleu value: 30.0 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2017-deen type: wmt-2017-news args: deu-eng metrics: - name: BLEU type: bleu value: 29.5 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2017-ende type: wmt-2017-news args: eng-deu metrics: - name: BLEU type: bleu value: 24.1 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2018-deen type: wmt-2018-news args: deu-eng metrics: - name: BLEU type: bleu value: 36.1 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2018-ende type: wmt-2018-news args: eng-deu metrics: - name: BLEU type: bleu value: 35.4 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2019-deen type: wmt-2019-news args: deu-eng metrics: - name: BLEU type: bleu value: 32.3 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2019-ende type: wmt-2019-news args: eng-deu metrics: - name: BLEU type: bleu value: 31.2 - task: name: Translation deu-eng type: translation args: deu-eng dataset: name: newstest2020-deen type: wmt-2020-news args: deu-eng metrics: - name: BLEU type: bleu value: 32.0 - task: name: Translation eng-deu type: translation args: eng-deu dataset: name: newstest2020-ende type: wmt-2020-news args: eng-deu metrics: - name: BLEU type: bleu value: 23.9 --- # opus-mt-tc-base-gmw-gmw 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). * 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", } ``` ## Model info * Release: 2021-02-23 * source language(s): afr deu eng fry gos hrx ltz nds nld pdc yid * target language(s): afr deu eng fry nds nld * valid target language labels: >>afr<< >>ang_Latn<< >>deu<< >>eng<< >>fry<< >>ltz<< >>nds<< >>nld<< >>sco<< >>yid<< * model: transformer (base) * data: opus ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opus-2021-02-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.zip) * more information released models: [OPUS-MT gmw-gmw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-gmw/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) 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<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>nld<< You need help.", ">>afr<< I love your son." ] model_name = "pytorch-models/opus-mt-tc-base-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: # Je hebt hulp nodig. # Ek is lief vir jou seun. ``` 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-base-gmw-gmw") print(pipe(>>nld<< You need help.)) # expected output: Je hebt hulp nodig. ``` ## Benchmarks * test set translations: [opus-2021-02-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.test.txt) * test set scores: [opus-2021-02-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.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 | 0.674 | 48.1 | 1583 | 9105 | | afr-eng | tatoeba-test-v2021-08-07 | 0.728 | 58.8 | 1374 | 9622 | | afr-nld | tatoeba-test-v2021-08-07 | 0.711 | 54.5 | 1056 | 6710 | | deu-afr | tatoeba-test-v2021-08-07 | 0.696 | 52.4 | 1583 | 9507 | | deu-eng | tatoeba-test-v2021-08-07 | 0.609 | 42.1 | 17565 | 149462 | | deu-nds | tatoeba-test-v2021-08-07 | 0.442 | 18.6 | 9999 | 76137 | | deu-nld | tatoeba-test-v2021-08-07 | 0.672 | 48.7 | 10218 | 75235 | | eng-afr | tatoeba-test-v2021-08-07 | 0.735 | 56.5 | 1374 | 10317 | | eng-deu | tatoeba-test-v2021-08-07 | 0.580 | 35.9 | 17565 | 151568 | | eng-nds | tatoeba-test-v2021-08-07 | 0.412 | 16.6 | 2500 | 18264 | | eng-nld | tatoeba-test-v2021-08-07 | 0.663 | 48.3 | 12696 | 91796 | | fry-eng | tatoeba-test-v2021-08-07 | 0.500 | 32.5 | 220 | 1573 | | fry-nld | tatoeba-test-v2021-08-07 | 0.633 | 43.1 | 260 | 1854 | | gos-nld | tatoeba-test-v2021-08-07 | 0.405 | 15.6 | 1852 | 9903 | | hrx-deu | tatoeba-test-v2021-08-07 | 0.484 | 24.7 | 471 | 2805 | | hrx-eng | tatoeba-test-v2021-08-07 | 0.362 | 20.4 | 221 | 1235 | | ltz-deu | tatoeba-test-v2021-08-07 | 0.556 | 37.2 | 347 | 2208 | | ltz-eng | tatoeba-test-v2021-08-07 | 0.485 | 32.4 | 293 | 1840 | | ltz-nld | tatoeba-test-v2021-08-07 | 0.534 | 39.3 | 292 | 1685 | | nds-deu | tatoeba-test-v2021-08-07 | 0.572 | 34.5 | 9999 | 74564 | | nds-eng | tatoeba-test-v2021-08-07 | 0.493 | 29.9 | 2500 | 17589 | | nds-nld | tatoeba-test-v2021-08-07 | 0.621 | 42.3 | 1657 | 11490 | | nld-afr | tatoeba-test-v2021-08-07 | 0.755 | 58.8 | 1056 | 6823 | | nld-deu | tatoeba-test-v2021-08-07 | 0.686 | 50.4 | 10218 | 74131 | | nld-eng | tatoeba-test-v2021-08-07 | 0.690 | 53.1 | 12696 | 89978 | | nld-fry | tatoeba-test-v2021-08-07 | 0.478 | 25.1 | 260 | 1857 | | nld-nds | tatoeba-test-v2021-08-07 | 0.462 | 21.4 | 1657 | 11711 | | afr-deu | flores101-devtest | 0.524 | 21.6 | 1012 | 25094 | | afr-eng | flores101-devtest | 0.693 | 46.8 | 1012 | 24721 | | afr-nld | flores101-devtest | 0.509 | 18.4 | 1012 | 25467 | | deu-afr | flores101-devtest | 0.534 | 21.4 | 1012 | 25740 | | deu-eng | flores101-devtest | 0.616 | 33.8 | 1012 | 24721 | | deu-nld | flores101-devtest | 0.516 | 19.2 | 1012 | 25467 | | eng-afr | flores101-devtest | 0.628 | 33.8 | 1012 | 25740 | | eng-deu | flores101-devtest | 0.581 | 29.1 | 1012 | 25094 | | eng-nld | flores101-devtest | 0.533 | 21.0 | 1012 | 25467 | | ltz-afr | flores101-devtest | 0.430 | 12.9 | 1012 | 25740 | | ltz-deu | flores101-devtest | 0.482 | 17.1 | 1012 | 25094 | | ltz-eng | flores101-devtest | 0.468 | 18.8 | 1012 | 24721 | | ltz-nld | flores101-devtest | 0.409 | 10.7 | 1012 | 25467 | | nld-afr | flores101-devtest | 0.494 | 16.8 | 1012 | 25740 | | nld-deu | flores101-devtest | 0.501 | 17.9 | 1012 | 25094 | | nld-eng | flores101-devtest | 0.551 | 25.6 | 1012 | 24721 | | deu-eng | multi30k_test_2016_flickr | 0.546 | 32.2 | 1000 | 12955 | | eng-deu | multi30k_test_2016_flickr | 0.582 | 28.8 | 1000 | 12106 | | deu-eng | multi30k_test_2017_flickr | 0.561 | 32.7 | 1000 | 11374 | | eng-deu | multi30k_test_2017_flickr | 0.573 | 27.6 | 1000 | 10755 | | deu-eng | multi30k_test_2017_mscoco | 0.499 | 25.5 | 461 | 5231 | | eng-deu | multi30k_test_2017_mscoco | 0.514 | 22.0 | 461 | 5158 | | deu-eng | multi30k_test_2018_flickr | 0.535 | 30.0 | 1071 | 14689 | | eng-deu | multi30k_test_2018_flickr | 0.547 | 25.3 | 1071 | 13703 | | deu-eng | newssyscomb2009 | 0.527 | 25.4 | 502 | 11818 | | eng-deu | newssyscomb2009 | 0.504 | 19.3 | 502 | 11271 | | deu-eng | news-test2008 | 0.518 | 23.8 | 2051 | 49380 | | eng-deu | news-test2008 | 0.492 | 19.3 | 2051 | 47447 | | deu-eng | newstest2009 | 0.516 | 23.4 | 2525 | 65399 | | eng-deu | newstest2009 | 0.498 | 18.8 | 2525 | 62816 | | deu-eng | newstest2010 | 0.546 | 25.8 | 2489 | 61711 | | eng-deu | newstest2010 | 0.508 | 20.7 | 2489 | 61503 | | deu-eng | newstest2011 | 0.524 | 23.7 | 3003 | 74681 | | eng-deu | newstest2011 | 0.493 | 19.2 | 3003 | 72981 | | deu-eng | newstest2012 | 0.532 | 24.8 | 3003 | 72812 | | eng-deu | newstest2012 | 0.493 | 19.5 | 3003 | 72886 | | deu-eng | newstest2013 | 0.548 | 27.7 | 3000 | 64505 | | eng-deu | newstest2013 | 0.517 | 22.5 | 3000 | 63737 | | deu-eng | newstest2014-deen | 0.548 | 27.3 | 3003 | 67337 | | eng-deu | newstest2014-deen | 0.532 | 22.0 | 3003 | 62688 | | deu-eng | newstest2015-deen | 0.553 | 28.6 | 2169 | 46443 | | eng-deu | newstest2015-ende | 0.544 | 25.7 | 2169 | 44260 | | deu-eng | newstest2016-deen | 0.596 | 33.3 | 2999 | 64119 | | eng-deu | newstest2016-ende | 0.580 | 30.0 | 2999 | 62669 | | deu-eng | newstest2017-deen | 0.561 | 29.5 | 3004 | 64399 | | eng-deu | newstest2017-ende | 0.535 | 24.1 | 3004 | 61287 | | deu-eng | newstest2018-deen | 0.610 | 36.1 | 2998 | 67012 | | eng-deu | newstest2018-ende | 0.613 | 35.4 | 2998 | 64276 | | deu-eng | newstest2019-deen | 0.582 | 32.3 | 2000 | 39227 | | eng-deu | newstest2019-ende | 0.583 | 31.2 | 1997 | 48746 | | deu-eng | newstest2020-deen | 0.604 | 32.0 | 785 | 38220 | | eng-deu | newstest2020-ende | 0.542 | 23.9 | 1418 | 52383 | | deu-eng | newstestB2020-deen | 0.598 | 31.2 | 785 | 37696 | | eng-deu | newstestB2020-ende | 0.532 | 23.3 | 1418 | 53092 | ## 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: 4.12.3 * OPUS-MT git hash: e56a06b * port time: Sun Feb 13 14:42:10 EET 2022 * port machine: LM0-400-22516.local