metadata
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
- 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
- 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
- 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
- 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
- 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
- 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, 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, 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 and training pipelines use the procedures of OPUS-MT-train.
- Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (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)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opus-2021-02-23.zip
- more information released models: OPUS-MT gmw-gmw README
- more information about the model: MarianMT
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:
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:
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
- test set scores: opus-2021-02-23.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: 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 as pilot project 2866, by the FoTran project, 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, 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, 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