metadata
language:
- af
- de
- en
- fy
- gos
- lb
- nds
- nl
- multilingual
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-big-gmw-gmw
results:
- task:
type: translation
name: Translation afr-deu
dataset:
name: flores101-devtest
type: flores_101
args: afr deu devtest
metrics:
- type: bleu
value: 30.2
name: BLEU
- type: chrf
value: 0.58718
name: chr-F
- type: bleu
value: 55.1
name: BLEU
- type: chrf
value: 0.74826
name: chr-F
- type: bleu
value: 15.7
name: BLEU
- type: chrf
value: 0.46826
name: chr-F
- type: bleu
value: 22.5
name: BLEU
- type: chrf
value: 0.54441
name: chr-F
- type: bleu
value: 26.4
name: BLEU
- type: chrf
value: 0.57835
name: chr-F
- type: bleu
value: 41.8
name: BLEU
- type: chrf
value: 0.6699
name: chr-F
- type: bleu
value: 20.3
name: BLEU
- type: chrf
value: 0.52554
name: chr-F
- type: bleu
value: 24.2
name: BLEU
- type: chrf
value: 0.5571
name: chr-F
- type: bleu
value: 40.7
name: BLEU
- type: chrf
value: 0.68429
name: chr-F
- type: bleu
value: 38.5
name: BLEU
- type: chrf
value: 0.64888
name: chr-F
- type: bleu
value: 18.4
name: BLEU
- type: chrf
value: 0.49231
name: chr-F
- type: bleu
value: 26.8
name: BLEU
- type: chrf
value: 0.57984
name: chr-F
- type: bleu
value: 23.2
name: BLEU
- type: chrf
value: 0.53623
name: chr-F
- type: bleu
value: 30
name: BLEU
- type: chrf
value: 0.59122
name: chr-F
- type: bleu
value: 31
name: BLEU
- type: chrf
value: 0.57557
name: chr-F
- type: bleu
value: 18.6
name: BLEU
- type: chrf
value: 0.49312
name: chr-F
- type: bleu
value: 20
name: BLEU
- type: chrf
value: 0.52409
name: chr-F
- type: bleu
value: 22.6
name: BLEU
- type: chrf
value: 0.53898
name: chr-F
- type: bleu
value: 30.7
name: BLEU
- type: chrf
value: 0.5897
name: chr-F
- type: bleu
value: 11.8
name: BLEU
- type: chrf
value: 0.42637
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: multi30k_test_2016_flickr
type: multi30k-2016_flickr
args: deu-eng
metrics:
- type: bleu
value: 39.9
name: BLEU
- type: chrf
value: 0.60928
name: chr-F
- type: bleu
value: 35.4
name: BLEU
- type: chrf
value: 0.64172
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: multi30k_test_2017_flickr
type: multi30k-2017_flickr
args: deu-eng
metrics:
- type: bleu
value: 40.5
name: BLEU
- type: chrf
value: 0.63154
name: chr-F
- type: bleu
value: 34.2
name: BLEU
- type: chrf
value: 0.63078
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: multi30k_test_2017_mscoco
type: multi30k-2017_mscoco
args: deu-eng
metrics:
- type: bleu
value: 32.2
name: BLEU
- type: chrf
value: 0.55708
name: chr-F
- type: bleu
value: 29.1
name: BLEU
- type: chrf
value: 0.57537
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: multi30k_test_2018_flickr
type: multi30k-2018_flickr
args: deu-eng
metrics:
- type: bleu
value: 36.9
name: BLEU
- type: chrf
value: 0.59422
name: chr-F
- type: bleu
value: 30
name: BLEU
- type: chrf
value: 0.59597
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: news-test2008
type: news-test2008
args: deu-eng
metrics:
- type: bleu
value: 27.2
name: BLEU
- type: chrf
value: 0.54601
name: chr-F
- type: bleu
value: 23.6
name: BLEU
- type: chrf
value: 0.53149
name: chr-F
- task:
type: translation
name: Translation afr-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: afr-deu
metrics:
- type: bleu
value: 50.4
name: BLEU
- type: chrf
value: 0.68679
name: chr-F
- type: bleu
value: 56.6
name: BLEU
- type: chrf
value: 0.70682
name: chr-F
- type: bleu
value: 55.5
name: BLEU
- type: chrf
value: 0.71516
name: chr-F
- type: bleu
value: 54.3
name: BLEU
- type: chrf
value: 0.70274
name: chr-F
- type: bleu
value: 48.6
name: BLEU
- type: chrf
value: 0.66023
name: chr-F
- type: bleu
value: 23.2
name: BLEU
- type: chrf
value: 0.48058
name: chr-F
- type: bleu
value: 54.6
name: BLEU
- type: chrf
value: 0.7144
name: chr-F
- type: bleu
value: 56.5
name: BLEU
- type: chrf
value: 0.71995
name: chr-F
- type: bleu
value: 42
name: BLEU
- type: chrf
value: 0.63103
name: chr-F
- type: bleu
value: 21.3
name: BLEU
- type: chrf
value: 0.3858
name: chr-F
- type: bleu
value: 54.5
name: BLEU
- type: chrf
value: 0.71062
name: chr-F
- type: bleu
value: 25.1
name: BLEU
- type: chrf
value: 0.40545
name: chr-F
- type: bleu
value: 41.7
name: BLEU
- type: chrf
value: 0.55771
name: chr-F
- type: bleu
value: 25.4
name: BLEU
- type: chrf
value: 0.45302
name: chr-F
- type: bleu
value: 24.1
name: BLEU
- type: chrf
value: 0.37628
name: chr-F
- type: bleu
value: 26.2
name: BLEU
- type: chrf
value: 0.45777
name: chr-F
- type: bleu
value: 21.3
name: BLEU
- type: chrf
value: 0.37165
name: chr-F
- type: bleu
value: 30.3
name: BLEU
- type: chrf
value: 0.37784
name: chr-F
- type: bleu
value: 26.7
name: BLEU
- type: chrf
value: 0.32823
name: chr-F
- type: bleu
value: 45.4
name: BLEU
- type: chrf
value: 0.64008
name: chr-F
- type: bleu
value: 38.3
name: BLEU
- type: chrf
value: 0.55193
name: chr-F
- type: bleu
value: 50
name: BLEU
- type: chrf
value: 0.66943
name: chr-F
- type: bleu
value: 62.3
name: BLEU
- type: chrf
value: 0.7661
name: chr-F
- type: bleu
value: 56.8
name: BLEU
- type: chrf
value: 0.73162
name: chr-F
- type: bleu
value: 60.5
name: BLEU
- type: chrf
value: 0.74088
name: chr-F
- type: bleu
value: 31.4
name: BLEU
- type: chrf
value: 0.4846
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2009
type: wmt-2009-news
args: deu-eng
metrics:
- type: bleu
value: 25.9
name: BLEU
- type: chrf
value: 0.53747
name: chr-F
- type: bleu
value: 22.9
name: BLEU
- type: chrf
value: 0.53283
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2010
type: wmt-2010-news
args: deu-eng
metrics:
- type: bleu
value: 30.6
name: BLEU
- type: chrf
value: 0.58355
name: chr-F
- type: bleu
value: 25.8
name: BLEU
- type: chrf
value: 0.54885
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2011
type: wmt-2011-news
args: deu-eng
metrics:
- type: bleu
value: 26.3
name: BLEU
- type: chrf
value: 0.54883
name: chr-F
- type: bleu
value: 23.1
name: BLEU
- type: chrf
value: 0.52712
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2012
type: wmt-2012-news
args: deu-eng
metrics:
- type: bleu
value: 28.5
name: BLEU
- type: chrf
value: 0.56153
name: chr-F
- type: bleu
value: 23.3
name: BLEU
- type: chrf
value: 0.52662
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2013
type: wmt-2013-news
args: deu-eng
metrics:
- type: bleu
value: 31.4
name: BLEU
- type: chrf
value: 0.5777
name: chr-F
- type: bleu
value: 27.8
name: BLEU
- type: chrf
value: 0.55774
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2014
type: wmt-2014-news
args: deu-eng
metrics:
- type: bleu
value: 33.2
name: BLEU
- type: chrf
value: 0.59826
name: chr-F
- type: bleu
value: 29
name: BLEU
- type: chrf
value: 0.59301
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2015
type: wmt-2015-news
args: deu-eng
metrics:
- type: bleu
value: 33.4
name: BLEU
- type: chrf
value: 0.5966
name: chr-F
- type: bleu
value: 32.3
name: BLEU
- type: chrf
value: 0.59889
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2016
type: wmt-2016-news
args: deu-eng
metrics:
- type: bleu
value: 39.8
name: BLEU
- type: chrf
value: 0.64736
name: chr-F
- type: bleu
value: 38.3
name: BLEU
- type: chrf
value: 0.64427
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2017
type: wmt-2017-news
args: deu-eng
metrics:
- type: bleu
value: 35.2
name: BLEU
- type: chrf
value: 0.60933
name: chr-F
- type: bleu
value: 30.7
name: BLEU
- type: chrf
value: 0.59257
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2018
type: wmt-2018-news
args: deu-eng
metrics:
- type: bleu
value: 42.6
name: BLEU
- type: chrf
value: 0.66797
name: chr-F
- type: bleu
value: 46.5
name: BLEU
- type: chrf
value: 0.69605
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2019
type: wmt-2019-news
args: deu-eng
metrics:
- type: bleu
value: 39.7
name: BLEU
- type: chrf
value: 0.63749
name: chr-F
- type: bleu
value: 42.9
name: BLEU
- type: chrf
value: 0.66751
name: chr-F
- task:
type: translation
name: Translation deu-eng
dataset:
name: newstest2020
type: wmt-2020-news
args: deu-eng
metrics:
- type: bleu
value: 35
name: BLEU
- type: chrf
value: 0.612
name: chr-F
- type: bleu
value: 32.3
name: BLEU
- type: chrf
value: 0.60411
name: chr-F
opus-mt-tc-big-gmw-gmw
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- How to Get Started With the Model
- Training
- Evaluation
- Citation Information
- 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, 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. 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
- Resources for more information:
- OPUS-MT-train GitHub Repo
- More information about released models for this language pair: OPUS-MT gmw-gmw README
- More information about MarianNMT models in the transformers library
- [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) and Bender et al. (2021)).
How to Get Started With the Model
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>nds<< Red keinen Quatsch.",
">>eng<< Findet ihr das nicht etwas �bereilt?"
]
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:
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)
- Pre-processing: SentencePiece (spm32k,spm32k)
- Model Type: transformer-big
- Original MarianNMT Model: opusTCv20210807_transformer-big_2022-08-11.zip
- Training Scripts: GitHub Repo
Evaluation
- test set translations: opusTCv20210807_transformer-big_2022-08-11.test.txt
- test set scores: opusTCv20210807_transformer-big_2022-08-11.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 | 0.68679 | 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 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",
}
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: 4.16.2
- OPUS-MT git hash: 8b9f0b0
- port time: Fri Aug 12 23:58:31 EEST 2022
- port machine: LM0-400-22516.local