Add `opus-mt-tc` tag (#1)

d688a46 almost 2 years ago

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25.9 kB

	---
	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