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	---
	language:
	- ca
	- da
	- es
	- fr
	- gl
	- is
	- it
	- nb
	- pt
	- ro
	- sv

	tags:
	- translation
	- opus-mt-tc

	license: cc-by-4.0
	model-index:
	- name: opus-mt-tc-big-gmq-itc
	results:
	- task:
	name: Translation dan-cat
	type: translation
	args: dan-cat
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan cat devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 33.4
	- name: chr-F
	type: chrf
	value: 0.59224
	- task:
	name: Translation dan-fra
	type: translation
	args: dan-fra
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan fra devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 38.3
	- name: chr-F
	type: chrf
	value: 0.63387
	- task:
	name: Translation dan-glg
	type: translation
	args: dan-glg
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan glg devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 26.4
	- name: chr-F
	type: chrf
	value: 0.54446
	- task:
	name: Translation dan-ita
	type: translation
	args: dan-ita
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan ita devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 25.7
	- name: chr-F
	type: chrf
	value: 0.55237
	- task:
	name: Translation dan-por
	type: translation
	args: dan-por
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan por devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 36.9
	- name: chr-F
	type: chrf
	value: 0.62233
	- task:
	name: Translation dan-ron
	type: translation
	args: dan-ron
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan ron devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 31.8
	- name: chr-F
	type: chrf
	value: 0.58235
	- task:
	name: Translation dan-spa
	type: translation
	args: dan-spa
	dataset:
	name: flores101-devtest
	type: flores_101
	args: dan spa devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 24.3
	- name: chr-F
	type: chrf
	value: 0.52453
	- task:
	name: Translation isl-cat
	type: translation
	args: isl-cat
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl cat devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 22.7
	- name: chr-F
	type: chrf
	value: 0.48930
	- task:
	name: Translation isl-fra
	type: translation
	args: isl-fra
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl fra devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 26.2
	- name: chr-F
	type: chrf
	value: 0.52704
	- task:
	name: Translation isl-glg
	type: translation
	args: isl-glg
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl glg devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 18.0
	- name: chr-F
	type: chrf
	value: 0.45387
	- task:
	name: Translation isl-ita
	type: translation
	args: isl-ita
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl ita devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 18.6
	- name: chr-F
	type: chrf
	value: 0.47303
	- task:
	name: Translation isl-por
	type: translation
	args: isl-por
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl por devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 24.9
	- name: chr-F
	type: chrf
	value: 0.51381
	- task:
	name: Translation isl-ron
	type: translation
	args: isl-ron
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl ron devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 21.6
	- name: chr-F
	type: chrf
	value: 0.48224
	- task:
	name: Translation isl-spa
	type: translation
	args: isl-spa
	dataset:
	name: flores101-devtest
	type: flores_101
	args: isl spa devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 18.1
	- name: chr-F
	type: chrf
	value: 0.45786
	- task:
	name: Translation nob-cat
	type: translation
	args: nob-cat
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob cat devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 28.9
	- name: chr-F
	type: chrf
	value: 0.55984
	- task:
	name: Translation nob-fra
	type: translation
	args: nob-fra
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob fra devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 33.8
	- name: chr-F
	type: chrf
	value: 0.60102
	- task:
	name: Translation nob-glg
	type: translation
	args: nob-glg
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob glg devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 23.4
	- name: chr-F
	type: chrf
	value: 0.52145
	- task:
	name: Translation nob-ita
	type: translation
	args: nob-ita
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob ita devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 22.2
	- name: chr-F
	type: chrf
	value: 0.52619
	- task:
	name: Translation nob-por
	type: translation
	args: nob-por
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob por devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 32.2
	- name: chr-F
	type: chrf
	value: 0.58836
	- task:
	name: Translation nob-ron
	type: translation
	args: nob-ron
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob ron devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 27.6
	- name: chr-F
	type: chrf
	value: 0.54845
	- task:
	name: Translation nob-spa
	type: translation
	args: nob-spa
	dataset:
	name: flores101-devtest
	type: flores_101
	args: nob spa devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 21.8
	- name: chr-F
	type: chrf
	value: 0.50661
	- task:
	name: Translation swe-cat
	type: translation
	args: swe-cat
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe cat devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 32.4
	- name: chr-F
	type: chrf
	value: 0.58542
	- task:
	name: Translation swe-fra
	type: translation
	args: swe-fra
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe fra devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 39.3
	- name: chr-F
	type: chrf
	value: 0.63688
	- task:
	name: Translation swe-glg
	type: translation
	args: swe-glg
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe glg devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 26.0
	- name: chr-F
	type: chrf
	value: 0.53989
	- task:
	name: Translation swe-ita
	type: translation
	args: swe-ita
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe ita devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 25.9
	- name: chr-F
	type: chrf
	value: 0.55232
	- task:
	name: Translation swe-por
	type: translation
	args: swe-por
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe por devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 36.5
	- name: chr-F
	type: chrf
	value: 0.61882
	- task:
	name: Translation swe-ron
	type: translation
	args: swe-ron
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe ron devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 31.0
	- name: chr-F
	type: chrf
	value: 0.57419
	- task:
	name: Translation swe-spa
	type: translation
	args: swe-spa
	dataset:
	name: flores101-devtest
	type: flores_101
	args: swe spa devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 23.8
	- name: chr-F
	type: chrf
	value: 0.52175
	- task:
	name: Translation dan-fra
	type: translation
	args: dan-fra
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: dan-fra
	metrics:
	- name: BLEU
	type: bleu
	value: 63.8
	- name: chr-F
	type: chrf
	value: 0.76671
	- task:
	name: Translation dan-ita
	type: translation
	args: dan-ita
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: dan-ita
	metrics:
	- name: BLEU
	type: bleu
	value: 56.2
	- name: chr-F
	type: chrf
	value: 0.74658
	- task:
	name: Translation dan-por
	type: translation
	args: dan-por
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: dan-por
	metrics:
	- name: BLEU
	type: bleu
	value: 57.8
	- name: chr-F
	type: chrf
	value: 0.74944
	- task:
	name: Translation dan-spa
	type: translation
	args: dan-spa
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: dan-spa
	metrics:
	- name: BLEU
	type: bleu
	value: 54.8
	- name: chr-F
	type: chrf
	value: 0.72328
	- task:
	name: Translation isl-ita
	type: translation
	args: isl-ita
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: isl-ita
	metrics:
	- name: BLEU
	type: bleu
	value: 51.0
	- name: chr-F
	type: chrf
	value: 0.69354
	- task:
	name: Translation isl-spa
	type: translation
	args: isl-spa
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: isl-spa
	metrics:
	- name: BLEU
	type: bleu
	value: 49.2
	- name: chr-F
	type: chrf
	value: 0.66008
	- task:
	name: Translation nob-fra
	type: translation
	args: nob-fra
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: nob-fra
	metrics:
	- name: BLEU
	type: bleu
	value: 54.4
	- name: chr-F
	type: chrf
	value: 0.70854
	- task:
	name: Translation nob-spa
	type: translation
	args: nob-spa
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: nob-spa
	metrics:
	- name: BLEU
	type: bleu
	value: 55.9
	- name: chr-F
	type: chrf
	value: 0.73672
	- task:
	name: Translation swe-fra
	type: translation
	args: swe-fra
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: swe-fra
	metrics:
	- name: BLEU
	type: bleu
	value: 59.2
	- name: chr-F
	type: chrf
	value: 0.73014
	- task:
	name: Translation swe-ita
	type: translation
	args: swe-ita
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: swe-ita
	metrics:
	- name: BLEU
	type: bleu
	value: 56.6
	- name: chr-F
	type: chrf
	value: 0.73211
	- task:
	name: Translation swe-por
	type: translation
	args: swe-por
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: swe-por
	metrics:
	- name: BLEU
	type: bleu
	value: 48.7
	- name: chr-F
	type: chrf
	value: 0.68146
	- task:
	name: Translation swe-spa
	type: translation
	args: swe-spa
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: swe-spa
	metrics:
	- name: BLEU
	type: bleu
	value: 55.3
	- name: chr-F
	type: chrf
	value: 0.71373
	---
	# opus-mt-tc-big-gmq-itc

	## 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 North Germanic languages (gmq) to Italic languages (itc).

	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: 2022-08-09
	- License: CC-BY-4.0
	- Language(s):
	- Source Language(s): dan isl nno nob nor swe
	- Target Language(s): cat fra glg ita lat por ron spa
	- Language Pair(s): dan-cat dan-fra dan-glg dan-ita dan-por dan-ron dan-spa isl-cat isl-fra isl-ita isl-por isl-ron isl-spa nob-cat nob-fra nob-glg nob-ita nob-por nob-ron nob-spa swe-cat swe-fra swe-glg swe-ita swe-por swe-ron swe-spa
	- Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>osp_Latn<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<<
	- Original Model: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.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 gmq-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-itc/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. `>>fra<<`

	## 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 = [
	">>spa<< Jag är inte religiös.",
	">>por<< Livet er for kort til å lære seg tysk."
	]

	model_name = "pytorch-models/opus-mt-tc-big-gmq-itc"
	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:
	# No soy religioso.
	# A vida é muito curta para aprender alemão.
	```

	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-gmq-itc")
	print(pipe(">>spa<< Jag är inte religiös."))

	# expected output: No soy religioso.
	```

	## 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_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.zip)
	- Training Scripts: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)

	## Evaluation

	* test set translations: [opusTCv20210807_transformer-big_2022-08-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.test.txt)
	* test set scores: [opusTCv20210807_transformer-big_2022-08-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.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 \|
	\|----------\|---------\|-------\|-------\|-------\|--------\|
	\| dan-fra \| tatoeba-test-v2021-08-07 \| 0.76671 \| 63.8 \| 1731 \| 11882 \|
	\| dan-ita \| tatoeba-test-v2021-08-07 \| 0.74658 \| 56.2 \| 284 \| 2226 \|
	\| dan-por \| tatoeba-test-v2021-08-07 \| 0.74944 \| 57.8 \| 873 \| 5360 \|
	\| dan-spa \| tatoeba-test-v2021-08-07 \| 0.72328 \| 54.8 \| 5000 \| 35528 \|
	\| isl-ita \| tatoeba-test-v2021-08-07 \| 0.69354 \| 51.0 \| 236 \| 1450 \|
	\| isl-spa \| tatoeba-test-v2021-08-07 \| 0.66008 \| 49.2 \| 238 \| 1229 \|
	\| nob-fra \| tatoeba-test-v2021-08-07 \| 0.70854 \| 54.4 \| 323 \| 2269 \|
	\| nob-spa \| tatoeba-test-v2021-08-07 \| 0.73672 \| 55.9 \| 885 \| 6866 \|
	\| swe-fra \| tatoeba-test-v2021-08-07 \| 0.73014 \| 59.2 \| 1407 \| 9580 \|
	\| swe-ita \| tatoeba-test-v2021-08-07 \| 0.73211 \| 56.6 \| 715 \| 4711 \|
	\| swe-por \| tatoeba-test-v2021-08-07 \| 0.68146 \| 48.7 \| 320 \| 2032 \|
	\| swe-spa \| tatoeba-test-v2021-08-07 \| 0.71373 \| 55.3 \| 1351 \| 8235 \|
	\| dan-cat \| flores101-devtest \| 0.59224 \| 33.4 \| 1012 \| 27304 \|
	\| dan-fra \| flores101-devtest \| 0.63387 \| 38.3 \| 1012 \| 28343 \|
	\| dan-glg \| flores101-devtest \| 0.54446 \| 26.4 \| 1012 \| 26582 \|
	\| dan-ita \| flores101-devtest \| 0.55237 \| 25.7 \| 1012 \| 27306 \|
	\| dan-por \| flores101-devtest \| 0.62233 \| 36.9 \| 1012 \| 26519 \|
	\| dan-ron \| flores101-devtest \| 0.58235 \| 31.8 \| 1012 \| 26799 \|
	\| dan-spa \| flores101-devtest \| 0.52453 \| 24.3 \| 1012 \| 29199 \|
	\| isl-cat \| flores101-devtest \| 0.48930 \| 22.7 \| 1012 \| 27304 \|
	\| isl-fra \| flores101-devtest \| 0.52704 \| 26.2 \| 1012 \| 28343 \|
	\| isl-glg \| flores101-devtest \| 0.45387 \| 18.0 \| 1012 \| 26582 \|
	\| isl-ita \| flores101-devtest \| 0.47303 \| 18.6 \| 1012 \| 27306 \|
	\| isl-por \| flores101-devtest \| 0.51381 \| 24.9 \| 1012 \| 26519 \|
	\| isl-ron \| flores101-devtest \| 0.48224 \| 21.6 \| 1012 \| 26799 \|
	\| isl-spa \| flores101-devtest \| 0.45786 \| 18.1 \| 1012 \| 29199 \|
	\| nob-cat \| flores101-devtest \| 0.55984 \| 28.9 \| 1012 \| 27304 \|
	\| nob-fra \| flores101-devtest \| 0.60102 \| 33.8 \| 1012 \| 28343 \|
	\| nob-glg \| flores101-devtest \| 0.52145 \| 23.4 \| 1012 \| 26582 \|
	\| nob-ita \| flores101-devtest \| 0.52619 \| 22.2 \| 1012 \| 27306 \|
	\| nob-por \| flores101-devtest \| 0.58836 \| 32.2 \| 1012 \| 26519 \|
	\| nob-ron \| flores101-devtest \| 0.54845 \| 27.6 \| 1012 \| 26799 \|
	\| nob-spa \| flores101-devtest \| 0.50661 \| 21.8 \| 1012 \| 29199 \|
	\| swe-cat \| flores101-devtest \| 0.58542 \| 32.4 \| 1012 \| 27304 \|
	\| swe-fra \| flores101-devtest \| 0.63688 \| 39.3 \| 1012 \| 28343 \|
	\| swe-glg \| flores101-devtest \| 0.53989 \| 26.0 \| 1012 \| 26582 \|
	\| swe-ita \| flores101-devtest \| 0.55232 \| 25.9 \| 1012 \| 27306 \|
	\| swe-por \| flores101-devtest \| 0.61882 \| 36.5 \| 1012 \| 26519 \|
	\| swe-ron \| flores101-devtest \| 0.57419 \| 31.0 \| 1012 \| 26799 \|
	\| swe-spa \| flores101-devtest \| 0.52175 \| 23.8 \| 1012 \| 29199 \|

	## 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: Sat Aug 13 00:00:00 EEST 2022
	* port machine: LM0-400-22516.local