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: 2024-05-30
License: Apache-2.0
Language(s):
- Source Language(s): acf arg ast cat cbk cos crs egl ext fra frm fro frp fur gcf glg hat ita kea lad lat lij lld lmo lou mfe mol mwl nap oci osp pap pcd pms por roh ron rup scn spa srd vec wln
- Target Language(s): deu eng fra por spa
- Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
Original Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip
Resources for more information:

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. >>deu<<

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:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>deu<< Replace this with text in an accepted source language.",
    ">>spa<< This is the second sentence."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-itc-deu_eng_fra_por_spa"
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) )

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-bible-big-itc-deu_eng_fra_por_spa")
print(pipe(">>deu<< Replace this with text in an accepted source language."))

Training

Data: opusTCv20230926max50+bt+jhubc (source)
Pre-processing: SentencePiece (spm32k,spm32k)
Model Type: transformer-big
Original MarianNMT Model: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip
Training Scripts: GitHub Repo

Evaluation

Model scores at the OPUS-MT dashboard
test set translations: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt
test set scores: opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
cat-deu	tatoeba-test-v2021-08-07	0.66856	47.9	723	5676
cat-eng	tatoeba-test-v2021-08-07	0.72313	57.9	1631	12627
cat-fra	tatoeba-test-v2021-08-07	0.71565	53.8	700	5664
cat-por	tatoeba-test-v2021-08-07	0.75797	58.7	747	6119
cat-spa	tatoeba-test-v2021-08-07	0.87610	77.7	1534	12094
fra-deu	tatoeba-test-v2021-08-07	0.68638	50.0	12418	100545
fra-eng	tatoeba-test-v2021-08-07	0.72664	58.0	12681	101754
fra-fra	tatoeba-test-v2021-08-07	0.62093	40.6	1000	7757
fra-por	tatoeba-test-v2021-08-07	0.70764	52.0	10518	77650
fra-spa	tatoeba-test-v2021-08-07	0.72229	55.0	10294	78406
glg-eng	tatoeba-test-v2021-08-07	0.70552	55.7	1015	8421
glg-por	tatoeba-test-v2021-08-07	0.77067	62.1	433	3105
glg-spa	tatoeba-test-v2021-08-07	0.82795	72.1	2121	17443
ita-deu	tatoeba-test-v2021-08-07	0.68325	49.4	10094	79762
ita-eng	tatoeba-test-v2021-08-07	0.81176	70.5	17320	119214
ita-fra	tatoeba-test-v2021-08-07	0.78299	64.4	10091	66377
ita-por	tatoeba-test-v2021-08-07	0.74169	55.6	3066	25668
ita-spa	tatoeba-test-v2021-08-07	0.77673	63.0	5000	34937
lad_Latn-eng	tatoeba-test-v2021-08-07	0.54247	36.7	672	3665
lad_Latn-spa	tatoeba-test-v2021-08-07	0.59790	40.4	239	1239
lat-deu	tatoeba-test-v2021-08-07	0.42548	24.8	2016	13326
lat-eng	tatoeba-test-v2021-08-07	0.42385	24.3	10298	100152
lat-spa	tatoeba-test-v2021-08-07	0.45821	25.2	3129	34036
oci-eng	tatoeba-test-v2021-08-07	0.40921	22.4	841	5299
oci-fra	tatoeba-test-v2021-08-07	0.49044	28.4	806	6302
pcd-fra	tatoeba-test-v2021-08-07	0.41500	15.0	266	1677
pms-eng	tatoeba-test-v2021-08-07	0.39308	20.8	269	2059
por-deu	tatoeba-test-v2021-08-07	0.68379	48.8	10000	81246
por-eng	tatoeba-test-v2021-08-07	0.77089	64.2	13222	105351
por-fra	tatoeba-test-v2021-08-07	0.75364	58.7	10518	80459
por-por	tatoeba-test-v2021-08-07	0.71396	50.3	2500	19220
por-spa	tatoeba-test-v2021-08-07	0.79684	65.2	10947	87335
ron-deu	tatoeba-test-v2021-08-07	0.68217	50.3	1141	7893
ron-eng	tatoeba-test-v2021-08-07	0.73059	59.0	5508	40717
ron-fra	tatoeba-test-v2021-08-07	0.70724	54.1	1925	13347
ron-por	tatoeba-test-v2021-08-07	0.73085	53.3	681	4593
ron-spa	tatoeba-test-v2021-08-07	0.73813	57.6	1959	12679
spa-deu	tatoeba-test-v2021-08-07	0.68124	49.3	10521	86430
spa-eng	tatoeba-test-v2021-08-07	0.74977	61.0	16583	138123
spa-fra	tatoeba-test-v2021-08-07	0.73392	56.6	10294	83501
spa-por	tatoeba-test-v2021-08-07	0.77280	61.1	10947	87610
spa-spa	tatoeba-test-v2021-08-07	0.68111	50.9	2500	21469
ast-deu	flores101-devtest	0.53243	24.2	1012	25094
ast-eng	flores101-devtest	0.61235	36.0	1012	24721
ast-fra	flores101-devtest	0.56687	31.2	1012	28343
ast-por	flores101-devtest	0.57033	30.6	1012	26519
ast-spa	flores101-devtest	0.49637	21.2	1012	29199
cat-fra	flores101-devtest	0.63271	38.4	1012	28343
fra-deu	flores101-devtest	0.58433	28.9	1012	25094
fra-eng	flores101-devtest	0.67826	43.3	1012	24721
glg-deu	flores101-devtest	0.56897	27.1	1012	25094
glg-spa	flores101-devtest	0.53183	24.2	1012	29199
ita-por	flores101-devtest	0.57961	28.4	1012	26519
kea-deu	flores101-devtest	0.48105	18.3	1012	25094
kea-eng	flores101-devtest	0.60362	35.0	1012	24721
kea-por	flores101-devtest	0.57808	29.0	1012	26519
kea-spa	flores101-devtest	0.46648	17.6	1012	29199
oci-deu	flores101-devtest	0.57391	28.0	1012	25094
oci-eng	flores101-devtest	0.72351	49.4	1012	24721
por-eng	flores101-devtest	0.70724	47.4	1012	24721
por-fra	flores101-devtest	0.64103	39.2	1012	28343
por-spa	flores101-devtest	0.53268	25.0	1012	29199
ron-deu	flores101-devtest	0.57980	28.1	1012	25094
ron-eng	flores101-devtest	0.67583	41.6	1012	24721
ron-spa	flores101-devtest	0.53082	24.3	1012	29199
spa-fra	flores101-devtest	0.57039	27.1	1012	28343
spa-por	flores101-devtest	0.55607	25.0	1012	26519
ast-deu	flores200-devtest	0.53776	24.8	1012	25094
ast-eng	flores200-devtest	0.61482	36.8	1012	24721
ast-fra	flores200-devtest	0.56504	31.3	1012	28343
ast-por	flores200-devtest	0.57158	31.1	1012	26519
ast-spa	flores200-devtest	0.49579	21.2	1012	29199
cat-deu	flores200-devtest	0.58203	29.2	1012	25094
cat-eng	flores200-devtest	0.69165	44.6	1012	24721
cat-fra	flores200-devtest	0.63612	38.9	1012	28343
cat-por	flores200-devtest	0.62911	37.7	1012	26519
cat-spa	flores200-devtest	0.53320	24.6	1012	29199
fra-deu	flores200-devtest	0.58592	29.1	1012	25094
fra-eng	flores200-devtest	0.68067	43.8	1012	24721
fra-por	flores200-devtest	0.62388	37.0	1012	26519
fra-spa	flores200-devtest	0.52983	24.4	1012	29199
fur-deu	flores200-devtest	0.51969	21.8	1012	25094
fur-eng	flores200-devtest	0.60793	34.3	1012	24721
fur-fra	flores200-devtest	0.56989	30.0	1012	28343
fur-por	flores200-devtest	0.56207	29.3	1012	26519
fur-spa	flores200-devtest	0.48436	20.0	1012	29199
glg-deu	flores200-devtest	0.57369	27.7	1012	25094
glg-eng	flores200-devtest	0.66358	40.0	1012	24721
glg-fra	flores200-devtest	0.62487	36.5	1012	28343
glg-por	flores200-devtest	0.60267	32.7	1012	26519
glg-spa	flores200-devtest	0.53227	24.3	1012	29199
hat-deu	flores200-devtest	0.49916	19.1	1012	25094
hat-eng	flores200-devtest	0.59656	32.5	1012	24721
hat-fra	flores200-devtest	0.61574	35.4	1012	28343
hat-por	flores200-devtest	0.55195	27.7	1012	26519
hat-spa	flores200-devtest	0.47382	18.4	1012	29199
ita-deu	flores200-devtest	0.55779	24.1	1012	25094
ita-eng	flores200-devtest	0.61563	32.2	1012	24721
ita-fra	flores200-devtest	0.60210	31.2	1012	28343
ita-por	flores200-devtest	0.58279	28.8	1012	26519
ita-spa	flores200-devtest	0.52348	23.2	1012	29199
kea-deu	flores200-devtest	0.49089	19.3	1012	25094
kea-eng	flores200-devtest	0.60553	35.5	1012	24721
kea-fra	flores200-devtest	0.54027	26.6	1012	28343
kea-por	flores200-devtest	0.57696	28.9	1012	26519
kea-spa	flores200-devtest	0.46974	18.0	1012	29199
lij-deu	flores200-devtest	0.51695	22.7	1012	25094
lij-eng	flores200-devtest	0.62347	36.2	1012	24721
lij-fra	flores200-devtest	0.57498	31.4	1012	28343
lij-por	flores200-devtest	0.56183	29.4	1012	26519
lij-spa	flores200-devtest	0.48038	20.0	1012	29199
lmo-deu	flores200-devtest	0.45516	15.4	1012	25094
lmo-eng	flores200-devtest	0.53540	25.5	1012	24721
lmo-fra	flores200-devtest	0.50076	22.2	1012	28343
lmo-por	flores200-devtest	0.50134	22.9	1012	26519
lmo-spa	flores200-devtest	0.44053	16.2	1012	29199
oci-deu	flores200-devtest	0.57822	28.7	1012	25094
oci-eng	flores200-devtest	0.73030	50.7	1012	24721
oci-fra	flores200-devtest	0.64900	39.7	1012	28343
oci-por	flores200-devtest	0.63318	36.9	1012	26519
oci-spa	flores200-devtest	0.52269	22.9	1012	29199
pap-deu	flores200-devtest	0.53166	23.2	1012	25094
pap-eng	flores200-devtest	0.68541	44.6	1012	24721
pap-fra	flores200-devtest	0.57224	30.5	1012	28343
pap-por	flores200-devtest	0.59064	33.2	1012	26519
pap-spa	flores200-devtest	0.49601	21.7	1012	29199
por-deu	flores200-devtest	0.59047	30.3	1012	25094
por-eng	flores200-devtest	0.71096	48.0	1012	24721
por-fra	flores200-devtest	0.64555	40.1	1012	28343
por-spa	flores200-devtest	0.53400	25.1	1012	29199
ron-deu	flores200-devtest	0.58428	28.7	1012	25094
ron-eng	flores200-devtest	0.67719	41.8	1012	24721
ron-fra	flores200-devtest	0.63678	37.6	1012	28343
ron-por	flores200-devtest	0.62371	36.1	1012	26519
ron-spa	flores200-devtest	0.53150	24.5	1012	29199
scn-deu	flores200-devtest	0.48102	19.2	1012	25094
scn-eng	flores200-devtest	0.55782	29.6	1012	24721
scn-fra	flores200-devtest	0.52773	26.1	1012	28343
scn-por	flores200-devtest	0.51894	25.2	1012	26519
scn-spa	flores200-devtest	0.45724	17.9	1012	29199
spa-deu	flores200-devtest	0.53451	21.5	1012	25094
spa-eng	flores200-devtest	0.58896	28.5	1012	24721
spa-fra	flores200-devtest	0.57406	27.6	1012	28343
spa-por	flores200-devtest	0.55749	25.2	1012	26519
srd-deu	flores200-devtest	0.49238	19.9	1012	25094
srd-eng	flores200-devtest	0.59392	34.2	1012	24721
srd-fra	flores200-devtest	0.54003	27.6	1012	28343
srd-por	flores200-devtest	0.53842	27.9	1012	26519
srd-spa	flores200-devtest	0.46002	18.2	1012	29199
vec-deu	flores200-devtest	0.48795	19.3	1012	25094
vec-eng	flores200-devtest	0.56840	30.7	1012	24721
vec-fra	flores200-devtest	0.54164	27.3	1012	28343
vec-por	flores200-devtest	0.53482	26.2	1012	26519
vec-spa	flores200-devtest	0.46588	18.4	1012	29199
fra-deu	generaltest2022	0.66476	42.4	2006	37696
fra-deu	multi30k_test_2016_flickr	0.61797	32.6	1000	12106
fra-eng	multi30k_test_2016_flickr	0.66271	47.2	1000	12955
fra-deu	multi30k_test_2017_flickr	0.59701	29.4	1000	10755
fra-eng	multi30k_test_2017_flickr	0.69422	50.3	1000	11374
fra-deu	multi30k_test_2017_mscoco	0.55509	25.7	461	5158
fra-eng	multi30k_test_2017_mscoco	0.67791	48.7	461	5231
fra-deu	multi30k_test_2018_flickr	0.55237	24.0	1071	13703
fra-eng	multi30k_test_2018_flickr	0.64722	43.8	1071	14689
fra-eng	newsdiscusstest2015	0.61385	38.4	1500	26982
fra-deu	newssyscomb2009	0.53530	23.7	502	11271
fra-eng	newssyscomb2009	0.57297	31.3	502	11818
fra-spa	newssyscomb2009	0.60233	34.1	502	12503
ita-deu	newssyscomb2009	0.53590	22.4	502	11271
ita-eng	newssyscomb2009	0.59976	34.8	502	11818
ita-fra	newssyscomb2009	0.61232	33.5	502	12331
ita-spa	newssyscomb2009	0.60782	35.3	502	12503
spa-deu	newssyscomb2009	0.52853	21.8	502	11271
spa-eng	newssyscomb2009	0.57347	31.0	502	11818
spa-fra	newssyscomb2009	0.61436	34.3	502	12331
fra-deu	newstest2008	0.53180	22.9	2051	47447
fra-eng	newstest2008	0.54379	26.5	2051	49380
fra-spa	newstest2008	0.58804	33.1	2051	52586
spa-deu	newstest2008	0.52221	21.6	2051	47447
spa-eng	newstest2008	0.55331	27.9	2051	49380
spa-fra	newstest2008	0.58769	32.0	2051	52685
fra-deu	newstest2009	0.52771	22.5	2525	62816
fra-eng	newstest2009	0.56679	30.2	2525	65399
fra-spa	newstest2009	0.58921	32.1	2525	68111
ita-deu	newstest2009	0.53022	22.8	2525	62816
ita-eng	newstest2009	0.59309	33.8	2525	65399
ita-fra	newstest2009	0.59309	32.0	2525	69263
ita-spa	newstest2009	0.59760	33.5	2525	68111
spa-deu	newstest2009	0.52822	22.3	2525	62816
spa-eng	newstest2009	0.56989	30.4	2525	65399
spa-fra	newstest2009	0.59150	32.2	2525	69263
fra-deu	newstest2010	0.53765	24.0	2489	61503
fra-eng	newstest2010	0.59251	32.6	2489	61711
fra-spa	newstest2010	0.62480	37.6	2489	65480
spa-deu	newstest2010	0.55161	26.0	2489	61503
spa-eng	newstest2010	0.61562	36.3	2489	61711
spa-fra	newstest2010	0.62021	35.7	2489	66022
fra-deu	newstest2011	0.53025	23.1	3003	72981
fra-eng	newstest2011	0.59636	32.9	3003	74681
fra-spa	newstest2011	0.63203	39.9	3003	79476
spa-deu	newstest2011	0.52934	23.3	3003	72981
spa-eng	newstest2011	0.59606	33.8	3003	74681
spa-fra	newstest2011	0.61079	34.9	3003	80626
fra-deu	newstest2012	0.52957	24.0	3003	72886
fra-eng	newstest2012	0.59352	33.6	3003	72812
fra-spa	newstest2012	0.62641	39.2	3003	79006
spa-deu	newstest2012	0.53519	24.6	3003	72886
spa-eng	newstest2012	0.62284	37.4	3003	72812
spa-fra	newstest2012	0.61076	33.8	3003	78011
fra-deu	newstest2013	0.54167	25.4	3000	63737
fra-eng	newstest2013	0.59236	34.0	3000	64505
fra-spa	newstest2013	0.59347	34.9	3000	70528
spa-deu	newstest2013	0.55130	26.3	3000	63737
spa-eng	newstest2013	0.60681	34.6	3000	64505
spa-fra	newstest2013	0.59816	33.2	3000	70037
fra-eng	newstest2014	0.63499	37.9	3003	70708
ron-eng	newstest2016	0.63996	39.5	1999	47562
fra-deu	newstest2019	0.60468	28.6	1701	36446
fra-deu	newstest2020	0.61401	28.8	1619	30265
fra-deu	newstest2021	0.65950	39.5	1026	26077
cat-deu	ntrex128	0.54096	24.0	1997	48761
cat-eng	ntrex128	0.63516	36.5	1997	47673
cat-fra	ntrex128	0.56385	28.1	1997	53481
cat-por	ntrex128	0.56246	28.7	1997	51631
cat-spa	ntrex128	0.61311	35.8	1997	54107
fra-deu	ntrex128	0.53059	23.4	1997	48761
fra-eng	ntrex128	0.61285	34.7	1997	47673
fra-por	ntrex128	0.54075	25.8	1997	51631
fra-spa	ntrex128	0.56863	30.6	1997	54107
glg-deu	ntrex128	0.53724	23.6	1997	48761
glg-eng	ntrex128	0.64481	38.7	1997	47673
glg-fra	ntrex128	0.55856	27.8	1997	53481
glg-por	ntrex128	0.56322	28.7	1997	51631
glg-spa	ntrex128	0.61794	36.8	1997	54107
ita-deu	ntrex128	0.54678	25.0	1997	48761
ita-eng	ntrex128	0.64636	39.2	1997	47673
ita-fra	ntrex128	0.57428	30.0	1997	53481
ita-por	ntrex128	0.56858	29.7	1997	51631
ita-spa	ntrex128	0.58886	33.0	1997	54107
por-deu	ntrex128	0.54833	24.6	1997	48761
por-eng	ntrex128	0.65223	39.7	1997	47673
por-fra	ntrex128	0.56793	28.9	1997	53481
por-spa	ntrex128	0.59218	33.8	1997	54107
ron-deu	ntrex128	0.53249	22.4	1997	48761
ron-eng	ntrex128	0.61807	33.8	1997	47673
ron-fra	ntrex128	0.55575	26.4	1997	53481
ron-por	ntrex128	0.55086	27.2	1997	51631
ron-spa	ntrex128	0.57787	31.9	1997	54107
spa-deu	ntrex128	0.54309	23.8	1997	48761
spa-eng	ntrex128	0.64416	37.4	1997	47673
spa-fra	ntrex128	0.57320	29.4	1997	53481
spa-por	ntrex128	0.56751	29.0	1997	51631
fra-eng	tico19-test	0.62364	39.7	2100	56323
fra-por	tico19-test	0.58563	34.2	2100	62729
fra-spa	tico19-test	0.59556	36.5	2100	66563
por-eng	tico19-test	0.74420	51.8	2100	56315
por-fra	tico19-test	0.60081	34.5	2100	64661
por-spa	tico19-test	0.68156	44.8	2100	66563
spa-eng	tico19-test	0.73454	50.3	2100	56315
spa-fra	tico19-test	0.60441	34.9	2100	64661
spa-por	tico19-test	0.67749	42.7	2100	62729

Citation Information

Publications: Democratizing neural machine translation with OPUS-MT and 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.)

bibtex

@article{tiedemann2023democratizing,
  title={Democratizing neural machine translation with {OPUS-MT}},
  author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
  journal={Language Resources and Evaluation},
  number={58},
  pages={713--755},
  year={2023},
  publisher={Springer Nature},
  issn={1574-0218},
  doi={10.1007/s10579-023-09704-w}
}

@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 HPLT project, funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland, and the EuroHPC supercomputer LUMI.

Model conversion info

transformers version: 4.45.1
OPUS-MT git hash: 0882077
port time: Tue Oct 8 11:57:19 EEST 2024
port machine: LM0-400-22516.local

WindstormLabs/origin-Helsinki-NLP-opus-mt-tc-bible-big-itc-deu_eng_fra_por_spa

opus-mt-tc-bible-big-itc-deu_eng_fra_por_spa

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Model Details