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): deu eng fra por spa
- Target Language(s): dan fao isl nno nob non nor swe
- Valid Target Language Labels: >>dan<< >>fao<< >>isl<< >>jut<< >>nno<< >>nob<< >>non<< >>nor<< >>nrn<< >>ovd<< >>rmg<< >>swe<< >>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. >>dan<<

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

model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-gmq"
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-deu_eng_fra_por_spa-gmq")
print(pipe(">>dan<< 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
deu-dan	tatoeba-test-v2021-08-07	0.74051	57.8	9998	74644
deu-isl	tatoeba-test-v2021-08-07	0.61256	31.7	969	5951
deu-nob	tatoeba-test-v2021-08-07	0.71413	52.9	3525	31978
deu-nor	tatoeba-test-v2021-08-07	0.71253	52.7	3651	32928
deu-swe	tatoeba-test-v2021-08-07	0.72650	58.2	3410	22701
eng-dan	tatoeba-test-v2021-08-07	0.74708	60.6	10795	79385
eng-fao	tatoeba-test-v2021-08-07	0.48304	29.0	294	1933
eng-isl	tatoeba-test-v2021-08-07	0.58312	33.2	2503	19023
eng-nno	tatoeba-test-v2021-08-07	0.62606	42.7	460	3428
eng-nob	tatoeba-test-v2021-08-07	0.72340	57.4	4539	36119
eng-nor	tatoeba-test-v2021-08-07	0.71514	56.2	5000	39552
eng-swe	tatoeba-test-v2021-08-07	0.73720	60.5	10362	68067
fra-dan	tatoeba-test-v2021-08-07	0.78018	64.1	1731	11312
fra-nob	tatoeba-test-v2021-08-07	0.74252	59.1	323	2175
fra-nor	tatoeba-test-v2021-08-07	0.74407	60.3	477	3097
fra-swe	tatoeba-test-v2021-08-07	0.75644	62.1	1407	9170
por-dan	tatoeba-test-v2021-08-07	0.79528	65.6	873	5258
por-nor	tatoeba-test-v2021-08-07	0.73559	58.0	481	4030
por-swe	tatoeba-test-v2021-08-07	0.75566	60.2	320	1938
spa-dan	tatoeba-test-v2021-08-07	0.73310	57.7	5000	35937
spa-isl	tatoeba-test-v2021-08-07	0.52169	18.7	238	1220
spa-nob	tatoeba-test-v2021-08-07	0.76501	60.9	885	6762
spa-nor	tatoeba-test-v2021-08-07	0.75815	60.1	960	7217
spa-swe	tatoeba-test-v2021-08-07	0.74222	60.7	1351	8357
deu-dan	flores101-devtest	0.62006	34.8	1012	24638
deu-isl	flores101-devtest	0.48236	18.8	1012	22834
deu-swe	flores101-devtest	0.61778	33.7	1012	23121
eng-swe	flores101-devtest	0.69435	45.5	1012	23121
fra-dan	flores101-devtest	0.61019	34.0	1012	24638
fra-isl	flores101-devtest	0.47647	18.1	1012	22834
fra-swe	flores101-devtest	0.60354	32.2	1012	23121
por-isl	flores101-devtest	0.47937	19.1	1012	22834
por-swe	flores101-devtest	0.60857	33.1	1012	23121
spa-dan	flores101-devtest	0.54890	24.4	1012	24638
spa-nob	flores101-devtest	0.50610	18.3	1012	23873
spa-swe	flores101-devtest	0.54011	22.4	1012	23121
deu-dan	flores200-devtest	0.62152	35.1	1012	24638
deu-isl	flores200-devtest	0.48648	19.1	1012	22834
deu-nno	flores200-devtest	0.53530	24.0	1012	24316
deu-nob	flores200-devtest	0.55748	25.1	1012	23873
deu-swe	flores200-devtest	0.62138	34.2	1012	23121
eng-dan	flores200-devtest	0.70321	47.0	1012	24638
eng-isl	flores200-devtest	0.52585	24.4	1012	22834
eng-nno	flores200-devtest	0.61372	33.8	1012	24316
eng-nob	flores200-devtest	0.62508	34.4	1012	23873
eng-swe	flores200-devtest	0.69703	46.0	1012	23121
fra-dan	flores200-devtest	0.61025	34.1	1012	24638
fra-isl	flores200-devtest	0.48273	18.8	1012	22834
fra-nno	flores200-devtest	0.53032	24.3	1012	24316
fra-nob	flores200-devtest	0.54933	25.0	1012	23873
fra-swe	flores200-devtest	0.60612	32.8	1012	23121
por-dan	flores200-devtest	0.62221	36.2	1012	24638
por-isl	flores200-devtest	0.48357	19.6	1012	22834
por-nno	flores200-devtest	0.54369	26.3	1012	24316
por-nob	flores200-devtest	0.56054	26.4	1012	23873
por-swe	flores200-devtest	0.61388	34.1	1012	23121
spa-dan	flores200-devtest	0.55091	24.7	1012	24638
spa-isl	flores200-devtest	0.44469	14.2	1012	22834
spa-nno	flores200-devtest	0.48898	18.6	1012	24316
spa-nob	flores200-devtest	0.50901	18.8	1012	23873
spa-swe	flores200-devtest	0.54182	22.7	1012	23121
eng-isl	newstest2021	0.51196	21.9	1000	25233
deu-dan	ntrex128	0.56412	29.1	1997	47643
deu-isl	ntrex128	0.48309	18.8	1997	46643
deu-nno	ntrex128	0.51535	22.0	1997	46512
deu-nob	ntrex128	0.56152	27.6	1997	45501
deu-swe	ntrex128	0.58061	29.6	1997	44889
eng-dan	ntrex128	0.61894	37.6	1997	47643
eng-isl	ntrex128	0.52027	23.9	1997	46643
eng-nno	ntrex128	0.60754	34.0	1997	46512
eng-nob	ntrex128	0.62327	36.9	1997	45501
eng-swe	ntrex128	0.66129	41.3	1997	44889
fra-dan	ntrex128	0.54102	27.1	1997	47643
fra-isl	ntrex128	0.47296	18.4	1997	46643
fra-nno	ntrex128	0.50532	21.6	1997	46512
fra-nob	ntrex128	0.54026	25.7	1997	45501
fra-swe	ntrex128	0.56278	27.9	1997	44889
por-dan	ntrex128	0.56288	30.0	1997	47643
por-isl	ntrex128	0.47577	17.8	1997	46643
por-nno	ntrex128	0.52158	23.0	1997	46512
por-nob	ntrex128	0.55788	27.4	1997	45501
por-swe	ntrex128	0.57790	29.3	1997	44889
spa-dan	ntrex128	0.55607	27.5	1997	47643
spa-isl	ntrex128	0.48566	18.4	1997	46643
spa-nno	ntrex128	0.51741	22.2	1997	46512
spa-nob	ntrex128	0.55824	26.8	1997	45501
spa-swe	ntrex128	0.57851	28.8	1997	44889

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 10:03:29 EEST 2024
port machine: LM0-400-22516.local

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

opus-mt-tc-bible-big-deu_eng_fra_por_spa-gmq

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Model Details