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opus-mt-tc-bible-big-deu_eng_fra_por_spa-bnt

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opus-mt-tc-bible-big-deu_eng_fra_por_spa-bnt

Table of Contents

Model Details

Neural machine translation model for translating from unknown (deu+eng+fra+por+spa) to Bantu languages (bnt).

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): bas bem bnt bss cce cjk cwe dig dug gog gwr hay heh her jmc kam kdc kdn kik kin kki kkj kmb kng kon ksb kua ldi lem lin lon lsm lua lug luy mcp myx nbl nde ndo nim nnb nso nuj nya nyf nyn nyo nyy old ozm pkb rim run seh sna sot ssw suk swa swc swh sxb thk tlj toh toi tsn tso tum umb ven vmw vun wmw xho xog zul
    • Valid Target Language Labels: >>abb<< >>agh<< >>akw<< >>asa<< >>auh<< >>axk<< >>baf<< >>bag<< >>bas<< >>bbg<< >>bbi<< >>bbm<< >>bcp<< >>bdp<< >>bdu<< >>beb<< >>bem<< >>beq<< >>bez<< >>bhy<< >>bip<< >>biw<< >>biz<< >>bja<< >>bkf<< >>bkh<< >>bkj<< >>bkp<< >>bkt<< >>bkw<< >>bli<< >>blv<< >>bmb<< >>bmg<< >>bml<< >>bmw<< >>bng<< >>bni<< >>bnm<< >>bnt_Latn<< >>bnx<< >>boh<< >>bok<< >>bou<< >>boy<< >>bpj<< >>bqm<< >>bqu<< >>bqz<< >>brf<< >>bri<< >>brl<< >>bsi<< >>bss<< >>btb<< >>btc<< >>buf<< >>bui<< >>bum<< >>buu<< >>buw<< >>bvb<< >>bvg<< >>bvx<< >>bwc<< >>bwg<< >>bwl<< >>bws<< >>bwt<< >>bww<< >>bwz<< >>bxc<< >>bxg<< >>bxp<< >>byi<< >>bzm<< >>bzo<< >>cce<< >>ccl<< >>cgg<< >>chw<< >>cjk<< >>cjk_Latn<< >>coh<< >>cuh<< >>cwa<< >>cwb<< >>cwe<< >>dav<< >>dde<< >>dez<< >>dhm<< >>dhs<< >>dig<< >>dii<< >>diu<< >>diz<< >>dma<< >>dmx<< >>dne<< >>doe<< >>dov<< >>dua<< >>dug<< >>dzn<< >>ebo<< >>ebu<< >>ekm<< >>eko<< >>eto<< >>ewo<< >>fan<< >>fip<< >>flr<< >>fwe<< >>gev<< >>gey<< >>gmx<< >>gog<< >>guz<< >>gwe<< >>gwr<< >>gyi<< >>han<< >>haq<< >>hav<< >>hay<< >>hba<< >>heh<< >>hem<< >>her<< >>hij<< >>hka<< >>hke<< >>hol<< >>hom<< >>hoo<< >>hum<< >>ifm<< >>ikz<< >>ilb<< >>isn<< >>iyx<< >>jgb<< >>jit<< >>jmc<< >>job<< >>kam<< >>kbj<< >>kbs<< >>kck<< >>kcu<< >>kcv<< >>kcw<< >>kcz<< >>kdc<< >>kde<< >>kdg<< >>kdn<< >>keb<< >>ked<< >>khu<< >>khx<< >>khy<< >>kik<< >>kin<< >>kiv<< >>kiz<< >>kki<< >>kkj<< >>kkq<< >>kkw<< >>kmb<< >>kme<< >>kmw<< >>kng<< >>kny<< >>koh<< >>kon<< >>koo<< >>koq<< >>kqn<< >>ksb<< >>ksf<< >>ksv<< >>ktf<< >>ktu<< >>kty<< >>kua<< >>kuj<< >>kwc<< >>kwm<< >>kwn<< >>kws<< >>kwu<< >>kxx<< >>kya<< >>kzn<< >>kzo<< >>kzy<< >>lag<< >>lai<< >>lam<< >>lch<< >>ldi<< >>lea<< >>leb<< >>leh<< >>lej<< >>lel<< >>lem<< >>leo<< >>lfa<< >>lgm<< >>lgz<< >>lie<< >>lik<< >>lin<< >>liz<< >>lke<< >>llb<< >>lli<< >>lnb<< >>lol<< >>lon<< >>loo<< >>loq<< >>loz<< >>lse<< >>lsm<< >>lua<< >>lub<< >>lue<< >>lug<< >>luj<< >>lum<< >>lun<< >>lup<< >>luy<< >>lwa<< >>lyn<< >>mbm<< >>mbo<< >>mck<< >>mcp<< >>mcx<< >>mdn<< >>mdp<< >>mdq<< >>mdt<< >>mdu<< >>mdw<< >>mer<< >>mfu<< >>mgg<< >>mgh<< >>mgq<< >>mgr<< >>mgs<< >>mgv<< >>mgw<< >>mgy<< >>mgz<< >>mhb<< >>mhm<< >>mho<< >>mhw<< >>mjh<< >>mkk<< >>mkw<< >>mlb<< >>mlk<< >>mmu<< >>mmz<< >>mny<< >>mow<< >>mpa<< >>mvw<< >>mwe<< >>mwn<< >>mws<< >>mwz<< >>mxc<< >>mxg<< >>mxo<< >>myc<< >>mye<< >>myx<< >>mzd<< >>nba<< >>nbd<< >>nbl<< >>nda<< >>ndc<< >>nde<< >>ndg<< >>ndh<< >>ndj<< >>ndk<< >>ndl<< >>ndn<< >>ndo<< >>ndq<< >>ndw<< >>ngc<< >>ngd<< >>ngl<< >>ngo<< >>ngp<< >>ngq<< >>ngy<< >>ngz<< >>nih<< >>nim<< >>nix<< >>njx<< >>njy<< >>nka<< >>nkc<< >>nkn<< >>nkt<< >>nkv<< >>nkw<< >>nlj<< >>nlo<< >>nmd<< >>nmg<< >>nmq<< >>nnb<< >>nnb_Latn<< >>nne<< >>nnq<< >>noq<< >>now<< >>nql<< >>nra<< >>nse<< >>nso<< >>nsx<< >>nte<< >>ntk<< >>nto<< >>nui<< >>nuj<< >>nvo<< >>nxd<< >>nxi<< >>nxo<< >>nya<< >>nyc<< >>nye<< >>nyf<< >>nyg<< >>nyj<< >>nyk<< >>nym<< >>nyn<< >>nyo<< >>nyr<< >>nyu<< >>nyy<< >>nzb<< >>nzd<< >>old<< >>olu<< >>oml<< >>ozm<< >>pae<< >>pbr<< >>pem<< >>phm<< >>pic<< >>piw<< >>pkb<< >>pmm<< >>pof<< >>poy<< >>puu<< >>reg<< >>rim<< >>rnd<< >>rng<< >>rnw<< >>rof<< >>rub<< >>ruc<< >>ruf<< >>run<< >>rwk<< >>rwm<< >>sak<< >>sbk<< >>sbm<< >>sbp<< >>sbs<< >>sbw<< >>sby<< >>sdj<< >>seg<< >>seh<< >>sgm<< >>shc<< >>shq<< >>shr<< >>sie<< >>skt<< >>slx<< >>smd<< >>smx<< >>sna<< >>sng<< >>snq<< >>soc<< >>sod<< >>soe<< >>soo<< >>sop<< >>sot<< >>sox<< >>soz<< >>ssc<< >>ssw<< >>sub<< >>suj<< >>suk<< >>suw<< >>swa<< >>swb<< >>swc<< >>swh<< >>swj<< >>swk<< >>sxb<< >>sxe<< >>syi<< >>syx<< >>szg<< >>szv<< >>tap<< >>tbt<< >>tck<< >>teg<< >>tek<< >>tga<< >>thk<< >>tii<< >>tke<< >>tlj<< >>tll<< >>tmv<< >>tny<< >>tog<< >>toh<< >>toi<< >>toi_Latn<< >>tsa<< >>tsc<< >>tsn<< >>tso<< >>tsv<< >>ttf<< >>ttj<< >>ttl<< >>tum<< >>tvs<< >>tvu<< >>twl<< >>two<< >>twx<< >>tyi<< >>tyx<< >>ukh<< >>umb<< >>vau<< >>ven<< >>vid<< >>vif<< >>vin<< >>vmk<< >>vmr<< >>vmw<< >>vum<< >>vun<< >>wbh<< >>wbi<< >>wdd<< >>wlc<< >>wmw<< >>wni<< >>won<< >>wum<< >>wun<< >>xdo<< >>xho<< >>xku<< >>xkv<< >>xma<< >>xmc<< >>xog<< >>xsq<< >>yaf<< >>yao<< >>yas<< >>yat<< >>yav<< >>yel<< >>yey<< >>yko<< >>ymk<< >>yns<< >>yom<< >>zaj<< >>zak<< >>zdj<< >>zga<< >>zin<< >>zmb<< >>zmf<< >>zmn<< >>zmp<< >>zmq<< >>zms<< >>zmw<< >>zmx<< >>zul<<
  • 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. >>bas<<

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

model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bnt"
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:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bnt")
print(pipe(">>bas<< Replace this with text in an accepted source language."))

Training

Evaluation

langpair testset chr-F BLEU #sent #words
eng-run tatoeba-test-v2021-08-07 0.44207 11.8 1703 6710
eng-swa tatoeba-test-v2021-08-07 0.60298 32.7 387 1888
fra-run tatoeba-test-v2021-08-07 0.42664 11.2 1274 5081
spa-run tatoeba-test-v2021-08-07 0.41921 10.5 963 3886
eng-lin flores101-devtest 0.43748 13.2 1012 26769
eng-nso flores101-devtest 0.47122 19.4 1012 31298
eng-sna flores101-devtest 0.44294 9.4 1012 20105
eng-xho flores101-devtest 0.50110 11.6 1012 18227
fra-sna flores101-devtest 0.40676 6.2 1012 20105
por-lin flores101-devtest 0.41675 10.7 1012 26769
spa-lin flores101-devtest 0.40631 8.8 1012 26769
deu-lin flores200-devtest 0.40763 9.9 1012 26769
deu-xho flores200-devtest 0.40586 4.8 1012 18227
eng-kin flores200-devtest 0.41492 11.1 1012 22774
eng-lin flores200-devtest 0.45568 14.7 1012 26769
eng-nso flores200-devtest 0.48626 20.8 1012 31298
eng-nya flores200-devtest 0.45067 10.7 1012 22180
eng-sna flores200-devtest 0.45629 10.1 1012 20105
eng-sot flores200-devtest 0.45331 15.4 1012 31600
eng-ssw flores200-devtest 0.43635 7.1 1012 18508
eng-tsn flores200-devtest 0.45233 17.7 1012 33831
eng-tso flores200-devtest 0.48529 18.3 1012 29548
eng-xho flores200-devtest 0.51974 13.1 1012 18227
eng-zul flores200-devtest 0.53320 14.0 1012 18556
fra-lin flores200-devtest 0.44410 13.0 1012 26769
fra-sna flores200-devtest 0.42053 6.9 1012 20105
fra-xho flores200-devtest 0.44537 7.1 1012 18227
fra-zul flores200-devtest 0.41291 5.7 1012 18556
por-lin flores200-devtest 0.42944 11.7 1012 26769
por-xho flores200-devtest 0.41363 5.8 1012 18227
spa-lin flores200-devtest 0.41938 9.4 1012 26769
deu-swa ntrex128 0.48979 18.0 1997 46859
deu-tsn ntrex128 0.41894 15.4 1997 71271
eng-nya ntrex128 0.46801 14.9 1997 43727
eng-ssw ntrex128 0.42880 6.7 1997 36169
eng-swa ntrex128 0.60117 33.4 1997 46859
eng-tsn ntrex128 0.46599 22.2 1997 71271
eng-xho ntrex128 0.48847 11.2 1997 35439
eng-zul ntrex128 0.49764 10.7 1997 34438
fra-swa ntrex128 0.45494 17.5 1997 46859
fra-tsn ntrex128 0.41426 15.3 1997 71271
fra-xho ntrex128 0.41206 5.2 1997 35439
por-swa ntrex128 0.46465 18.0 1997 46859
por-tsn ntrex128 0.40236 14.5 1997 71271
por-xho ntrex128 0.40070 5.0 1997 35439
spa-swa ntrex128 0.46670 18.1 1997 46859
spa-tsn ntrex128 0.40263 14.2 1997 71271
spa-xho ntrex128 0.40247 4.9 1997 35439
eng-kin tico19-test 0.40952 11.3 2100 55034
eng-lin tico19-test 0.44670 15.5 2100 61116
eng-swa tico19-test 0.56798 28.0 2100 58846
eng-zul tico19-test 0.53624 14.4 2100 44098
fra-swa tico19-test 0.44926 16.8 2100 58846
fra-zul tico19-test 0.40588 6.0 2100 44098
por-lin tico19-test 0.41729 12.5 2100 61116
por-swa tico19-test 0.49303 19.6 2100 58846
spa-lin tico19-test 0.41645 12.1 2100 61116
spa-swa tico19-test 0.48614 18.8 2100 58846
spa-zul tico19-test 0.40058 5.3 2100 44098

Citation Information 

@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 09:00:33 EEST 2024
  • port machine: LM0-400-22516.local
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