--- library_name: transformers language: - aa - am - ar - arc - bcw - byn - cop - daa - de - dsh - en - es - fr - gde - gnd - ha - hbo - he - hig - irk - jpa - kab - ker - kqp - ktb - kxc - lln - lme - meq - mfh - mfi - mfk - mif - mpg - mqb - mt - muy - oar - om - pbi - phn - pt - rif - sgw - shi - shy - so - sur - syc - syr - taq - thv - ti - tig - tmc - tmh - tmr - ttr - tzm - wal - xed - zgh tags: - translation - opus-mt-tc-bible license: apache-2.0 model-index: - name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa results: - task: name: Translation deu-hau type: translation args: deu-hau dataset: name: flores200-devtest type: flores200-devtest args: deu-hau metrics: - name: BLEU type: bleu value: 11.4 - name: chr-F type: chrf value: 0.40471 - task: name: Translation deu-heb type: translation args: deu-heb dataset: name: flores200-devtest type: flores200-devtest args: deu-heb metrics: - name: BLEU type: bleu value: 18.1 - name: chr-F type: chrf value: 0.48645 - task: name: Translation deu-mlt type: translation args: deu-mlt dataset: name: flores200-devtest type: flores200-devtest args: deu-mlt metrics: - name: BLEU type: bleu value: 17.5 - name: chr-F type: chrf value: 0.54079 - task: name: Translation eng-arz type: translation args: eng-arz dataset: name: flores200-devtest type: flores200-devtest args: eng-arz metrics: - name: BLEU type: bleu value: 11.1 - name: chr-F type: chrf value: 0.42804 - task: name: Translation eng-hau type: translation args: eng-hau dataset: name: flores200-devtest type: flores200-devtest args: eng-hau metrics: - name: BLEU type: bleu value: 20.4 - name: chr-F type: chrf value: 0.49023 - task: name: Translation eng-heb type: translation args: eng-heb dataset: name: flores200-devtest type: flores200-devtest args: eng-heb metrics: - name: BLEU type: bleu value: 27.1 - name: chr-F type: chrf value: 0.56635 - task: name: Translation eng-mlt type: translation args: eng-mlt dataset: name: flores200-devtest type: flores200-devtest args: eng-mlt metrics: - name: BLEU type: bleu value: 34.9 - name: chr-F type: chrf value: 0.68334 - task: name: Translation fra-hau type: translation args: fra-hau dataset: name: flores200-devtest type: flores200-devtest args: fra-hau metrics: - name: BLEU type: bleu value: 13.2 - name: chr-F type: chrf value: 0.42731 - task: name: Translation fra-heb type: translation args: fra-heb dataset: name: flores200-devtest type: flores200-devtest args: fra-heb metrics: - name: BLEU type: bleu value: 19.1 - name: chr-F type: chrf value: 0.49683 - task: name: Translation fra-mlt type: translation args: fra-mlt dataset: name: flores200-devtest type: flores200-devtest args: fra-mlt metrics: - name: BLEU type: bleu value: 20.4 - name: chr-F type: chrf value: 0.56844 - task: name: Translation por-hau type: translation args: por-hau dataset: name: flores200-devtest type: flores200-devtest args: por-hau metrics: - name: BLEU type: bleu value: 13.6 - name: chr-F type: chrf value: 0.42593 - task: name: Translation por-heb type: translation args: por-heb dataset: name: flores200-devtest type: flores200-devtest args: por-heb metrics: - name: BLEU type: bleu value: 19.7 - name: chr-F type: chrf value: 0.50345 - task: name: Translation por-mlt type: translation args: por-mlt dataset: name: flores200-devtest type: flores200-devtest args: por-mlt metrics: - name: BLEU type: bleu value: 21.5 - name: chr-F type: chrf value: 0.58913 - task: name: Translation spa-heb type: translation args: spa-heb dataset: name: flores200-devtest type: flores200-devtest args: spa-heb metrics: - name: BLEU type: bleu value: 13.5 - name: chr-F type: chrf value: 0.45249 - task: name: Translation spa-mlt type: translation args: spa-mlt dataset: name: flores200-devtest type: flores200-devtest args: spa-mlt metrics: - name: BLEU type: bleu value: 12.7 - name: chr-F type: chrf value: 0.51077 - task: name: Translation deu-ara type: translation args: deu-ara dataset: name: flores101-devtest type: flores_101 args: deu ara devtest metrics: - name: BLEU type: bleu value: 15.7 - name: chr-F type: chrf value: 0.47927 - task: name: Translation deu-hau type: translation args: deu-hau dataset: name: flores101-devtest type: flores_101 args: deu hau devtest metrics: - name: BLEU type: bleu value: 10.6 - name: chr-F type: chrf value: 0.39583 - task: name: Translation eng-hau type: translation args: eng-hau dataset: name: flores101-devtest type: flores_101 args: eng hau devtest metrics: - name: BLEU type: bleu value: 19.0 - name: chr-F type: chrf value: 0.47807 - task: name: Translation eng-mlt type: translation args: eng-mlt dataset: name: flores101-devtest type: flores_101 args: eng mlt devtest metrics: - name: BLEU type: bleu value: 32.9 - name: chr-F type: chrf value: 0.67196 - task: name: Translation fra-mlt type: translation args: fra-mlt dataset: name: flores101-devtest type: flores_101 args: fra mlt devtest metrics: - name: BLEU type: bleu value: 19.9 - name: chr-F type: chrf value: 0.56271 - task: name: Translation por-heb type: translation args: por-heb dataset: name: flores101-devtest type: flores_101 args: por heb devtest metrics: - name: BLEU type: bleu value: 19.6 - name: chr-F type: chrf value: 0.49378 - task: name: Translation spa-ara type: translation args: spa-ara dataset: name: flores101-devtest type: flores_101 args: spa ara devtest metrics: - name: BLEU type: bleu value: 11.7 - name: chr-F type: chrf value: 0.44988 - task: name: Translation deu-hau type: translation args: deu-hau dataset: name: ntrex128 type: ntrex128 args: deu-hau metrics: - name: BLEU type: bleu value: 12.5 - name: chr-F type: chrf value: 0.41931 - task: name: Translation deu-heb type: translation args: deu-heb dataset: name: ntrex128 type: ntrex128 args: deu-heb metrics: - name: BLEU type: bleu value: 13.3 - name: chr-F type: chrf value: 0.43961 - task: name: Translation deu-mlt type: translation args: deu-mlt dataset: name: ntrex128 type: ntrex128 args: deu-mlt metrics: - name: BLEU type: bleu value: 15.1 - name: chr-F type: chrf value: 0.49871 - task: name: Translation eng-hau type: translation args: eng-hau dataset: name: ntrex128 type: ntrex128 args: eng-hau metrics: - name: BLEU type: bleu value: 23.2 - name: chr-F type: chrf value: 0.51601 - task: name: Translation eng-heb type: translation args: eng-heb dataset: name: ntrex128 type: ntrex128 args: eng-heb metrics: - name: BLEU type: bleu value: 20.3 - name: chr-F type: chrf value: 0.50625 - task: name: Translation eng-mlt type: translation args: eng-mlt dataset: name: ntrex128 type: ntrex128 args: eng-mlt metrics: - name: BLEU type: bleu value: 29.0 - name: chr-F type: chrf value: 0.62552 - task: name: Translation eng-som type: translation args: eng-som dataset: name: ntrex128 type: ntrex128 args: eng-som metrics: - name: BLEU type: bleu value: 13.5 - name: chr-F type: chrf value: 0.46845 - task: name: Translation fra-hau type: translation args: fra-hau dataset: name: ntrex128 type: ntrex128 args: fra-hau metrics: - name: BLEU type: bleu value: 14.5 - name: chr-F type: chrf value: 0.43729 - task: name: Translation fra-heb type: translation args: fra-heb dataset: name: ntrex128 type: ntrex128 args: fra-heb metrics: - name: BLEU type: bleu value: 13.9 - name: chr-F type: chrf value: 0.43855 - task: name: Translation fra-mlt type: translation args: fra-mlt dataset: name: ntrex128 type: ntrex128 args: fra-mlt metrics: - name: BLEU type: bleu value: 17.3 - name: chr-F type: chrf value: 0.51640 - task: name: Translation por-hau type: translation args: por-hau dataset: name: ntrex128 type: ntrex128 args: por-hau metrics: - name: BLEU type: bleu value: 15.1 - name: chr-F type: chrf value: 0.44408 - task: name: Translation por-heb type: translation args: por-heb dataset: name: ntrex128 type: ntrex128 args: por-heb metrics: - name: BLEU type: bleu value: 15.0 - name: chr-F type: chrf value: 0.45739 - task: name: Translation por-mlt type: translation args: por-mlt dataset: name: ntrex128 type: ntrex128 args: por-mlt metrics: - name: BLEU type: bleu value: 18.2 - name: chr-F type: chrf value: 0.53719 - task: name: Translation spa-hau type: translation args: spa-hau dataset: name: ntrex128 type: ntrex128 args: spa-hau metrics: - name: BLEU type: bleu value: 14.8 - name: chr-F type: chrf value: 0.44695 - task: name: Translation spa-heb type: translation args: spa-heb dataset: name: ntrex128 type: ntrex128 args: spa-heb metrics: - name: BLEU type: bleu value: 14.5 - name: chr-F type: chrf value: 0.45509 - task: name: Translation spa-mlt type: translation args: spa-mlt dataset: name: ntrex128 type: ntrex128 args: spa-mlt metrics: - name: BLEU type: bleu value: 17.7 - name: chr-F type: chrf value: 0.53631 - task: name: Translation deu-ara type: translation args: deu-ara dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-ara metrics: - name: BLEU type: bleu value: 20.2 - name: chr-F type: chrf value: 0.49517 - task: name: Translation deu-heb type: translation args: deu-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-heb metrics: - name: BLEU type: bleu value: 35.8 - name: chr-F type: chrf value: 0.56943 - task: name: Translation eng-heb type: translation args: eng-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-heb metrics: - name: BLEU type: bleu value: 34.9 - name: chr-F type: chrf value: 0.57708 - task: name: Translation eng-mlt type: translation args: eng-mlt dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-mlt metrics: - name: BLEU type: bleu value: 29.5 - name: chr-F type: chrf value: 0.61044 - task: name: Translation fra-heb type: translation args: fra-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fra-heb metrics: - name: BLEU type: bleu value: 37.5 - name: chr-F type: chrf value: 0.58681 - task: name: Translation por-heb type: translation args: por-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: por-heb metrics: - name: BLEU type: bleu value: 41.0 - name: chr-F type: chrf value: 0.61593 - task: name: Translation spa-ara type: translation args: spa-ara dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: spa-ara metrics: - name: BLEU type: bleu value: 23.9 - name: chr-F type: chrf value: 0.53669 - task: name: Translation spa-heb type: translation args: spa-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: spa-heb metrics: - name: BLEU type: bleu value: 41.2 - name: chr-F type: chrf value: 0.61966 - task: name: Translation eng-ara type: translation args: eng-ara dataset: name: tico19-test type: tico19-test args: eng-ara metrics: - name: BLEU type: bleu value: 25.4 - name: chr-F type: chrf value: 0.56288 - task: name: Translation eng-hau type: translation args: eng-hau dataset: name: tico19-test type: tico19-test args: eng-hau metrics: - name: BLEU type: bleu value: 22.2 - name: chr-F type: chrf value: 0.50060 - task: name: Translation fra-ara type: translation args: fra-ara dataset: name: tico19-test type: tico19-test args: fra-ara metrics: - name: BLEU type: bleu value: 13.8 - name: chr-F type: chrf value: 0.39785 - task: name: Translation por-ara type: translation args: por-ara dataset: name: tico19-test type: tico19-test args: por-ara metrics: - name: BLEU type: bleu value: 16.0 - name: chr-F type: chrf value: 0.44442 - task: name: Translation spa-ara type: translation args: spa-ara dataset: name: tico19-test type: tico19-test args: spa-ara metrics: - name: BLEU type: bleu value: 16.5 - name: chr-F type: chrf value: 0.45429 - task: name: Translation eng-hau type: translation args: eng-hau dataset: name: newstest2021 type: wmt-2021-news args: eng-hau metrics: - name: BLEU type: bleu value: 13.1 - name: chr-F type: chrf value: 0.43617 --- # opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa ## 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 unknown (deu+eng+fra+por+spa) to Afro-Asiatic languages (afa). 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**: 2024-05-29 - **License:** Apache-2.0 - **Language(s):** - Source Language(s): deu eng fra por spa - Target Language(s): aar acm afb amh apc ara arc arq arz bcw byn cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar orm pbi phn rif sgw shi shy som sur syc syr taq thv tig tir tmc tmh tmr ttr tzm wal xed zgh - Valid Target Language Labels: >>aal<< >>aar<< >>aas<< >>acm<< >>afb<< >>agj<< >>ahg<< >>aij<< >>aiw<< >>ajw<< >>akk<< >>alw<< >>amh<< >>amw<< >>anc<< >>ank<< >>apc<< >>ara<< >>arc<< >>arq<< >>arv<< >>arz<< >>auj<< >>auo<< >>awn<< >>bbt<< >>bcq<< >>bcw<< >>bcy<< >>bde<< >>bdm<< >>bdn<< >>bds<< >>bej<< >>bhm<< >>bhn<< >>bhs<< >>bid<< >>bjf<< >>bji<< >>bnl<< >>bob<< >>bol<< >>bsw<< >>bta<< >>btf<< >>bux<< >>bva<< >>bvf<< >>bvh<< >>bvw<< >>bwo<< >>bwr<< >>bxe<< >>bxq<< >>byn<< >>cie<< >>ckl<< >>ckq<< >>cky<< >>cla<< >>cnu<< >>cop<< >>cop_Copt<< >>cuv<< >>daa<< >>dal<< >>dbb<< >>dbp<< >>dbq<< >>dbr<< >>dgh<< >>dim<< >>dkx<< >>dlk<< >>dme<< >>dot<< >>dox<< >>doz<< >>drs<< >>dsh<< >>dwa<< >>egy<< >>elo<< >>fie<< >>fkk<< >>fli<< >>gab<< >>gde<< >>gdf<< >>gdk<< >>gdl<< >>gdq<< >>gdu<< >>gea<< >>gek<< >>gew<< >>gex<< >>gez<< >>gft<< >>gha<< >>gho<< >>gid<< >>gis<< >>giz<< >>gji<< >>glo<< >>glw<< >>gnc<< >>gnd<< >>gou<< >>gow<< >>gqa<< >>grd<< >>grr<< >>gru<< >>gwd<< >>gwn<< >>har<< >>hau<< >>hau_Latn<< >>hbb<< >>hbo<< >>hbo_Hebr<< >>hdy<< >>heb<< >>hed<< >>hia<< >>hig<< >>hna<< >>hod<< >>hoh<< >>hrt<< >>hss<< >>huy<< >>hwo<< >>hya<< >>inm<< >>ior<< >>irk<< >>jaf<< >>jbe<< >>jbn<< >>jeu<< >>jia<< >>jie<< >>jii<< >>jim<< >>jmb<< >>jmi<< >>jnj<< >>jpa<< >>jpa_Hebr<< >>jrb<< >>juu<< >>kab<< >>kai<< >>kbz<< >>kcn<< >>kcs<< >>ker<< >>kil<< >>kkr<< >>kks<< >>kna<< >>kof<< >>kot<< >>kpa<< >>kqd<< >>kqp<< >>kqx<< >>ksq<< >>ktb<< >>ktc<< >>kuh<< >>kul<< >>kvf<< >>kvi<< >>kvj<< >>kwl<< >>kxc<< >>ldd<< >>lhs<< >>liq<< >>lln<< >>lme<< >>lsd<< >>maf<< >>mcn<< >>mcw<< >>mdx<< >>meq<< >>mes<< >>mew<< >>mey<< >>mfh<< >>mfi<< >>mfj<< >>mfk<< >>mfl<< >>mfm<< >>mid<< >>mif<< >>mje<< >>mjs<< >>mkf<< >>mlj<< >>mlr<< >>mlt<< >>mlw<< >>mmf<< >>mmy<< >>mou<< >>moz<< >>mpg<< >>mpi<< >>mpk<< >>mqb<< >>mrt<< >>mse<< >>msv<< >>mtl<< >>mub<< >>mug<< >>muj<< >>muu<< >>muy<< >>mvh<< >>mvz<< >>mxf<< >>mxu<< >>mys<< >>myz<< >>mzb<< >>nbh<< >>ndm<< >>ngi<< >>ngs<< >>ngw<< >>ngx<< >>nja<< >>nmi<< >>nnc<< >>nnn<< >>noz<< >>nxm<< >>oar<< >>oar_Hebr<< >>oar_Syrc<< >>orm<< >>oua<< >>pbi<< >>pcw<< >>phn<< >>phn_Phnx<< >>pip<< >>piy<< >>plj<< >>pqa<< >>rel<< >>rif<< >>rif_Latn<< >>rzh<< >>saa<< >>sam<< >>say<< >>scw<< >>sds<< >>sgw<< >>she<< >>shi<< >>shi_Latn<< >>shv<< >>shy<< >>shy_Latn<< >>sid<< >>sir<< >>siz<< >>sjs<< >>smp<< >>sok<< >>som<< >>sor<< >>sqr<< >>sqt<< >>ssn<< >>ssy<< >>stv<< >>sur<< >>swn<< >>swq<< >>swy<< >>syc<< >>syk<< >>syn<< >>syr<< >>tak<< >>tal<< >>tan<< >>taq<< >>tax<< >>tdk<< >>tez<< >>tgd<< >>thv<< >>tia<< >>tig<< >>tir<< >>tjo<< >>tmc<< >>tmh<< >>tmr<< >>tmr_Hebr<< >>tng<< >>tqq<< >>trg<< >>trj<< >>tru<< >>tsb<< >>tsh<< >>ttr<< >>twc<< >>tzm<< >>tzm_Latn<< >>tzm_Tfng<< >>ubi<< >>udl<< >>uga<< >>vem<< >>wal<< >>wbj<< >>wji<< >>wka<< >>wle<< >>xaa<< >>xan<< >>xeb<< >>xed<< >>xhd<< >>xmd<< >>xmj<< >>xna<< >>xpu<< >>xqt<< >>xsa<< >>ymm<< >>zah<< >>zay<< >>zaz<< >>zen<< >>zgh<< >>zim<< >>ziz<< >>zns<< >>zrn<< >>zua<< >>zuy<< >>zwa<< - **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip) - **Resources for more information:** - [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-afa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29) - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - [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/) - [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1) - [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/) 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. `>>aar<<` ## 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 = [ ">>kab<< Tu seras parmi nous demain.", ">>heb<< Let's get out of here while we can." ] model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa" 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: # Azekka ad tiliḍ yid-i # בוא נצא מכאן כל עוד אנחנו יכולים. ``` 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-afa") print(pipe(">>kab<< Tu seras parmi nous demain.")) # expected output: Azekka ad tiliḍ yid-i ``` ## Training - **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-afa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29) * test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt) * test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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 | |----------|---------|-------|-------|-------|--------| | deu-ara | tatoeba-test-v2021-08-07 | 0.49517 | 20.2 | 1209 | 6324 | | deu-heb | tatoeba-test-v2021-08-07 | 0.56943 | 35.8 | 3090 | 20341 | | eng-ara | tatoeba-test-v2021-08-07 | 0.46273 | 17.3 | 10305 | 61356 | | eng-heb | tatoeba-test-v2021-08-07 | 0.57708 | 34.9 | 10519 | 63628 | | eng-mlt | tatoeba-test-v2021-08-07 | 0.61044 | 29.5 | 203 | 899 | | fra-ara | tatoeba-test-v2021-08-07 | 0.42223 | 10.4 | 1569 | 7956 | | fra-heb | tatoeba-test-v2021-08-07 | 0.58681 | 37.5 | 3281 | 20655 | | por-heb | tatoeba-test-v2021-08-07 | 0.61593 | 41.0 | 719 | 4423 | | spa-ara | tatoeba-test-v2021-08-07 | 0.53669 | 23.9 | 1511 | 7547 | | spa-heb | tatoeba-test-v2021-08-07 | 0.61966 | 41.2 | 1849 | 12112 | | deu-ara | flores101-devtest | 0.47927 | 15.7 | 1012 | 21357 | | eng-hau | flores101-devtest | 0.47807 | 19.0 | 1012 | 27730 | | eng-mlt | flores101-devtest | 0.67196 | 32.9 | 1012 | 22169 | | fra-mlt | flores101-devtest | 0.56271 | 19.9 | 1012 | 22169 | | por-heb | flores101-devtest | 0.49378 | 19.6 | 1012 | 20749 | | spa-ara | flores101-devtest | 0.44988 | 11.7 | 1012 | 21357 | | deu-ara | flores200-devtest | 0.661 | 0.0 | 1012 | 5 | | deu-hau | flores200-devtest | 0.40471 | 11.4 | 1012 | 27730 | | deu-heb | flores200-devtest | 0.48645 | 18.1 | 1012 | 20238 | | deu-mlt | flores200-devtest | 0.54079 | 17.5 | 1012 | 22169 | | eng-ara | flores200-devtest | 0.627 | 0.0 | 1012 | 5 | | eng-arz | flores200-devtest | 0.42804 | 11.1 | 1012 | 21034 | | eng-hau | flores200-devtest | 0.49023 | 20.4 | 1012 | 27730 | | eng-heb | flores200-devtest | 0.56635 | 27.1 | 1012 | 20238 | | eng-mlt | flores200-devtest | 0.68334 | 34.9 | 1012 | 22169 | | eng-som | flores200-devtest | 0.42814 | 9.9 | 1012 | 25991 | | fra-ara | flores200-devtest | 0.631 | 0.0 | 1012 | 5 | | fra-hau | flores200-devtest | 0.42731 | 13.2 | 1012 | 27730 | | fra-heb | flores200-devtest | 0.49683 | 19.1 | 1012 | 20238 | | fra-mlt | flores200-devtest | 0.56844 | 20.4 | 1012 | 22169 | | por-ara | flores200-devtest | 0.622 | 0.0 | 1012 | 5 | | por-hau | flores200-devtest | 0.42593 | 13.6 | 1012 | 27730 | | por-heb | flores200-devtest | 0.50345 | 19.7 | 1012 | 20238 | | por-mlt | flores200-devtest | 0.58913 | 21.5 | 1012 | 22169 | | spa-ara | flores200-devtest | 0.587 | 0.0 | 1012 | 5 | | spa-hau | flores200-devtest | 0.40309 | 9.4 | 1012 | 27730 | | spa-heb | flores200-devtest | 0.45249 | 13.5 | 1012 | 20238 | | spa-mlt | flores200-devtest | 0.51077 | 12.7 | 1012 | 22169 | | eng-hau | newstest2021 | 0.43617 | 13.1 | 1000 | 32966 | | deu-hau | ntrex128 | 0.41931 | 12.5 | 1997 | 54982 | | deu-heb | ntrex128 | 0.43961 | 13.3 | 1997 | 39624 | | deu-mlt | ntrex128 | 0.49871 | 15.1 | 1997 | 43308 | | eng-hau | ntrex128 | 0.51601 | 23.2 | 1997 | 54982 | | eng-heb | ntrex128 | 0.50625 | 20.3 | 1997 | 39624 | | eng-mlt | ntrex128 | 0.62552 | 29.0 | 1997 | 43308 | | eng-som | ntrex128 | 0.46845 | 13.5 | 1997 | 49351 | | fra-hau | ntrex128 | 0.43729 | 14.5 | 1997 | 54982 | | fra-heb | ntrex128 | 0.43855 | 13.9 | 1997 | 39624 | | fra-mlt | ntrex128 | 0.51640 | 17.3 | 1997 | 43308 | | fra-som | ntrex128 | 0.41813 | 9.6 | 1997 | 49351 | | por-hau | ntrex128 | 0.44408 | 15.1 | 1997 | 54982 | | por-heb | ntrex128 | 0.45739 | 15.0 | 1997 | 39624 | | por-mlt | ntrex128 | 0.53719 | 18.2 | 1997 | 43308 | | por-som | ntrex128 | 0.41367 | 9.3 | 1997 | 49351 | | spa-hau | ntrex128 | 0.44695 | 14.8 | 1997 | 54982 | | spa-heb | ntrex128 | 0.45509 | 14.5 | 1997 | 39624 | | spa-mlt | ntrex128 | 0.53631 | 17.7 | 1997 | 43308 | | spa-som | ntrex128 | 0.41755 | 9.1 | 1997 | 49351 | | eng-ara | tico19-test | 0.56288 | 25.4 | 2100 | 51339 | | eng-hau | tico19-test | 0.50060 | 22.2 | 2100 | 64509 | | fra-amh | tico19-test | 3.575 | 1.3 | 2100 | 44782 | | fra-hau | tico19-test | 5.071 | 1.8 | 2100 | 64509 | | fra-orm | tico19-test | 4.044 | 1.8 | 2100 | 50032 | | fra-som | tico19-test | 2.698 | 0.9 | 2100 | 63654 | | fra-tir | tico19-test | 4.151 | 1.4 | 2100 | 46685 | | por-amh | tico19-test | 3.799 | 1.4 | 2100 | 44782 | | por-ara | tico19-test | 0.44442 | 16.0 | 2100 | 51339 | | por-hau | tico19-test | 5.786 | 2.0 | 2100 | 64509 | | por-orm | tico19-test | 4.613 | 2.0 | 2100 | 50032 | | por-som | tico19-test | 3.413 | 1.2 | 2100 | 63654 | | por-tir | tico19-test | 5.092 | 1.6 | 2100 | 46685 | | spa-amh | tico19-test | 3.831 | 1.4 | 2100 | 44782 | | spa-ara | tico19-test | 0.45429 | 16.5 | 2100 | 51339 | | spa-hau | tico19-test | 5.790 | 1.9 | 2100 | 64509 | | spa-orm | tico19-test | 4.617 | 1.9 | 2100 | 50032 | | spa-som | tico19-test | 3.402 | 1.2 | 2100 | 63654 | | spa-tir | tico19-test | 5.033 | 1.6 | 2100 | 46685 | ## Citation Information * Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [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.) ```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](https://hplt-project.org/), 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](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/). ## Model conversion info * transformers version: 4.45.1 * OPUS-MT git hash: 0882077 * port time: Tue Oct 8 08:58:38 EEST 2024 * port machine: LM0-400-22516.local