--- library_name: transformers language: - bru - cmo - de - en - es - fr - hoc - jun - kha - km - kxm - mnw - ngt - pt - sat - vi - wbm tags: - translation - opus-mt-tc-bible license: apache-2.0 model-index: - name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav results: - task: name: Translation deu-vie type: translation args: deu-vie dataset: name: flores200-devtest type: flores200-devtest args: deu-vie metrics: - name: BLEU type: bleu value: 34.0 - name: chr-F type: chrf value: 0.53671 - task: name: Translation eng-vie type: translation args: eng-vie dataset: name: flores200-devtest type: flores200-devtest args: eng-vie metrics: - name: BLEU type: bleu value: 42.4 - name: chr-F type: chrf value: 0.59842 - task: name: Translation fra-vie type: translation args: fra-vie dataset: name: flores200-devtest type: flores200-devtest args: fra-vie metrics: - name: BLEU type: bleu value: 34.6 - name: chr-F type: chrf value: 0.54101 - task: name: Translation por-vie type: translation args: por-vie dataset: name: flores200-devtest type: flores200-devtest args: por-vie metrics: - name: BLEU type: bleu value: 36.1 - name: chr-F type: chrf value: 0.54970 - task: name: Translation spa-vie type: translation args: spa-vie dataset: name: flores200-devtest type: flores200-devtest args: spa-vie metrics: - name: BLEU type: bleu value: 28.1 - name: chr-F type: chrf value: 0.50025 - task: name: Translation deu-vie type: translation args: deu-vie dataset: name: flores101-devtest type: flores_101 args: deu vie devtest metrics: - name: BLEU type: bleu value: 33.8 - name: chr-F type: chrf value: 0.53381 - task: name: Translation eng-vie type: translation args: eng-vie dataset: name: flores101-devtest type: flores_101 args: eng vie devtest metrics: - name: BLEU type: bleu value: 42.1 - name: chr-F type: chrf value: 0.59621 - task: name: Translation por-vie type: translation args: por-vie dataset: name: flores101-devtest type: flores_101 args: por vie devtest metrics: - name: BLEU type: bleu value: 36.0 - name: chr-F type: chrf value: 0.54919 - task: name: Translation spa-vie type: translation args: spa-vie dataset: name: flores101-devtest type: flores_101 args: spa vie devtest metrics: - name: BLEU type: bleu value: 27.8 - name: chr-F type: chrf value: 0.49921 - task: name: Translation deu-vie type: translation args: deu-vie dataset: name: ntrex128 type: ntrex128 args: deu-vie metrics: - name: BLEU type: bleu value: 31.4 - name: chr-F type: chrf value: 0.52124 - task: name: Translation fra-vie type: translation args: fra-vie dataset: name: ntrex128 type: ntrex128 args: fra-vie metrics: - name: BLEU type: bleu value: 31.8 - name: chr-F type: chrf value: 0.52044 - task: name: Translation por-vie type: translation args: por-vie dataset: name: ntrex128 type: ntrex128 args: por-vie metrics: - name: BLEU type: bleu value: 33.3 - name: chr-F type: chrf value: 0.53060 - task: name: Translation spa-vie type: translation args: spa-vie dataset: name: ntrex128 type: ntrex128 args: spa-vie metrics: - name: BLEU type: bleu value: 33.4 - name: chr-F type: chrf value: 0.53293 - task: name: Translation deu-vie type: translation args: deu-vie dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: deu-vie metrics: - name: BLEU type: bleu value: 25.6 - name: chr-F type: chrf value: 0.45795 - task: name: Translation eng-vie type: translation args: eng-vie dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-vie metrics: - name: BLEU type: bleu value: 39.4 - name: chr-F type: chrf value: 0.56461 - task: name: Translation fra-vie type: translation args: fra-vie dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fra-vie metrics: - name: BLEU type: bleu value: 35.2 - name: chr-F type: chrf value: 0.52806 - task: name: Translation multi-multi type: translation args: multi-multi dataset: name: tatoeba-test-v2020-07-28-v2023-09-26 type: tatoeba_mt args: multi-multi metrics: - name: BLEU type: bleu value: 22.9 - name: chr-F type: chrf value: 0.40649 - task: name: Translation spa-vie type: translation args: spa-vie dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: spa-vie metrics: - name: BLEU type: bleu value: 34.2 - name: chr-F type: chrf value: 0.52131 --- # opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav ## 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 Austro-Asiatic languages (aav). 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): bru cmo hoc jun kha khm kxm mnw ngt sat vie wbm - Valid Target Language Labels: >>aem<< >>alk<< >>aml<< >>asr<< >>bbh<< >>bdq<< >>bfw<< >>bgk<< >>bgl<< >>bix<< >>biy<< >>blr<< >>brb<< >>bru<< >>brv<< >>btq<< >>caq<< >>cbn<< >>cdz<< >>cma<< >>cmo<< >>cog<< >>crv<< >>crw<< >>cua<< >>cwg<< >>dnu<< >>ekl<< >>gaq<< >>gbj<< >>hal<< >>hld<< >>hnu<< >>hoc<< >>hoc_Wara<< >>hre<< >>huo<< >>irr<< >>jah<< >>jeh<< >>jhi<< >>jun<< >>juy<< >>kdt<< >>kfp<< >>kfq<< >>kha<< >>khf<< >>khm<< >>khr<< >>kjg<< >>kjm<< >>knq<< >>kns<< >>kpm<< >>krr<< >>krv<< >>ksz<< >>kta<< >>ktv<< >>kuf<< >>kxm<< >>kxy<< >>lbn<< >>lbo<< >>lcp<< >>lnh<< >>lwl<< >>lyg<< >>mef<< >>mhe<< >>mjx<< >>mlf<< >>mmj<< >>mml<< >>mng<< >>mnn<< >>mnq<< >>mnw<< >>moo<< >>mqt<< >>mra<< >>mtq<< >>mzt<< >>ncb<< >>ncq<< >>nev<< >>ngt<< >>ngt_Latn<< >>nik<< >>nuo<< >>nyl<< >>omx<< >>oog<< >>oyb<< >>pac<< >>pbv<< >>pcb<< >>pce<< >>pcj<< >>phg<< >>pkt<< >>pll<< >>ply<< >>pnx<< >>prk<< >>prt<< >>puo<< >>rbb<< >>ren<< >>ril<< >>rka<< >>rmx<< >>sat<< >>sat_Latn<< >>sbo<< >>scb<< >>scq<< >>sct<< >>sea<< >>sed<< >>sii<< >>smu<< >>spu<< >>sqq<< >>srb<< >>ssm<< >>sss<< >>stg<< >>sti<< >>stt<< >>stu<< >>syo<< >>sza<< >>szc<< >>tdf<< >>tdr<< >>tea<< >>tef<< >>thm<< >>tkz<< >>tlq<< >>tmo<< >>tnz<< >>tou<< >>tpu<< >>trd<< >>tth<< >>tto<< >>tyh<< >>unr<< >>uuu<< >>vie<< >>vwa<< >>wbm<< >>xao<< >>xkk<< >>xnh<< >>xxx<< >>yin<< >>zng<< - **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-aav/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-aav/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. `>>bru<<` ## 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 = [ ">>khm<< Der Junge wirft einen Stein.", ">>vie<< ¿Y tú?" ] model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav" 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: # ក្មេងប្រុស នោះ យក ដុំ ថ្ម គប់ ។ # Còn anh thì sao? ``` 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-aav") print(pipe(">>khm<< Der Junge wirft einen Stein.")) # expected output: ក្មេងប្រុស នោះ យក ដុំ ថ្ម គប់ ។ ``` ## 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-aav/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-aav/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-aav/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-aav/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-vie | tatoeba-test-v2021-08-07 | 0.45795 | 25.6 | 400 | 3768 | | eng-hoc | tatoeba-test-v2021-08-07 | 6.438 | 0.2 | 660 | 2591 | | eng-kha | tatoeba-test-v2021-08-07 | 5.741 | 0.0 | 1314 | 9269 | | eng-vie | tatoeba-test-v2021-08-07 | 0.56461 | 39.4 | 2500 | 24427 | | fra-vie | tatoeba-test-v2021-08-07 | 0.52806 | 35.2 | 1299 | 13219 | | spa-vie | tatoeba-test-v2021-08-07 | 0.52131 | 34.2 | 594 | 4740 | | deu-vie | flores101-devtest | 0.53381 | 33.8 | 1012 | 33331 | | eng-khm | flores101-devtest | 0.42302 | 1.3 | 1012 | 7006 | | eng-vie | flores101-devtest | 0.59621 | 42.1 | 1012 | 33331 | | fra-khm | flores101-devtest | 0.40042 | 2.2 | 1012 | 7006 | | por-khm | flores101-devtest | 0.40585 | 2.1 | 1012 | 7006 | | por-vie | flores101-devtest | 0.54919 | 36.0 | 1012 | 33331 | | spa-vie | flores101-devtest | 0.49921 | 27.8 | 1012 | 33331 | | deu-vie | flores200-devtest | 0.53671 | 34.0 | 1012 | 33331 | | eng-khm | flores200-devtest | 0.42148 | 1.3 | 1012 | 7006 | | eng-vie | flores200-devtest | 0.59842 | 42.4 | 1012 | 33331 | | fra-vie | flores200-devtest | 0.54101 | 34.6 | 1012 | 33331 | | por-khm | flores200-devtest | 0.40832 | 1.9 | 1012 | 7006 | | por-vie | flores200-devtest | 0.54970 | 36.1 | 1012 | 33331 | | spa-vie | flores200-devtest | 0.50025 | 28.1 | 1012 | 33331 | | deu-khm | ntrex128 | 0.44903 | 3.5 | 1997 | 15866 | | deu-vie | ntrex128 | 0.52124 | 31.4 | 1997 | 64655 | | eng-khm | ntrex128 | 0.50494 | 1.6 | 1997 | 15866 | | eng-vie | ntrex128 | 3.831 | 0.0 | 1997 | 64655 | | fra-khm | ntrex128 | 0.43841 | 2.4 | 1997 | 15866 | | fra-vie | ntrex128 | 0.52044 | 31.8 | 1997 | 64655 | | por-khm | ntrex128 | 0.46655 | 2.5 | 1997 | 15866 | | por-vie | ntrex128 | 0.53060 | 33.3 | 1997 | 64655 | | spa-khm | ntrex128 | 0.46443 | 2.7 | 1997 | 15866 | | spa-vie | ntrex128 | 0.53293 | 33.4 | 1997 | 64655 | | eng-khm | tico19-test | 0.47806 | 2.5 | 2100 | 15810 | | fra-khm | tico19-test | 3.268 | 1.0 | 2100 | 15810 | | por-khm | tico19-test | 3.900 | 1.1 | 2100 | 15810 | | spa-khm | tico19-test | 3.784 | 1.0 | 2100 | 15810 | ## 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 00:06:48 EEST 2024 * port machine: LM0-400-22516.local