--- language: - be - de - ru - uk - zle tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-zle-de results: - task: name: Translation rus-deu type: translation args: rus-deu dataset: name: flores101-devtest type: flores_101 args: rus deu devtest metrics: - name: BLEU type: bleu value: 26.1 - task: name: Translation ukr-deu type: translation args: ukr-deu dataset: name: flores101-devtest type: flores_101 args: ukr deu devtest metrics: - name: BLEU type: bleu value: 28.1 - task: name: Translation bel-deu type: translation args: bel-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bel-deu metrics: - name: BLEU type: bleu value: 44.8 - task: name: Translation rus-deu type: translation args: rus-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-deu metrics: - name: BLEU type: bleu value: 51.8 - task: name: Translation ukr-deu type: translation args: ukr-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-deu metrics: - name: BLEU type: bleu value: 54.7 - task: name: Translation rus-deu type: translation args: rus-deu dataset: name: newstest2013 type: wmt-2013-news args: rus-deu metrics: - name: BLEU type: bleu value: 25.2 --- # opus-mt-tc-big-zle-de Neural machine translation model for translating from East Slavic languages (zle) to German (de). 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). * Publications: [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.) ``` @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", } ``` ## Model info * Release: 2022-03-19 * source language(s): bel rus ukr * target language(s): deu * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.zip) * more information released models: [OPUS-MT zle-deu README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-deu/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Это был по-настоящему прекрасный день.", "Дождь кончился?" ] model_name = "pytorch-models/opus-mt-tc-big-zle-de" 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: # Es war ein wirklich schöner Tag. # Ist der Regen vorbei? ``` 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-big-zle-de") print(pipe("Это был по-настоящему прекрасный день.")) # expected output: Es war ein wirklich schöner Tag. ``` ## Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.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 | |----------|---------|-------|-------|-------|--------| | bel-deu | tatoeba-test-v2021-08-07 | 0.63720 | 44.8 | 551 | 4182 | | rus-deu | tatoeba-test-v2021-08-07 | 0.69768 | 51.8 | 12800 | 98842 | | ukr-deu | tatoeba-test-v2021-08-07 | 0.70860 | 54.7 | 10319 | 64646 | | bel-deu | flores101-devtest | 0.47052 | 12.9 | 1012 | 25094 | | rus-deu | flores101-devtest | 0.56159 | 26.1 | 1012 | 25094 | | ukr-deu | flores101-devtest | 0.57251 | 28.1 | 1012 | 25094 | | rus-deu | newstest2012 | 0.49257 | 19.8 | 3003 | 72886 | | rus-deu | newstest2013 | 0.54015 | 25.2 | 3000 | 63737 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 1bdabf7 * port time: Wed Mar 23 22:16:45 EET 2022 * port machine: LM0-400-22516.local