--- language: - be - en - ru - uk - zle tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-en-zle results: - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: flores101-devtest type: flores_101 args: eng rus devtest metrics: - name: BLEU type: bleu value: 32.7 - task: name: Translation eng-ukr type: translation args: eng-ukr dataset: name: flores101-devtest type: flores_101 args: eng ukr devtest metrics: - name: BLEU type: bleu value: 32.1 - task: name: Translation eng-bel type: translation args: eng-bel dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-bel metrics: - name: BLEU type: bleu value: 24.9 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-rus metrics: - name: BLEU type: bleu value: 45.5 - task: name: Translation eng-ukr type: translation args: eng-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-ukr metrics: - name: BLEU type: bleu value: 37.7 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: tico19-test type: tico19-test args: eng-rus metrics: - name: BLEU type: bleu value: 33.7 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2012 type: wmt-2012-news args: eng-rus metrics: - name: BLEU type: bleu value: 36.8 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2013 type: wmt-2013-news args: eng-rus metrics: - name: BLEU type: bleu value: 26.9 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2014 type: wmt-2014-news args: eng-rus metrics: - name: BLEU type: bleu value: 43.5 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2015 type: wmt-2015-news args: eng-rus metrics: - name: BLEU type: bleu value: 34.9 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2016 type: wmt-2016-news args: eng-rus metrics: - name: BLEU type: bleu value: 33.1 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2017 type: wmt-2017-news args: eng-rus metrics: - name: BLEU type: bleu value: 37.3 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2018 type: wmt-2018-news args: eng-rus metrics: - name: BLEU type: bleu value: 32.9 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2019 type: wmt-2019-news args: eng-rus metrics: - name: BLEU type: bleu value: 31.8 - task: name: Translation eng-rus type: translation args: eng-rus dataset: name: newstest2020 type: wmt-2020-news args: eng-rus metrics: - name: BLEU type: bleu value: 25.5 --- # opus-mt-tc-big-en-zle Neural machine translation model for translating from English (en) to East Slavic languages (zle). 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: 2022-03-13 * source language(s): eng * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zle/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) 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. `>>bel<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Are they coming as well?", ">>rus<< I didn't let Tom do what he wanted to do." ] model_name = "pytorch-models/opus-mt-tc-big-en-zle" 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: # Они тоже приедут? # Я не позволил Тому сделать то, что он хотел. ``` 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-en-zle") print(pipe(">>rus<< Are they coming as well?")) # expected output: Они тоже приедут? ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opusTCv20210807+bt_transformer-big_2022-03-13.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 | |----------|---------|-------|-------|-------|--------| | eng-bel | tatoeba-test-v2021-08-07 | 0.50345 | 24.9 | 2500 | 16237 | | eng-rus | tatoeba-test-v2021-08-07 | 0.66182 | 45.5 | 19425 | 134296 | | eng-ukr | tatoeba-test-v2021-08-07 | 0.60175 | 37.7 | 13127 | 80998 | | eng-bel | flores101-devtest | 0.42078 | 11.2 | 1012 | 24829 | | eng-rus | flores101-devtest | 0.59654 | 32.7 | 1012 | 23295 | | eng-ukr | flores101-devtest | 0.60131 | 32.1 | 1012 | 22810 | | eng-rus | newstest2012 | 0.62842 | 36.8 | 3003 | 64790 | | eng-rus | newstest2013 | 0.54627 | 26.9 | 3000 | 58560 | | eng-rus | newstest2014 | 0.68348 | 43.5 | 3003 | 61603 | | eng-rus | newstest2015 | 0.62621 | 34.9 | 2818 | 55915 | | eng-rus | newstest2016 | 0.60595 | 33.1 | 2998 | 62014 | | eng-rus | newstest2017 | 0.64249 | 37.3 | 3001 | 60253 | | eng-rus | newstest2018 | 0.61219 | 32.9 | 3000 | 61907 | | eng-rus | newstest2019 | 0.57902 | 31.8 | 1997 | 48147 | | eng-rus | newstest2020 | 0.52939 | 25.5 | 2002 | 47083 | | eng-rus | tico19-test | 0.59314 | 33.7 | 2100 | 55843 | ## 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: Thu Mar 24 01:58:40 EET 2022 * port machine: LM0-400-22516.local