--- language: - be - en - ru - uk - zle tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-zle-en results: - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: flores101-devtest type: flores_101 args: rus eng devtest metrics: - name: BLEU type: bleu value: 35.2 - task: name: Translation ukr-eng type: translation args: ukr-eng dataset: name: flores101-devtest type: flores_101 args: ukr eng devtest metrics: - name: BLEU type: bleu value: 39.2 - task: name: Translation bel-eng type: translation args: bel-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bel-eng metrics: - name: BLEU type: bleu value: 48.1 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-eng metrics: - name: BLEU type: bleu value: 57.4 - task: name: Translation ukr-eng type: translation args: ukr-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-eng metrics: - name: BLEU type: bleu value: 56.9 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: tico19-test type: tico19-test args: rus-eng metrics: - name: BLEU type: bleu value: 33.3 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2012 type: wmt-2012-news args: rus-eng metrics: - name: BLEU type: bleu value: 39.2 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2013 type: wmt-2013-news args: rus-eng metrics: - name: BLEU type: bleu value: 31.3 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2014 type: wmt-2014-news args: rus-eng metrics: - name: BLEU type: bleu value: 40.5 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2015 type: wmt-2015-news args: rus-eng metrics: - name: BLEU type: bleu value: 36.1 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2016 type: wmt-2016-news args: rus-eng metrics: - name: BLEU type: bleu value: 35.7 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2017 type: wmt-2017-news args: rus-eng metrics: - name: BLEU type: bleu value: 40.8 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2018 type: wmt-2018-news args: rus-eng metrics: - name: BLEU type: bleu value: 35.2 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2019 type: wmt-2019-news args: rus-eng metrics: - name: BLEU type: bleu value: 41.6 - task: name: Translation rus-eng type: translation args: rus-eng dataset: name: newstest2020 type: wmt-2020-news args: rus-eng metrics: - name: BLEU type: bleu value: 36.9 --- # opus-mt-tc-big-zle-en Neural machine translation model for translating from East Slavic languages (zle) to English (en). 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: 2022-03-17 * source language(s): bel rus ukr * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip) * more information released models: [OPUS-MT zle-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Скільки мені слід купити пива?", "Я клієнтка." ] model_name = "pytorch-models/opus-mt-tc-big-zle-en" 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: # How much beer should I buy? # I'm a client. ``` 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-en") print(pipe("Скільки мені слід купити пива?")) # expected output: How much beer should I buy? ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opusTCv20210807+bt_transformer-big_2022-03-17.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-eng | tatoeba-test-v2021-08-07 | 0.65221 | 48.1 | 2500 | 18571 | | rus-eng | tatoeba-test-v2021-08-07 | 0.71452 | 57.4 | 19425 | 147872 | | ukr-eng | tatoeba-test-v2021-08-07 | 0.71162 | 56.9 | 13127 | 88607 | | bel-eng | flores101-devtest | 0.51689 | 18.1 | 1012 | 24721 | | rus-eng | flores101-devtest | 0.62581 | 35.2 | 1012 | 24721 | | ukr-eng | flores101-devtest | 0.65001 | 39.2 | 1012 | 24721 | | rus-eng | newstest2012 | 0.63724 | 39.2 | 3003 | 72812 | | rus-eng | newstest2013 | 0.57641 | 31.3 | 3000 | 64505 | | rus-eng | newstest2014 | 0.65667 | 40.5 | 3003 | 69190 | | rus-eng | newstest2015 | 0.61747 | 36.1 | 2818 | 64428 | | rus-eng | newstest2016 | 0.61414 | 35.7 | 2998 | 69278 | | rus-eng | newstest2017 | 0.65365 | 40.8 | 3001 | 69025 | | rus-eng | newstest2018 | 0.61386 | 35.2 | 3000 | 71291 | | rus-eng | newstest2019 | 0.65476 | 41.6 | 2000 | 42642 | | rus-eng | newstest2020 | 0.64878 | 36.9 | 991 | 20217 | | rus-eng | newstestB2020 | 0.65685 | 39.3 | 991 | 20423 | | rus-eng | tico19-test | 0.63280 | 33.3 | 2100 | 56323 | ## 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 22:17:11 EET 2022 * port machine: LM0-400-22516.local