--- language: - ces - slk - cs - sk - en tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-en-ces_slk results: - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: flores101-devtest type: flores_101 args: eng ces devtest metrics: - name: BLEU type: bleu value: 34.1 - task: name: Translation eng-slk type: translation args: eng-slk dataset: name: flores101-devtest type: flores_101 args: eng slk devtest metrics: - name: BLEU type: bleu value: 35.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: eng-ces metrics: - name: BLEU type: bleu value: 33.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: eng-ces metrics: - name: BLEU type: bleu value: 33.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: news-test2008 type: news-test2008 args: eng-ces metrics: - name: BLEU type: bleu value: 22.8 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-ces metrics: - name: BLEU type: bleu value: 47.5 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2009 type: wmt-2009-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.3 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2010 type: wmt-2010-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2011 type: wmt-2011-news args: eng-ces metrics: - name: BLEU type: bleu value: 25.5 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2012 type: wmt-2012-news args: eng-ces metrics: - name: BLEU type: bleu value: 22.6 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2013 type: wmt-2013-news args: eng-ces metrics: - name: BLEU type: bleu value: 27.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2014 type: wmt-2014-news args: eng-ces metrics: - name: BLEU type: bleu value: 31.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2015 type: wmt-2015-news args: eng-ces metrics: - name: BLEU type: bleu value: 27.0 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2016 type: wmt-2016-news args: eng-ces metrics: - name: BLEU type: bleu value: 29.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2017 type: wmt-2017-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2018 type: wmt-2018-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.6 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2019 type: wmt-2019-news args: eng-ces metrics: - name: BLEU type: bleu value: 26.4 --- # opus-mt-tc-big-en-ces_slk Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk). 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): ces * 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-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-ces+slk README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ces+slk/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ces<< We were enemies.", ">>ces<< Do you think Tom knows what's going on?" ] model_name = "pytorch-models/opus-mt-tc-big-en-ces_slk" 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: # Byli jsme nepřátelé. # Myslíš, že Tom ví, co se děje? ``` 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-ces_slk") print(pipe(">>ces<< We were enemies.")) # expected output: Byli jsme nepřátelé. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/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-ces+slk/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-ces | tatoeba-test-v2021-08-07 | 0.66128 | 47.5 | 13824 | 91332 | | eng-ces | flores101-devtest | 0.60411 | 34.1 | 1012 | 22101 | | eng-slk | flores101-devtest | 0.62415 | 35.9 | 1012 | 22543 | | eng-ces | multi30k_test_2016_flickr | 0.58547 | 33.4 | 1000 | 10503 | | eng-ces | multi30k_test_2018_flickr | 0.59236 | 33.4 | 1071 | 11631 | | eng-ces | newssyscomb2009 | 0.52702 | 25.3 | 502 | 10032 | | eng-ces | news-test2008 | 0.50286 | 22.8 | 2051 | 42484 | | eng-ces | newstest2009 | 0.52152 | 24.3 | 2525 | 55533 | | eng-ces | newstest2010 | 0.52527 | 24.4 | 2489 | 52955 | | eng-ces | newstest2011 | 0.52721 | 25.5 | 3003 | 65653 | | eng-ces | newstest2012 | 0.50007 | 22.6 | 3003 | 65456 | | eng-ces | newstest2013 | 0.53643 | 27.4 | 3000 | 57250 | | eng-ces | newstest2014 | 0.58944 | 31.4 | 3003 | 59902 | | eng-ces | newstest2015 | 0.55094 | 27.0 | 2656 | 45858 | | eng-ces | newstest2016 | 0.56864 | 29.9 | 2999 | 56998 | | eng-ces | newstest2017 | 0.52504 | 24.9 | 3005 | 54361 | | eng-ces | newstest2018 | 0.52490 | 24.6 | 2983 | 54652 | | eng-ces | newstest2019 | 0.53994 | 26.4 | 1997 | 43113 | ## 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: 3405783 * port time: Wed Apr 13 16:46:48 EEST 2022 * port machine: LM0-400-22516.local