--- language: - en - fr tags: - translation license: cc-by-4.0 model-index: - name: opus-mt-tc-big-fr-en results: - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: flores101-devtest type: flores_101 args: fra eng devtest metrics: - name: BLEU type: bleu value: 46.0 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: fra-eng metrics: - name: BLEU type: bleu value: 49.7 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: multi30k_test_2017_flickr type: multi30k-2017_flickr args: fra-eng metrics: - name: BLEU type: bleu value: 52.0 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: multi30k_test_2017_mscoco type: multi30k-2017_mscoco args: fra-eng metrics: - name: BLEU type: bleu value: 50.6 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: fra-eng metrics: - name: BLEU type: bleu value: 44.9 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: news-test2008 type: news-test2008 args: fra-eng metrics: - name: BLEU type: bleu value: 26.5 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newsdiscussdev2015 type: newsdiscussdev2015 args: fra-eng metrics: - name: BLEU type: bleu value: 34.4 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newsdiscusstest2015 type: newsdiscusstest2015 args: fra-eng metrics: - name: BLEU type: bleu value: 40.2 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fra-eng metrics: - name: BLEU type: bleu value: 59.8 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: tico19-test type: tico19-test args: fra-eng metrics: - name: BLEU type: bleu value: 41.3 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2009 type: wmt-2009-news args: fra-eng metrics: - name: BLEU type: bleu value: 30.4 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2010 type: wmt-2010-news args: fra-eng metrics: - name: BLEU type: bleu value: 33.4 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2011 type: wmt-2011-news args: fra-eng metrics: - name: BLEU type: bleu value: 33.8 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2012 type: wmt-2012-news args: fra-eng metrics: - name: BLEU type: bleu value: 33.6 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2013 type: wmt-2013-news args: fra-eng metrics: - name: BLEU type: bleu value: 34.8 - task: name: Translation fra-eng type: translation args: fra-eng dataset: name: newstest2014 type: wmt-2014-news args: fra-eng metrics: - name: BLEU type: bleu value: 39.4 --- # opus-mt-tc-big-fr-en Neural machine translation model for translating from French (fr) 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: 2022-03-09 * source language(s): fra * 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_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip) * more information released models: [OPUS-MT fra-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "J'ai adoré l'Angleterre.", "C'était la seule chose à faire." ] model_name = "pytorch-models/opus-mt-tc-big-fr-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: # I loved England. # It was the only thing to do. ``` 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-fr-en") print(pipe("J'ai adoré l'Angleterre.")) # expected output: I loved England. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opusTCv20210807+bt_transformer-big_2022-03-09.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 | |----------|---------|-------|-------|-------|--------| | fra-eng | tatoeba-test-v2021-08-07 | 0.73772 | 59.8 | 12681 | 101754 | | fra-eng | flores101-devtest | 0.69350 | 46.0 | 1012 | 24721 | | fra-eng | multi30k_test_2016_flickr | 0.68005 | 49.7 | 1000 | 12955 | | fra-eng | multi30k_test_2017_flickr | 0.70596 | 52.0 | 1000 | 11374 | | fra-eng | multi30k_test_2017_mscoco | 0.69356 | 50.6 | 461 | 5231 | | fra-eng | multi30k_test_2018_flickr | 0.65751 | 44.9 | 1071 | 14689 | | fra-eng | newsdiscussdev2015 | 0.59008 | 34.4 | 1500 | 27759 | | fra-eng | newsdiscusstest2015 | 0.62603 | 40.2 | 1500 | 26982 | | fra-eng | newssyscomb2009 | 0.57488 | 31.1 | 502 | 11818 | | fra-eng | news-test2008 | 0.54316 | 26.5 | 2051 | 49380 | | fra-eng | newstest2009 | 0.56959 | 30.4 | 2525 | 65399 | | fra-eng | newstest2010 | 0.59561 | 33.4 | 2489 | 61711 | | fra-eng | newstest2011 | 0.60271 | 33.8 | 3003 | 74681 | | fra-eng | newstest2012 | 0.59507 | 33.6 | 3003 | 72812 | | fra-eng | newstest2013 | 0.59691 | 34.8 | 3000 | 64505 | | fra-eng | newstest2014 | 0.64533 | 39.4 | 3003 | 70708 | | fra-eng | tico19-test | 0.63326 | 41.3 | 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: 3405783 * port time: Wed Apr 13 19:02:28 EEST 2022 * port machine: LM0-400-22516.local