--- language: - da - gmq - is - nb - false - ru - sv - uk - zle tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-gmq-zle results: - task: name: Translation dan-rus type: translation args: dan-rus dataset: name: flores101-devtest type: flores_101 args: dan rus devtest metrics: - name: BLEU type: bleu value: 25.6 - task: name: Translation dan-ukr type: translation args: dan-ukr dataset: name: flores101-devtest type: flores_101 args: dan ukr devtest metrics: - name: BLEU type: bleu value: 25.5 - task: name: Translation nob-rus type: translation args: nob-rus dataset: name: flores101-devtest type: flores_101 args: nob rus devtest metrics: - name: BLEU type: bleu value: 22.1 - task: name: Translation nob-ukr type: translation args: nob-ukr dataset: name: flores101-devtest type: flores_101 args: nob ukr devtest metrics: - name: BLEU type: bleu value: 21.6 - task: name: Translation swe-rus type: translation args: swe-rus dataset: name: flores101-devtest type: flores_101 args: swe rus devtest metrics: - name: BLEU type: bleu value: 25.8 - task: name: Translation swe-ukr type: translation args: swe-ukr dataset: name: flores101-devtest type: flores_101 args: swe ukr devtest metrics: - name: BLEU type: bleu value: 25.7 - task: name: Translation dan-rus type: translation args: dan-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-rus metrics: - name: BLEU type: bleu value: 53.9 - task: name: Translation nob-rus type: translation args: nob-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nob-rus metrics: - name: BLEU type: bleu value: 45.8 - task: name: Translation swe-rus type: translation args: swe-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-rus metrics: - name: BLEU type: bleu value: 45.9 --- # opus-mt-tc-big-gmq-zle Neural machine translation model for translating from North Germanic languages (gmq) 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: 2022-03-23 * source language(s): dan isl nob nor swe * target language(s): rus ukr * valid target language labels: >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pbt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zle/opusTCv20210807+pbt_transformer-big_2022-03-23.zip) * more information released models: [OPUS-MT gmq-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-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. `>>rus<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>bel<< Det er allerede torsdag i morgen.", ">>ukr<< Tom lekte katt och råtta med Mary." ] model_name = "pytorch-models/opus-mt-tc-big-gmq-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: # Гэта ўжо чацвер заўтра. # Том грав кішку і щура з Марією. ``` 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-gmq-zle") print(pipe(">>bel<< Det er allerede torsdag i morgen.")) # expected output: Гэта ўжо чацвер заўтра. ``` ## Benchmarks * test set translations: [opusTCv20210807+pbt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zle/opusTCv20210807+pbt_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807+pbt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zle/opusTCv20210807+pbt_transformer-big_2022-03-23.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 | |----------|---------|-------|-------|-------|--------| | dan-rus | tatoeba-test-v2021-08-07 | 0.72627 | 53.9 | 1713 | 10480 | | nob-rus | tatoeba-test-v2021-08-07 | 0.66881 | 45.8 | 1277 | 10659 | | swe-rus | tatoeba-test-v2021-08-07 | 0.66248 | 45.9 | 1282 | 7659 | | dan-rus | flores101-devtest | 0.53271 | 25.6 | 1012 | 23295 | | dan-ukr | flores101-devtest | 0.54273 | 25.5 | 1012 | 22810 | | nob-rus | flores101-devtest | 0.50426 | 22.1 | 1012 | 23295 | | nob-ukr | flores101-devtest | 0.51156 | 21.6 | 1012 | 22810 | | swe-rus | flores101-devtest | 0.53226 | 25.8 | 1012 | 23295 | | swe-ukr | flores101-devtest | 0.54257 | 25.7 | 1012 | 22810 | ## 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 02:08:53 EET 2022 * port machine: LM0-400-22516.local