--- language: - ca - da - es - fr - gl - is - it - nb - pt - ro - sv tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-gmq-itc results: - task: name: Translation dan-cat type: translation args: dan-cat dataset: name: flores101-devtest type: flores_101 args: dan cat devtest metrics: - name: BLEU type: bleu value: 33.4 - name: chr-F type: chrf value: 0.59224 - task: name: Translation dan-fra type: translation args: dan-fra dataset: name: flores101-devtest type: flores_101 args: dan fra devtest metrics: - name: BLEU type: bleu value: 38.3 - name: chr-F type: chrf value: 0.63387 - task: name: Translation dan-glg type: translation args: dan-glg dataset: name: flores101-devtest type: flores_101 args: dan glg devtest metrics: - name: BLEU type: bleu value: 26.4 - name: chr-F type: chrf value: 0.54446 - task: name: Translation dan-ita type: translation args: dan-ita dataset: name: flores101-devtest type: flores_101 args: dan ita devtest metrics: - name: BLEU type: bleu value: 25.7 - name: chr-F type: chrf value: 0.55237 - task: name: Translation dan-por type: translation args: dan-por dataset: name: flores101-devtest type: flores_101 args: dan por devtest metrics: - name: BLEU type: bleu value: 36.9 - name: chr-F type: chrf value: 0.62233 - task: name: Translation dan-ron type: translation args: dan-ron dataset: name: flores101-devtest type: flores_101 args: dan ron devtest metrics: - name: BLEU type: bleu value: 31.8 - name: chr-F type: chrf value: 0.58235 - task: name: Translation dan-spa type: translation args: dan-spa dataset: name: flores101-devtest type: flores_101 args: dan spa devtest metrics: - name: BLEU type: bleu value: 24.3 - name: chr-F type: chrf value: 0.52453 - task: name: Translation isl-cat type: translation args: isl-cat dataset: name: flores101-devtest type: flores_101 args: isl cat devtest metrics: - name: BLEU type: bleu value: 22.7 - name: chr-F type: chrf value: 0.48930 - task: name: Translation isl-fra type: translation args: isl-fra dataset: name: flores101-devtest type: flores_101 args: isl fra devtest metrics: - name: BLEU type: bleu value: 26.2 - name: chr-F type: chrf value: 0.52704 - task: name: Translation isl-glg type: translation args: isl-glg dataset: name: flores101-devtest type: flores_101 args: isl glg devtest metrics: - name: BLEU type: bleu value: 18.0 - name: chr-F type: chrf value: 0.45387 - task: name: Translation isl-ita type: translation args: isl-ita dataset: name: flores101-devtest type: flores_101 args: isl ita devtest metrics: - name: BLEU type: bleu value: 18.6 - name: chr-F type: chrf value: 0.47303 - task: name: Translation isl-por type: translation args: isl-por dataset: name: flores101-devtest type: flores_101 args: isl por devtest metrics: - name: BLEU type: bleu value: 24.9 - name: chr-F type: chrf value: 0.51381 - task: name: Translation isl-ron type: translation args: isl-ron dataset: name: flores101-devtest type: flores_101 args: isl ron devtest metrics: - name: BLEU type: bleu value: 21.6 - name: chr-F type: chrf value: 0.48224 - task: name: Translation isl-spa type: translation args: isl-spa dataset: name: flores101-devtest type: flores_101 args: isl spa devtest metrics: - name: BLEU type: bleu value: 18.1 - name: chr-F type: chrf value: 0.45786 - task: name: Translation nob-cat type: translation args: nob-cat dataset: name: flores101-devtest type: flores_101 args: nob cat devtest metrics: - name: BLEU type: bleu value: 28.9 - name: chr-F type: chrf value: 0.55984 - task: name: Translation nob-fra type: translation args: nob-fra dataset: name: flores101-devtest type: flores_101 args: nob fra devtest metrics: - name: BLEU type: bleu value: 33.8 - name: chr-F type: chrf value: 0.60102 - task: name: Translation nob-glg type: translation args: nob-glg dataset: name: flores101-devtest type: flores_101 args: nob glg devtest metrics: - name: BLEU type: bleu value: 23.4 - name: chr-F type: chrf value: 0.52145 - task: name: Translation nob-ita type: translation args: nob-ita dataset: name: flores101-devtest type: flores_101 args: nob ita devtest metrics: - name: BLEU type: bleu value: 22.2 - name: chr-F type: chrf value: 0.52619 - task: name: Translation nob-por type: translation args: nob-por dataset: name: flores101-devtest type: flores_101 args: nob por devtest metrics: - name: BLEU type: bleu value: 32.2 - name: chr-F type: chrf value: 0.58836 - task: name: Translation nob-ron type: translation args: nob-ron dataset: name: flores101-devtest type: flores_101 args: nob ron devtest metrics: - name: BLEU type: bleu value: 27.6 - name: chr-F type: chrf value: 0.54845 - task: name: Translation nob-spa type: translation args: nob-spa dataset: name: flores101-devtest type: flores_101 args: nob spa devtest metrics: - name: BLEU type: bleu value: 21.8 - name: chr-F type: chrf value: 0.50661 - task: name: Translation swe-cat type: translation args: swe-cat dataset: name: flores101-devtest type: flores_101 args: swe cat devtest metrics: - name: BLEU type: bleu value: 32.4 - name: chr-F type: chrf value: 0.58542 - task: name: Translation swe-fra type: translation args: swe-fra dataset: name: flores101-devtest type: flores_101 args: swe fra devtest metrics: - name: BLEU type: bleu value: 39.3 - name: chr-F type: chrf value: 0.63688 - task: name: Translation swe-glg type: translation args: swe-glg dataset: name: flores101-devtest type: flores_101 args: swe glg devtest metrics: - name: BLEU type: bleu value: 26.0 - name: chr-F type: chrf value: 0.53989 - task: name: Translation swe-ita type: translation args: swe-ita dataset: name: flores101-devtest type: flores_101 args: swe ita devtest metrics: - name: BLEU type: bleu value: 25.9 - name: chr-F type: chrf value: 0.55232 - task: name: Translation swe-por type: translation args: swe-por dataset: name: flores101-devtest type: flores_101 args: swe por devtest metrics: - name: BLEU type: bleu value: 36.5 - name: chr-F type: chrf value: 0.61882 - task: name: Translation swe-ron type: translation args: swe-ron dataset: name: flores101-devtest type: flores_101 args: swe ron devtest metrics: - name: BLEU type: bleu value: 31.0 - name: chr-F type: chrf value: 0.57419 - task: name: Translation swe-spa type: translation args: swe-spa dataset: name: flores101-devtest type: flores_101 args: swe spa devtest metrics: - name: BLEU type: bleu value: 23.8 - name: chr-F type: chrf value: 0.52175 - task: name: Translation dan-fra type: translation args: dan-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-fra metrics: - name: BLEU type: bleu value: 63.8 - name: chr-F type: chrf value: 0.76671 - task: name: Translation dan-ita type: translation args: dan-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-ita metrics: - name: BLEU type: bleu value: 56.2 - name: chr-F type: chrf value: 0.74658 - task: name: Translation dan-por type: translation args: dan-por dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-por metrics: - name: BLEU type: bleu value: 57.8 - name: chr-F type: chrf value: 0.74944 - task: name: Translation dan-spa type: translation args: dan-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-spa metrics: - name: BLEU type: bleu value: 54.8 - name: chr-F type: chrf value: 0.72328 - task: name: Translation isl-ita type: translation args: isl-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: isl-ita metrics: - name: BLEU type: bleu value: 51.0 - name: chr-F type: chrf value: 0.69354 - task: name: Translation isl-spa type: translation args: isl-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: isl-spa metrics: - name: BLEU type: bleu value: 49.2 - name: chr-F type: chrf value: 0.66008 - task: name: Translation nob-fra type: translation args: nob-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nob-fra metrics: - name: BLEU type: bleu value: 54.4 - name: chr-F type: chrf value: 0.70854 - task: name: Translation nob-spa type: translation args: nob-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nob-spa metrics: - name: BLEU type: bleu value: 55.9 - name: chr-F type: chrf value: 0.73672 - task: name: Translation swe-fra type: translation args: swe-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-fra metrics: - name: BLEU type: bleu value: 59.2 - name: chr-F type: chrf value: 0.73014 - task: name: Translation swe-ita type: translation args: swe-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-ita metrics: - name: BLEU type: bleu value: 56.6 - name: chr-F type: chrf value: 0.73211 - task: name: Translation swe-por type: translation args: swe-por dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-por metrics: - name: BLEU type: bleu value: 48.7 - name: chr-F type: chrf value: 0.68146 - task: name: Translation swe-spa type: translation args: swe-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-spa metrics: - name: BLEU type: bleu value: 55.3 - name: chr-F type: chrf value: 0.71373 --- # opus-mt-tc-big-gmq-itc ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from North Germanic languages (gmq) to Italic languages (itc). 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). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-08-09 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): dan isl nno nob nor swe - Target Language(s): cat fra glg ita lat por ron spa - Language Pair(s): dan-cat dan-fra dan-glg dan-ita dan-por dan-ron dan-spa isl-cat isl-fra isl-ita isl-por isl-ron isl-spa nob-cat nob-fra nob-glg nob-ita nob-por nob-ron nob-spa swe-cat swe-fra swe-glg swe-ita swe-por swe-ron swe-spa - Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>osp_Latn<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<< - **Original Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT gmq-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-itc/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ 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. `>>fra<<` ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>spa<< Jag är inte religiös.", ">>por<< Livet er for kort til å lære seg tysk." ] model_name = "pytorch-models/opus-mt-tc-big-gmq-itc" 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: # No soy religioso. # A vida é muito curta para aprender alemão. ``` 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-itc") print(pipe(">>spa<< Jag är inte religiös.")) # expected output: No soy religioso. ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.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 | |----------|---------|-------|-------|-------|--------| | dan-fra | tatoeba-test-v2021-08-07 | 0.76671 | 63.8 | 1731 | 11882 | | dan-ita | tatoeba-test-v2021-08-07 | 0.74658 | 56.2 | 284 | 2226 | | dan-por | tatoeba-test-v2021-08-07 | 0.74944 | 57.8 | 873 | 5360 | | dan-spa | tatoeba-test-v2021-08-07 | 0.72328 | 54.8 | 5000 | 35528 | | isl-ita | tatoeba-test-v2021-08-07 | 0.69354 | 51.0 | 236 | 1450 | | isl-spa | tatoeba-test-v2021-08-07 | 0.66008 | 49.2 | 238 | 1229 | | nob-fra | tatoeba-test-v2021-08-07 | 0.70854 | 54.4 | 323 | 2269 | | nob-spa | tatoeba-test-v2021-08-07 | 0.73672 | 55.9 | 885 | 6866 | | swe-fra | tatoeba-test-v2021-08-07 | 0.73014 | 59.2 | 1407 | 9580 | | swe-ita | tatoeba-test-v2021-08-07 | 0.73211 | 56.6 | 715 | 4711 | | swe-por | tatoeba-test-v2021-08-07 | 0.68146 | 48.7 | 320 | 2032 | | swe-spa | tatoeba-test-v2021-08-07 | 0.71373 | 55.3 | 1351 | 8235 | | dan-cat | flores101-devtest | 0.59224 | 33.4 | 1012 | 27304 | | dan-fra | flores101-devtest | 0.63387 | 38.3 | 1012 | 28343 | | dan-glg | flores101-devtest | 0.54446 | 26.4 | 1012 | 26582 | | dan-ita | flores101-devtest | 0.55237 | 25.7 | 1012 | 27306 | | dan-por | flores101-devtest | 0.62233 | 36.9 | 1012 | 26519 | | dan-ron | flores101-devtest | 0.58235 | 31.8 | 1012 | 26799 | | dan-spa | flores101-devtest | 0.52453 | 24.3 | 1012 | 29199 | | isl-cat | flores101-devtest | 0.48930 | 22.7 | 1012 | 27304 | | isl-fra | flores101-devtest | 0.52704 | 26.2 | 1012 | 28343 | | isl-glg | flores101-devtest | 0.45387 | 18.0 | 1012 | 26582 | | isl-ita | flores101-devtest | 0.47303 | 18.6 | 1012 | 27306 | | isl-por | flores101-devtest | 0.51381 | 24.9 | 1012 | 26519 | | isl-ron | flores101-devtest | 0.48224 | 21.6 | 1012 | 26799 | | isl-spa | flores101-devtest | 0.45786 | 18.1 | 1012 | 29199 | | nob-cat | flores101-devtest | 0.55984 | 28.9 | 1012 | 27304 | | nob-fra | flores101-devtest | 0.60102 | 33.8 | 1012 | 28343 | | nob-glg | flores101-devtest | 0.52145 | 23.4 | 1012 | 26582 | | nob-ita | flores101-devtest | 0.52619 | 22.2 | 1012 | 27306 | | nob-por | flores101-devtest | 0.58836 | 32.2 | 1012 | 26519 | | nob-ron | flores101-devtest | 0.54845 | 27.6 | 1012 | 26799 | | nob-spa | flores101-devtest | 0.50661 | 21.8 | 1012 | 29199 | | swe-cat | flores101-devtest | 0.58542 | 32.4 | 1012 | 27304 | | swe-fra | flores101-devtest | 0.63688 | 39.3 | 1012 | 28343 | | swe-glg | flores101-devtest | 0.53989 | 26.0 | 1012 | 26582 | | swe-ita | flores101-devtest | 0.55232 | 25.9 | 1012 | 27306 | | swe-por | flores101-devtest | 0.61882 | 36.5 | 1012 | 26519 | | swe-ron | flores101-devtest | 0.57419 | 31.0 | 1012 | 26799 | | swe-spa | flores101-devtest | 0.52175 | 23.8 | 1012 | 29199 | ## Citation Information * 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", } ``` ## 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: 8b9f0b0 * port time: Sat Aug 13 00:00:00 EEST 2022 * port machine: LM0-400-22516.local