--- language: - cs - da - nb - pl - sv tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-gmq-zlw results: - task: name: Translation dan-ces type: translation args: dan-ces dataset: name: flores101-devtest type: flores_101 args: dan ces devtest metrics: - name: BLEU type: bleu value: 26.7 - name: chr-F type: chrf value: 0.54065 - task: name: Translation dan-pol type: translation args: dan-pol dataset: name: flores101-devtest type: flores_101 args: dan pol devtest metrics: - name: BLEU type: bleu value: 18.8 - name: chr-F type: chrf value: 0.48389 - task: name: Translation isl-ces type: translation args: isl-ces dataset: name: flores101-devtest type: flores_101 args: isl ces devtest metrics: - name: BLEU type: bleu value: 17.7 - name: chr-F type: chrf value: 0.43582 - task: name: Translation isl-pol type: translation args: isl-pol dataset: name: flores101-devtest type: flores_101 args: isl pol devtest metrics: - name: BLEU type: bleu value: 13.9 - name: chr-F type: chrf value: 0.41929 - task: name: Translation nob-ces type: translation args: nob-ces dataset: name: flores101-devtest type: flores_101 args: nob ces devtest metrics: - name: BLEU type: bleu value: 22.3 - name: chr-F type: chrf value: 0.50336 - task: name: Translation nob-pol type: translation args: nob-pol dataset: name: flores101-devtest type: flores_101 args: nob pol devtest metrics: - name: BLEU type: bleu value: 16.3 - name: chr-F type: chrf value: 0.46130 - task: name: Translation swe-ces type: translation args: swe-ces dataset: name: flores101-devtest type: flores_101 args: swe ces devtest metrics: - name: BLEU type: bleu value: 25.7 - name: chr-F type: chrf value: 0.53188 - task: name: Translation swe-pol type: translation args: swe-pol dataset: name: flores101-devtest type: flores_101 args: swe pol devtest metrics: - name: BLEU type: bleu value: 18.6 - name: chr-F type: chrf value: 0.48163 - task: name: Translation swe-pol type: translation args: swe-pol dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-pol metrics: - name: BLEU type: bleu value: 46.2 - name: chr-F type: chrf value: 0.66326 --- # opus-mt-tc-big-gmq-zlw ## 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 West Slavic languages (zlw). 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**: 2022-08-03 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): dan nob nor swe - Target Language(s): ces pol - Language Pair(s): dan-ces nob-ces swe-ces swe-pol - Valid Target Language Labels: >>ces<< >>csb<< >>czk<< >>dsb<< >>hsb<< >>pol<< >>pox<< >>slk<< >>szl<< - **Original Model**: [opusTCv20210807_transformer-big_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zlw/opusTCv20210807_transformer-big_2022-08-03.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-zlw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-zlw/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. `>>ces<<` ## 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 = [ ">>ces<< Normalt er jeg hjemme hele weekenden.", ">>pol<< Lev ditt liv." ] model_name = "pytorch-models/opus-mt-tc-big-gmq-zlw" 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: # Většinou jsem doma celý víkend. # Żyj swoim życiem. ``` 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-zlw") print(pipe(">>ces<< Normalt er jeg hjemme hele weekenden.")) # expected output: Většinou jsem doma celý víkend. ``` ## 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_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zlw/opusTCv20210807_transformer-big_2022-08-03.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zlw/opusTCv20210807_transformer-big_2022-08-03.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-zlw/opusTCv20210807_transformer-big_2022-08-03.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 | |----------|---------|-------|-------|-------|--------| | swe-pol | tatoeba-test-v2021-08-07 | 0.66326 | 46.2 | 1392 | 8157 | | dan-ces | flores101-devtest | 0.54065 | 26.7 | 1012 | 22101 | | dan-pol | flores101-devtest | 0.48389 | 18.8 | 1012 | 22520 | | isl-ces | flores101-devtest | 0.43582 | 17.7 | 1012 | 22101 | | isl-pol | flores101-devtest | 0.41929 | 13.9 | 1012 | 22520 | | nob-ces | flores101-devtest | 0.50336 | 22.3 | 1012 | 22101 | | nob-pol | flores101-devtest | 0.46130 | 16.3 | 1012 | 22520 | | swe-ces | flores101-devtest | 0.53188 | 25.7 | 1012 | 22101 | | swe-pol | flores101-devtest | 0.48163 | 18.6 | 1012 | 22520 | ## 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 00:02:29 EEST 2022 * port machine: LM0-400-22516.local