--- language: - bg - de - hr - mk - sh - sl - sr - multilingual license: cc-by-4.0 tags: - translation - opus-mt-tc language_bcp47: - sr_Cyrl - sr_Latn model-index: - name: opus-mt-tc-big-zls-de results: - task: type: translation name: Translation bul-deu dataset: name: flores101-devtest type: flores_101 args: bul deu devtest metrics: - type: bleu value: 28.4 name: BLEU - type: chrf value: 0.57688 name: chr-F - type: bleu value: 27.4 name: BLEU - type: chrf value: 0.56674 name: chr-F - type: bleu value: 29.3 name: BLEU - type: chrf value: 0.57688 name: chr-F - type: bleu value: 26.7 name: BLEU - type: chrf value: 0.56258 name: chr-F - type: bleu value: 30.7 name: BLEU - type: chrf value: 0.59271 name: chr-F - task: type: translation name: Translation bul-deu dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-deu metrics: - type: bleu value: 54.5 name: BLEU - type: chrf value: 0.7122 name: chr-F - type: bleu value: 54.8 name: BLEU - type: chrf value: 0.71283 name: chr-F - type: bleu value: 53.1 name: BLEU - type: chrf value: 0.69448 name: chr-F - type: bleu value: 21.1 name: BLEU - type: chrf value: 0.36339 name: chr-F - type: bleu value: 56.0 name: BLEU - type: chrf value: 0.72489 name: chr-F --- # opus-mt-tc-big-zls-de ## 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 South Slavic languages (zls) to German (de). 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**: 2022-07-26 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn - Target Language(s): deu - Language Pair(s): bul-deu hbs-deu hrv-deu mkd-deu slv-deu srp_Cyrl-deu srp_Latn-deu - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.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 zls-deu README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-deu/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/ ## 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 = [ "Jesi li ti student?", "Dve stvari deca treba da dobiju od svojih roditelja: korene i krila." ] model_name = "pytorch-models/opus-mt-tc-big-zls-de" 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: # Sind Sie Student? # Zwei Dinge sollten Kinder von ihren Eltern bekommen: Wurzeln und Flügel. ``` 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-zls-de") print(pipe("Jesi li ti student?")) # expected output: Sind Sie Student? ``` ## 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_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.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 | |----------|---------|-------|-------|-------|--------| | bul-deu | tatoeba-test-v2021-08-07 | 0.71220 | 54.5 | 314 | 2224 | | hbs-deu | tatoeba-test-v2021-08-07 | 0.71283 | 54.8 | 1959 | 15559 | | hrv-deu | tatoeba-test-v2021-08-07 | 0.69448 | 53.1 | 782 | 5734 | | slv-deu | tatoeba-test-v2021-08-07 | 0.36339 | 21.1 | 492 | 3003 | | srp_Latn-deu | tatoeba-test-v2021-08-07 | 0.72489 | 56.0 | 986 | 8500 | | bul-deu | flores101-devtest | 0.57688 | 28.4 | 1012 | 25094 | | hrv-deu | flores101-devtest | 0.56674 | 27.4 | 1012 | 25094 | | mkd-deu | flores101-devtest | 0.57688 | 29.3 | 1012 | 25094 | | slv-deu | flores101-devtest | 0.56258 | 26.7 | 1012 | 25094 | | srp_Cyrl-deu | flores101-devtest | 0.59271 | 30.7 | 1012 | 25094 | ## 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 00:05:30 EEST 2022 * port machine: LM0-400-22516.local