| | --- |
| | library_name: transformers |
| | language: |
| | - be |
| | - bg |
| | - bs |
| | - cs |
| | - csb |
| | - cu |
| | - dsb |
| | - en |
| | - hr |
| | - hsb |
| | - mk |
| | - orv |
| | - pl |
| | - ru |
| | - rue |
| | - sh |
| | - sk |
| | - sl |
| | - sr |
| | - szl |
| | - uk |
| |
|
| | tags: |
| | - translation |
| | - opus-mt-tc-bible |
| |
|
| | license: apache-2.0 |
| | model-index: |
| | - name: opus-mt-tc-bible-big-sla-en |
| | results: |
| | - task: |
| | name: Translation multi-eng |
| | type: translation |
| | args: multi-eng |
| | dataset: |
| | name: tatoeba-test-v2020-07-28-v2023-09-26 |
| | type: tatoeba_mt |
| | args: multi-eng |
| | metrics: |
| | - name: BLEU |
| | type: bleu |
| | value: 55.6 |
| | - name: chr-F |
| | type: chrf |
| | value: 0.70473 |
| | --- |
| | # opus-mt-tc-bible-big-sla-en |
| |
|
| | ## 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 Slavic languages (sla) 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). |
| | **Model Description:** |
| | - **Developed by:** Language Technology Research Group at the University of Helsinki |
| | - **Model Type:** Translation (transformer-big) |
| | - **Release**: 2024-08-17 |
| | - **License:** Apache-2.0 |
| | - **Language(s):** |
| | - Source Language(s): bel bos bul ces chu cnr csb dsb hbs hrv hsb mkd orv pol rue rus slk slv srp szl ukr |
| | - Target Language(s): eng |
| | - **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip) |
| | - **Resources for more information:** |
| | - [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/sla-eng/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17) |
| | - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
| | - [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/) |
| | - [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1) |
| | - [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/) |
| |
|
| | ## 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 = [ |
| | "Nie winię ciebie.", |
| | "Так это по-немецки сказать нельзя." |
| | ] |
| | |
| | model_name = "pytorch-models/opus-mt-tc-bible-big-sla-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 don't blame you. |
| | # You can't say that in German. |
| | ``` |
| |
|
| | 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-bible-big-sla-en") |
| | print(pipe("Nie winię ciebie.")) |
| | |
| | # expected output: I don't blame you. |
| | ``` |
| |
|
| | ## Training |
| |
|
| | - **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
| | - **Pre-processing**: SentencePiece (spm32k,spm32k) |
| | - **Model Type:** transformer-big |
| | - **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip) |
| | - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
| |
|
| | ## Evaluation |
| |
|
| | * [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/sla-eng/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17) |
| | * test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt) |
| | * test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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 | |
| | |----------|---------|-------|-------|-------|--------| |
| | | multi-eng | tatoeba-test-v2020-07-28-v2023-09-26 | 0.70473 | 55.6 | 10000 | 74777 | |
| |
|
| | ## Citation Information |
| |
|
| | * Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [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.) |
| |
|
| | ```bibtex |
| | @article{tiedemann2023democratizing, |
| | title={Democratizing neural machine translation with {OPUS-MT}}, |
| | author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami}, |
| | journal={Language Resources and Evaluation}, |
| | number={58}, |
| | pages={713--755}, |
| | year={2023}, |
| | publisher={Springer Nature}, |
| | issn={1574-0218}, |
| | doi={10.1007/s10579-023-09704-w} |
| | } |
| | |
| | @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 [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/). |
| |
|
| | ## Model conversion info |
| |
|
| | * transformers version: 4.45.1 |
| | * OPUS-MT git hash: 0882077 |
| | * port time: Tue Oct 8 22:44:35 EEST 2024 |
| | * port machine: LM0-400-22516.local |
| |
|
