| --- |
| language: |
| - ar |
| - en |
| tags: |
| - translation |
| - opus-mt-tc |
| license: cc-by-4.0 |
| model-index: |
| - name: opus-mt-tc-big-en-ar |
| results: |
| - task: |
| name: Translation eng-ara |
| type: translation |
| args: eng-ara |
| dataset: |
| name: flores101-devtest |
| type: flores_101 |
| args: eng ara devtest |
| metrics: |
| - name: BLEU |
| type: bleu |
| value: 29.4 |
| - task: |
| name: Translation eng-ara |
| type: translation |
| args: eng-ara |
| dataset: |
| name: tatoeba-test-v2020-07-28 |
| type: tatoeba_mt |
| args: eng-ara |
| metrics: |
| - name: BLEU |
| type: bleu |
| value: 20.0 |
| - task: |
| name: Translation eng-ara |
| type: translation |
| args: eng-ara |
| dataset: |
| name: tico19-test |
| type: tico19-test |
| args: eng-ara |
| metrics: |
| - name: BLEU |
| type: bleu |
| value: 30.0 |
| --- |
| # opus-mt-tc-big-en-ar |
|
|
| Neural machine translation model for translating from English (en) to Arabic (ar). |
|
|
| 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: 2022-02-25 |
| * source language(s): eng |
| * target language(s): afb ara |
| * valid target language labels: >>afb<< >>ara<< |
| * model: transformer-big |
| * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
| * tokenization: SentencePiece (spm32k,spm32k) |
| * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ara/opusTCv20210807+bt_transformer-big_2022-02-25.zip) |
| * more information released models: [OPUS-MT eng-ara README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ara/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. `>>afb<<` |
|
|
| ## Usage |
|
|
| A short example code: |
|
|
| ```python |
| from transformers import MarianMTModel, MarianTokenizer |
| |
| src_text = [ |
| ">>ara<< I can't help you because I'm busy.", |
| ">>ara<< I have to write a letter. Do you have some paper?" |
| ] |
| |
| model_name = "pytorch-models/opus-mt-tc-big-en-ar" |
| 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: |
| # لا أستطيع مساعدتك لأنني مشغول. |
| # يجب أن أكتب رسالة هل لديك بعض الأوراق؟ |
| ``` |
|
|
| 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-en-ar") |
| print(pipe(">>ara<< I can't help you because I'm busy.")) |
| |
| # expected output: لا أستطيع مساعدتك لأنني مشغول. |
| ``` |
|
|
| ## Benchmarks |
|
|
| * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ara/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) |
| * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ara/opusTCv20210807+bt_transformer-big_2022-02-25.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 | |
| |----------|---------|-------|-------|-------|--------| |
| | eng-ara | tatoeba-test-v2021-08-07 | 0.48813 | 19.8 | 10305 | 61356 | |
| | eng-ara | flores101-devtest | 0.61154 | 29.4 | 1012 | 21357 | |
| | eng-ara | tico19-test | 0.60075 | 30.0 | 2100 | 51339 | |
|
|
| ## 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: 3405783 |
| * port time: Wed Apr 13 16:37:31 EEST 2022 |
| * port machine: LM0-400-22516.local |
|
|
