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--- |
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language: |
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- be |
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- cs |
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- pl |
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- ru |
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- uk |
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- zle |
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- zlw |
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tags: |
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- translation |
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- opus-mt-tc |
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-big-zle-zlw |
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results: |
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- task: |
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name: Translation rus-ces |
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type: translation |
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args: rus-ces |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus ces devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 23.1 |
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- task: |
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name: Translation ukr-ces |
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type: translation |
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args: ukr-ces |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr ces devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 25.1 |
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- task: |
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name: Translation bel-pol |
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type: translation |
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args: bel-pol |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: bel-pol |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 47.1 |
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- task: |
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name: Translation rus-ces |
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type: translation |
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args: rus-ces |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: rus-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 53.4 |
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- task: |
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name: Translation rus-pol |
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type: translation |
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args: rus-pol |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: rus-pol |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 53.7 |
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- task: |
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name: Translation ukr-ces |
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type: translation |
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args: ukr-ces |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: ukr-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 58.0 |
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- task: |
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name: Translation ukr-pol |
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type: translation |
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args: ukr-pol |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: ukr-pol |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 57.0 |
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- task: |
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name: Translation rus-ces |
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type: translation |
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args: rus-ces |
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dataset: |
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name: newstest2013 |
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type: wmt-2013-news |
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args: rus-ces |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 26.0 |
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--- |
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# opus-mt-tc-big-zle-zlw |
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Neural machine translation model for translating from East Slavic languages (zle) to West Slavic languages (zlw). |
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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). |
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* 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.) |
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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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## Model info |
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* Release: 2022-03-23 |
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* source language(s): bel rus ukr |
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* target language(s): ces pol |
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* valid target language labels: >>ces<< >>pol<< |
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* model: transformer-big |
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* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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* tokenization: SentencePiece (spm32k,spm32k) |
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* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.zip) |
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* more information released models: [OPUS-MT zle-zlw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zlw/README.md) |
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* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) |
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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<<` |
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## Usage |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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">>pol<< Это метафора.", |
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">>pol<< Что вы делали?" |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-zle-zlw" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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# expected output: |
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# To metafora. |
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# Co robiliście? |
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``` |
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zlw") |
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print(pipe(">>pol<< Это метафора.")) |
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# expected output: To metafora. |
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``` |
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## Benchmarks |
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt) |
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* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| bel-pol | tatoeba-test-v2021-08-07 | 0.65517 | 47.1 | 287 | 1706 | |
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| rus-ces | tatoeba-test-v2021-08-07 | 0.69695 | 53.4 | 2934 | 16831 | |
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| rus-pol | tatoeba-test-v2021-08-07 | 0.72176 | 53.7 | 3543 | 21505 | |
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| ukr-ces | tatoeba-test-v2021-08-07 | 0.73149 | 58.0 | 1787 | 8550 | |
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| ukr-pol | tatoeba-test-v2021-08-07 | 0.74649 | 57.0 | 2519 | 13201 | |
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| bel-ces | flores101-devtest | 0.41248 | 11.1 | 1012 | 22101 | |
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| bel-pol | flores101-devtest | 0.42240 | 10.2 | 1012 | 22520 | |
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| rus-ces | flores101-devtest | 0.50971 | 23.1 | 1012 | 22101 | |
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| rus-pol | flores101-devtest | 0.48672 | 18.4 | 1012 | 22520 | |
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| ukr-ces | flores101-devtest | 0.52482 | 25.1 | 1012 | 22101 | |
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| ukr-pol | flores101-devtest | 0.48790 | 18.8 | 1012 | 22520 | |
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| rus-ces | newstest2012 | 0.45834 | 18.8 | 3003 | 65456 | |
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| rus-ces | newstest2013 | 0.52364 | 26.0 | 3000 | 57250 | |
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## Acknowledgements |
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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. |
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## Model conversion info |
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 1bdabf7 |
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* port time: Thu Mar 24 00:50:29 EET 2022 |
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* port machine: LM0-400-22516.local |
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