readme: ---
  language:
  - tr
  - uk
  tags:
  - translation
  - opus-mt-tc
  license: cc-by-4.0
  model-index:
  - name: opus-mt-tc-base-tr-uk
    results:
    - task:
        name: Translation tur-ukr
        type: translation
        args: tur-ukr
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: tur-ukr
      metrics:
      - name: BLEU
        type: bleu
        value: 40.5
  ---
  # opus-mt-tc-base-tr-uk
  
  Neural machine translation model for translating from Turkish (tr) to Ukrainian (uk).
  
  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: 2022-03-07
  * source language(s): 
  * target language(s): ukr
  * model: transformer-align
  * data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
  * tokenization: SentencePiece (spm32k,spm32k)
  * original model: [opusTCv20210807+pbt_transformer-align_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.zip)
  * more information released models: [OPUS-MT tur-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md)
  
  ## Usage
  
  A short example code:
  
  ```python
  from transformers import MarianMTModel, MarianTokenizer
  
  src_text = [
      "1000 yen yeterli mi?",
      "Zürih, İsviçre'de bir şehirdir."
  ]
  
  model_name = "pytorch-models/opus-mt-tc-base-tr-uk"
  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:
  #     Чи достатньо 1000 ієн?
  #     Цюрих - місто в Швейцарії.
  ```
  
  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-base-tr-uk")
  print(pipe("1000 yen yeterli mi?"))
  
  # expected output: Чи достатньо 1000 ієн?
  ```
  
  ## Benchmarks
  
  * test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt)
  * test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.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 |
  |----------|---------|-------|-------|-------|--------|
  | tur-ukr | tatoeba-test-v2021-08-07 | 0.63573 | 40.5 | 2520 | 13079 |
  | tur-ukr | flores101-devtest | 0.49944 | 19.9 | 1012 | 22810 |
  
  ## 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-groundin