readme: ---
  language:
  - bg
  - bs
  - en
  - hr
  - mk
  - sh
  - sl
  - sr
  - zls
  tags:
  - translation
  - opus-mt-tc
  license: cc-by-4.0
  model-index:
  - name: opus-mt-tc-big-zls-en
    results:
    - task:
        name: Translation bul-eng
        type: translation
        args: bul-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: bul eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 42.0
    - task:
        name: Translation hrv-eng
        type: translation
        args: hrv-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: hrv eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 37.1
    - task:
        name: Translation mkd-eng
        type: translation
        args: mkd-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: mkd eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 43.2
    - task:
        name: Translation slv-eng
        type: translation
        args: slv-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: slv eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 35.2
    - task:
        name: Translation srp_Cyrl-eng
        type: translation
        args: srp_Cyrl-eng
      dataset:
        name: flores101-devtest
        type: flores_101
        args: srp_Cyrl eng devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 36.8
    - task:
        name: Translation bos_Latn-eng
        type: translation
        args: bos_Latn-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: bos_Latn-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 66.5
    - task:
        name: Translation bul-eng
        type: translation
        args: bul-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: bul-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 59.3
    - task:
        name: Translation hbs-eng
        type: translation
        args: hbs-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: hbs-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 57.3
    - task:
        name: Translation hrv-eng
        type: translation
        args: hrv-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: hrv-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 59.2
    - task:
        name: Translation mkd-eng
        type: translation
        args: mkd-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: mkd-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 57.4
    - task:
        name: Translation slv-eng
        type: translation
        args: slv-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: slv-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 23.5
    - task:
        name: Translation srp_Cyrl-eng
        type: translation
        args: srp_Cyrl-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: srp_Cyrl-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 47.0
    - task:
        name: Translation srp_Latn-eng
        type: translation
        args: srp_Latn-eng
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: srp_Latn-eng
      metrics:
      - name: BLEU
        type: bleu
        value: 58.5
  ---
  # opus-mt-tc-big-zls-en
  
  Neural machine translation model for translating from South Slavic languages (zls) 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).
  
  * 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