readme: ---
  language:
  - en
  - fi
  tags:
  - translation
  - opus-mt-tc
  license: cc-by-4.0
  model-index:
  - name: opus-mt-tc-big-en-fi
    results:
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: flores101-devtest
        type: flores_101
        args: eng fin devtest
      metrics:
      - name: BLEU
        type: bleu
        value: 27.6
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: newsdev2015
        type: newsdev2015
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 24.2
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 39.3
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: newstest2015
        type: wmt-2015-news
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 26.4
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: newstest2016
        type: wmt-2016-news
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 28.8
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: newstest2017
        type: wmt-2017-news
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 31.3
    - task:
        name: Translation eng-fin
        type: translation
        args: eng-fin
      dataset:
        name: newstest2019
        type: wmt-2019-news
        args: eng-fin
      metrics:
      - name: BLEU
        type: bleu
        value: 26.4
  ---
  # opus-mt-tc-big-en-fi
  
  Neural machine translation model for translating from English (en) to Finnish (fi).
  
  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: 2022-03-09
  * source language(s): eng
  * target language(s): fin
  * valid target language labels: >>fin<<
  * model: transformer (big)
  * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
  * tokenization: SentencePiece (spm32k,spm32k)
  * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fin/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
  * more information released models: [OPUS-MT eng-fin README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-fin/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. `>>fin<<`
  
  ## Usage
  
  A short example code:
  
  ```python
  from transformers import MarianMTModel, MarianTokenizer
  
  src_text = [
      "Russia is big.",
      "Touch wood!"
  ]
  
  model_name = "pytorch-models/opus-mt-tc-big-en-fi"
  tokenizer = MarianTokenizer.from_pretrained(model_name)
  mode