readme: ---
  language:
  - es
  - eu
  
  tags:
  - translation
  - opus-mt-tc
  
  license: cc-by-4.0
  model-index:
  - name: opus-mt-tc-big-itc-eu
    results:
    - task:
        name: Translation spa-eus
        type: translation
        args: spa-eus
      dataset:
        name: tatoeba-test-v2021-08-07
        type: tatoeba_mt
        args: spa-eus
      metrics:
         - name: BLEU
           type: bleu
           value: 32.4
         - name: chr-F
           type: chrf
           value: 0.60699
  ---
  # opus-mt-tc-big-itc-eu
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  - [Training](#training)
  - [Evaluation](#evaluation)
  - [Citation Information](#citation-information)
  - [Acknowledgements](#acknowledgements)
  
  ## Model Details
  
  Neural machine translation model for translating from Italic languages (itc) to Basque (eu).
  
  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).
  **Model Description:**
  - **Developed by:** Language Technology Research Group at the University of Helsinki
  - **Model Type:** Translation (transformer-big)
  - **Release**: 2022-07-23
  - **License:** CC-BY-4.0
  - **Language(s):**  
    - Source Language(s): fra ita spa
    - Target Language(s): eus
    - Language Pair(s): spa-eus
    - Valid Target Language Labels: 
  - **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
  - **Resources for more information:**
    - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
    - More information about released models for this language pair: [OPUS-MT itc-eus README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-eus/README.md)
    - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
    - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
  
  ## Uses
  
  This model can be used for translation and text-to-text generation.
  
  ## Risks, Limitations and Biases
  
  **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
  
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
  
  ## How to Get Started With the Model
  
  A short example code:
  
  ```python
  from transformers import MarianMTModel, MarianTokenizer
  
  src_text = [
      "Il est riche.",
      "¿Correcto?"
  ]
  
  model_name = "pytorch-models/opus-mt-tc-big-itc-eu"
  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:
  #     Aberatsa da.
  #     Zuzena?
  ```
  
  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-itc-eu")
  print(pipe("Il est riche."))
  
  # expected output: Aberatsa da.
  ```
  
  ## Training
  
  - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
  - **Pre-processing**: SentencePiece (spm32k,spm32k)
  - **Model Type:**  transformer-big
  - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
  - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
  
  ## Evaluation
  
  * test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.test.txt)
  * test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
  * benchmark results: [benchmark_results.txt](benchmark_results.txt)
  * benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
  
  | lang