readme: ---
  
  language:
  
  - en
  
  - pt
  
  datasets:
  
  - EMEA
  
  - ParaCrawl 99k
  
  - CAPES
  
  - Scielo
  
  - JRC-Acquis
  
  - Biomedical Domain Corpora
  
  tags:
  
  - translation
  
  metrics:
  
  - bleu
  
  ---
  
  # Introduction
  
  This repository brings an implementation of T5 for translation in PT-EN tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our repository](https://github.com/unicamp-dl/Lite-T5-Translation). Also, check [our paper](https://aclanthology.org/2020.wmt-1.90.pdf)!
  
  # Usage
  
  Just follow "Use in Transformers" instructions. It is necessary to add a few words before to define the task to T5. 
  
  You can also create a pipeline for it. An example with the phrase " Eu gosto de comer arroz" is:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
    
  tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/translation-pt-en-t5")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/translation-pt-en-t5")
  
  pten_pipeline = pipeline('text2text-generation', model=model, tokenizer=tokenizer)
  
  pten_pipeline("translate Portuguese to English: Eu gosto de comer arroz.")
  
  ```
  
  # Citation
  
  ```bibtex
  @inproceedings{lopes-etal-2020-lite,
      title = "Lite Training Strategies for {P}ortuguese-{E}nglish and {E}nglish-{P}ortuguese Translation",
      author = "Lopes, Alexandre  and
        Nogueira, Rodrigo  and
        Lotufo, Roberto  and
        Pedrini, Helio",
      booktitle = "Proceedings of the Fifth Conference on Machine Translation",
      month = nov,
      year = "2020",
      address = "Online",
      publisher = "Association for Computational Linguistics",
      url = "https://www.aclweb.org/anthology/2020.wmt-1.90",
      pages = "833--840",
  }
  ```