metadata

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 EN-PT 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. Also, check our paper!

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 "I like to eat rice" is:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
  
tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/translation-en-pt-t5")

model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/translation-en-pt-t5")

enpt_pipeline = pipeline('text2text-generation', model=model, tokenizer=tokenizer)

enpt_pipeline("translate English to Portuguese: I like to eat rice.")

Citation

@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",
}