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language: |
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- en |
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- pt |
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datasets: |
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- EMEA |
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- ParaCrawl 99k |
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- CAPES |
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- Scielo |
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- JRC-Acquis |
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- Biomedical Domain Corpora |
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tags: |
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- translation |
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metrics: |
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- bleu |
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--- |
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# Introduction |
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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](https://github.com/unicamp-dl/Lite-T5-Translation). Also, check [our paper](https://aclanthology.org/2020.wmt-1.90.pdf)! |
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# Usage |
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Just follow "Use in Transformers" instructions. It is necessary to add a few words before to define the task to T5. |
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You can also create a pipeline for it. An example with the phrase "I like to eat rice" is: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/translation-en-pt-t5") |
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model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/translation-en-pt-t5") |
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enpt_pipeline = pipeline('text2text-generation', model=model, tokenizer=tokenizer) |
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enpt_pipeline("translate English to Portuguese: I like to eat rice.") |
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``` |
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# Citation |
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```bibtex |
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@inproceedings{lopes-etal-2020-lite, |
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title = "Lite Training Strategies for {P}ortuguese-{E}nglish and {E}nglish-{P}ortuguese Translation", |
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author = "Lopes, Alexandre and |
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Nogueira, Rodrigo and |
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Lotufo, Roberto and |
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Pedrini, Helio", |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.wmt-1.90", |
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pages = "833--840", |
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} |
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``` |
