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README.md

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+# 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](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 "I like to eat rice" is:
+
+```python
+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
+
+```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",
+}
+```