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README.md

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+---
+language: pt
+license: mit
+tags:
+  - t5
+  - pytorch
+  - tensorflow
+datasets:
+  - brWaC
+---
+
+# Portuguese T5 (aka "PTT5")
+
+## Introduction
+PTT5 is a T5 model pretrained on Portuguese data which improves T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 original and the Portuguese ours, trained on Portuguese Wikipedia data).
+
+For further information or requests, please go to [PTT5 repository](https://github.com/unicamp-dl/PTT5).
+
+## Available models
+| Model                                    | Architecture | #Params | Vocabulary  |
+| :-:                                      | :-:          | :-:     | :-:         |
+| `unicamp-dl/ptt5-small-t5-vocab`         | t5-small     | 60M     | Google's T5 |
+| `unicamp-dl/ptt5-base-t5-vocab`          | t5-base      | 220M    | Google's T5 |
+| `unicamp-dl/ptt5-large-t5-vocab`         | t5-large     | 740M    | Google's T5 |
+| `unicamp-dl/ptt5-small-portuguese-vocab` | t5-small     | 60M     | Portuguese  |
+| `unicamp-dl/ptt5-base-portuguese-vocab`  | t5-base      | 220M    | Portuguese  |
+| `unicamp-dl/ptt5-large-portuguese-vocab` | t5-large     | 740M    | Portuguese  |
+
+
+## Usage
+```python
+# Tokenizer 
+from transformers import AutoTokenizer # or T5Tokenizer
+
+# PyTorch (bare model, baremodel + language modeling head)
+from transformers import T5Model, T5ForConditionalGeneration
+
+# Tensorflow (bare model, baremodel + language modeling head)
+from transformers import TFT5Model, TFT5ForConditionalGeneration
+
+model_name = 'unicamp-dl/ptt5-base-portuguese-vocab'
+
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+# PyTorch
+model_pt = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# TensorFlow
+model_tf = TFT5ForConditionalGeneration.from_pretrained(model_name)
+```
+
+
+## Citation
+We are preparing an arXiv submission and soon will provide a citation. For now, if you need to cite use:
+```bibtex
+@misc{ptt5_2020,
+  Author = {Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
+  Title = {PTT5: Pre-training and validating the T5 transformer in Brazilian Portuguese data},
+  Year = {2020},
+  Publisher = {GitHub},
+  Journal = {GitHub repository},
+  Howpublished = {\url{https://github.com/unicamp-dl/PTT5}}
+}
+```