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unicamp-dl/ptt5-base-portuguese-vocab unicamp-dl/ptt5-base-portuguese-vocab
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pytorch

tf

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unicamp-dl university
3 team members · 6 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/ptt5-base-portuguese-vocab") model = AutoModelWithLMHead.from_pretrained("unicamp-dl/ptt5-base-portuguese-vocab")
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Portuguese T5 (aka "PTT5")

Introduction

PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving 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 ours, trained on Portuguese Wikipedia).

For further information or requests, please go to PTT5 repository.

Available models

Model Size #Params Vocabulary
unicamp-dl/ptt5-small-t5-vocab small 60M Google's T5
unicamp-dl/ptt5-base-t5-vocab base 220M Google's T5
unicamp-dl/ptt5-large-t5-vocab large 740M Google's T5
unicamp-dl/ptt5-small-portuguese-vocab small 60M Portuguese
unicamp-dl/ptt5-base-portuguese-vocab (Recommended) base 220M Portuguese
unicamp-dl/ptt5-large-portuguese-vocab large 740M Portuguese

Usage

# Tokenizer 
from transformers import 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

If you use PTT5, please cite:

@article{ptt5_2020,
  title={PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data},
  author={Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
  journal={arXiv preprint arXiv:2008.09144},
  year={2020}
}