You can deploy this model and start earning money today!

ragavabe/
ptt5-base-portuguese-vocab

Name: ptt5-base-portuguese-vocab
Availability: InStock

Views

No views yet

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

python

# 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}
}

ragavabe/ptt5-base-portuguese-vocab