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unicamp-dl/ptt5-base-t5-vocab
ptt5-base-t5-vocab is a text generation model from unicamp-dl. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
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…
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From the Hugging Face model README
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.
| 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 |
# 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)
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}
}