README.md

---
datasets:
- allenai/c4
- legacy-datasets/mc4
language:
- pt
pipeline_tag: text2text-generation
base_model: google-t5/t5-small
license: apache-2.0
---

# ptt5-v2-small

## Introduction
[ptt5-v2 models](https://huggingface.co/collections/unicamp-dl/ptt5-v2-666538a650188ba00aa8d2d0) are pretrained T5 models tailored for the Portuguese language, continuing from Google's original checkpoints with sizes from t5-small to t5-3B.
These checkpoints were used to train MonoT5 rerankers for the Portuguese language, which can be found in their  [HuggingFace collection](https://huggingface.co/collections/unicamp-dl/monoptt5-66653981877df3ea727f720d).
For further information about the pretraining process, please refer to our paper, [ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language](https://arxiv.org/abs/2008.09144).

## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("unicamp-dl/ptt5-v2-small")
model = T5ForConditionalGeneration.from_pretrained("unicamp-dl/ptt5-v2-small")
```

## Citation
If you use our models, please cite:
```
@misc{piau2024ptt5v2,
      title={ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language}, 
      author={Marcos Piau and Roberto Lotufo and Rodrigo Nogueira},
      year={2024},
      eprint={2406.10806},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```