Ptt5 V2 Base

🚀 ptt5-v2-base

ptt5-v2模型是专门为葡萄牙语定制的预训练T5模型，它基于谷歌原始的检查点继续训练，模型大小从t5-small到t5-3B不等。这些检查点被用于训练葡萄牙语的MonoT5重排器，你可以在它们的HuggingFace集合中找到。如需了解更多关于预训练过程的信息，请参考我们的论文ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language。

🚀 快速开始

模型信息

模型介绍

ptt5-v2模型是专门为葡萄牙语定制的预训练T5模型，它在谷歌原始检查点的基础上继续训练，模型大小涵盖从t5-small到t5-3B。这些检查点用于训练葡萄牙语的MonoT5重排器，可在其HuggingFace集合中找到。关于预训练过程的更多信息，请参考我们的论文ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language。

💻 使用示例

基础用法

from transformers import T5Tokenizer, T5ForConditionalGeneration

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

📄 许可证

本项目采用apache-2.0许可证。

📚 详细文档

引用信息

如果你使用了我们的模型，请按照以下格式进行引用：

@article{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.'}
}