T5 Portuguese Legal Summarization

🚀 stjiris/t5-portuguese-legal-summarization

这是一个在“unicamp - dl/ptt5 - base - portuguese - vocab” T5模型基础上进行微调的模型，利用各种判例及其摘要进行训练，可用于法律领域的文本摘要任务。

🚀 快速开始

本项目是作为 [Project IRIS](https://www.inesc - id.pt/projects/PR07005/) 的一部分而开展的工作。相关论文为 A Semantic Search System for Supremo Tribunal de Justiça。

项目相关链接

• [INESC - ID](https://www.inesc - id.pt/projects/PR07005/) [](https://www.inesc - id.pt/projects/PR07005/)
• A Semantic Search System for Supremo Tribunal de Justiça

✨ 主要特性

该模型是在“unicamp - dl/ptt5 - base - portuguese - vocab” T5模型基础上进行微调得到的，使用了各种判例及其摘要进行训练。

💻 使用示例

基础用法

# name of folder principal
from transformers import T5Tokenizer, T5ForConditionalGeneration

model_checkpoint = "stjiris/t5-portuguese-legal-summarization"
t5_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
t5_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)

preprocess_text = "These are some big words and text and words and text, again, that we want to summarize"
t5_prepared_Text = "summarize: "+preprocess_text
#print ("original text preprocessed: \n", preprocess_text)

tokenized_text = t5_tokenizer.encode(t5_prepared_Text, return_tensors="pt").to(device)


# summmarize 
summary_ids = t5_model.generate(tokenized_text,
                                    num_beams=4,
                                    no_repeat_ngram_size=2,
                                    min_length=512,
                                    max_length=1024,
                                    early_stopping=True)

output = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print ("\n\nSummarized text: \n",output)

📄 许可证

本项目采用 MIT 许可证。

📚 引用与作者

贡献者

@rufimelo99

引用信息

如果您使用了本项目的成果，请引用以下文献：

@InProceedings{MeloSemantic,
  author="Melo, Rui
  and Santos, Pedro A.
  and Dias, Jo{\~a}o",
  editor="Moniz, Nuno
  and Vale, Zita
  and Cascalho, Jos{\'e}
  and Silva, Catarina
  and Sebasti{\~a}o, Raquel",
  title="A Semantic Search System for the Supremo Tribunal de Justi{\c{c}}a",
  booktitle="Progress in Artificial Intelligence",
  year="2023",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="142--154",
  abstract="Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justi{\c{c}}a (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a {\$}{\$}335{\backslash}{\%}{\$}{\$}335{\%}increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.",
  isbn="978-3-031-49011-8"
}


@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}
}