Biobertpt Clin

🚀 BioBERTpt - 葡萄牙语临床与生物医学BERT

BioBERTpt是基于BERT的葡萄牙语临床与生物医学模型。它以BERT多语言大小写敏感模型（BERT - Multilingual - Cased）为基础进行初始化，并在临床笔记和生物医学文献上进行训练。本项目聚焦于BioBERTpt(clin)模型，这是BioBERTpt的临床版本，在巴西医院电子健康记录中的临床叙述数据上进行了训练。

🚀 快速开始

如何使用该模型

可以通过transformers库来加载模型：

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-clin")
model = AutoModel.from_pretrained("pucpr/biobertpt-clin")

📚 详细文档

更多详细信息和该模型在葡萄牙语命名实体识别（NER）任务中的性能表现，请参考原始论文 BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition。

👏 致谢

本研究部分由巴西高等教育人员素质提升协调局（Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil，简称CAPES）资助，资助代码为001。

📄 许可证

引用

如果您使用了该模型，请按照以下格式进行引用：

@inproceedings{schneider-etal-2020-biobertpt,
    title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition",
    author = "Schneider, Elisa Terumi Rubel  and
      de Souza, Jo{\~a}o Vitor Andrioli  and
      Knafou, Julien  and
      Oliveira, Lucas Emanuel Silva e  and
      Copara, Jenny  and
      Gumiel, Yohan Bonescki  and
      Oliveira, Lucas Ferro Antunes de  and
      Paraiso, Emerson Cabrera  and
      Teodoro, Douglas  and
      Barra, Cl{\'a}udia Maria Cabral Moro",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7",
    pages = "65--72",
    abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.",
}

❓ 有疑问？

如果您有任何问题，请在 BioBERTpt仓库 上创建一个GitHub issue。