Biobertpt All

🚀 BioBERTpt - 葡萄牙语临床与生物医学BERT

BioBERTpt是一个用于葡萄牙语临床命名实体识别的神经语言模型。它基于BERT架构，使用多语言BERT模型初始化，并在临床笔记和生物医学文献上进行训练，能够有效处理葡萄牙语的临床和生物医学文本。

🚀 快速开始

模型加载

你可以通过transformers库加载BioBERTpt模型：

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

📚 详细文档

模型介绍

论文 BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition 中包含了基于BERT的葡萄牙语临床和生物医学模型。该模型使用多语言BERT模型（BERT-Multilingual-Cased）初始化，并在临床笔记和生物医学文献上进行训练。

本模型卡片描述的是BioBERTpt(all)模型，它是一个完整版本，包含葡萄牙语的临床叙述和生物医学文献。

更多信息

若需了解更多关于BioBERTpt模型在葡萄牙语命名实体识别（NER）任务中的详细信息和性能，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，CAPES）资助，资助代码为001。

📄 许可证

引用信息

如果你使用了BioBERTpt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.",
}

❓ 常见问题

如果你有任何问题，可以在 BioBERTpt仓库 上提交GitHub问题。