Biobertpt Bio

🚀 BioBERTpt - ポルトガル語の臨床および生物医学用BERT

論文「BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition」には、ポルトガル語用の臨床および生物医学に基づくBERTモデルが含まれています。これらのモデルは、BERT-Multilingual-Casedで初期化され、臨床ノートと生物医学文献を用いて学習されています。

このモデルカードでは、BioBERTpt(bio)モデル、つまりBioBERTptの生物医学版について説明します。このモデルは、PubmedとScieloの科学論文から得られたポルトガル語の生物医学文献を用いて学習されています。

🚀 クイックスタート

モデルの使い方

transformersライブラリを介してモデルを読み込みます。

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

📚 ドキュメント

詳細情報

追加の詳細やポルトガル語の固有表現抽出（NER）タスクにおけるパフォーマンスについては、元の論文「BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition」を参照してください。

📄 謝辞

この研究は、Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)によって一部資金提供されました（財政コード001）。

📖 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.",
}

❓ 質問はこちら

BioBERTptリポジトリにGitHubのissueを投稿してください。