Transformers-sklearn: a toolkit for medical language understanding with transformer-based models.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical language understanding and expand the capability of the scikit-learn toolkit in deep learning, we proposed an easy to learn Python toolkit named transformers-sklearn. By wrapping the interfaces of transformers in only three functions (i.e., fit, score, and predict), transformers-sklearn combines the advantages of the transformers and scikit-learn toolkits.

Authors

  • Feihong Yang
    Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, 3rd Yabao Road, Beijing, 100020, China.
  • Xuwen Wang
    Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, 3rd Yabao Road, Beijing, 100020, China.
  • Hetong Ma
    Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, 3rd Yabao Road, Beijing, 100020, China.
  • Jiao Li
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301 China yinhao@scsio.ac.cn.