Edit model card

This is a model for named entity recognition of Japanese medical documents.

How to use

Download the following five files and put into the same folder.

  • id_to_tags.pkl
  • key_attr.pkl
  • NER_medNLP.py
  • predict.py
  • text.txt (This is an input file which should be predicted, which could be changed.)

You can use this model by running predict.py.

python3 predict.py

Input Example


Output Example

 <d certainty="positive">肥大型心筋症、心房細動</d>に対して<m-key state="executed">WF</m-key>投与が開始となった。
<timex3 type="med">治療経過中</timex3>に<d certainty="positive">非持続性心室頻拍</d>が認められたため<m-key state="executed">アミオダロン</m-key>が併用となった。


Tomohiro Nishiyama, Aki Ando, Mihiro Nishidani, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki: NAISTSOC at the NTCIR-16 Real-MedNLP Task, In Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-16), pp. 330-333, 2022

Downloads last month
Hosted inference API
This model can be loaded on the Inference API on-demand.