| tags: | |
| - flair | |
| - token-classification | |
| - sequence-tagger-model | |
| datasets: GuiGel/meddocan | |
| ### Demo: How to use in Flair | |
| Requires: | |
| - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) | |
| ```python | |
| from flair.data import Sentence | |
| from flair.models import SequenceTagger | |
| # load tagger | |
| tagger = SequenceTagger.load("GuiGel/meddocan") | |
| # make example sentence | |
| sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.") | |
| # predict NER tags | |
| tagger.predict(sentence) | |
| # print sentence | |
| print(sentence) | |
| # print predicted NER spans | |
| print('The following NER tags are found:') | |
| # iterate over entities and print | |
| for entity in sentence.get_spans('ner'): | |
| print(entity) | |
| ``` |