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| # Import generic wrappers | |
| from transformers import AutoModel, AutoTokenizer | |
| if __name__ == "__main__": | |
| from flair.data import Sentence | |
| from flair.models import SequenceTagger | |
| # load tagger | |
| tagger = SequenceTagger.load("flair/ner-english-large") | |
| # make example sentence | |
| sentence = Sentence("George Washington went to Washington") | |
| # 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) | |