| | --- |
| | tags: |
| | - flair |
| | language: en |
| | datasets: |
| | - conll2003 |
| | --- |
| | |
| | # Flair NER fine-tuned on Private Dataset |
| |
|
| | This is specifically Designed on locations. the tag is <unk> |
| |
|
| | ```python |
| | from flair.data import Sentence |
| | from flair.models import SequenceTagger |
| | # load tagger |
| | tagger = SequenceTagger.load("Saisam/Inquirer_ner_loc") |
| | # 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) |
| | ``` |
| |
|
| |
|
| | ``` |
| | @inproceedings{akbik2018coling, |
| | title={Contextual String Embeddings for Sequence Labeling}, |
| | author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, |
| | booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, |
| | pages = {1638--1649}, |
| | year = {2018} |
| | } |
| | ``` |
