|
--- |
|
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} |
|
} |
|
``` |
