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--- |
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tags: |
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- flair |
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- token-classification |
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language: en |
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datasets: |
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- conll2003 |
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widget: |
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- text: "George Washington went to Washington" |
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--- |
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|
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# Flair NER fine-tuned on Private Dataset |
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|
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("Saisam/Inquirer_ner_loc") |
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# make example sentence |
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sentence = Sentence("George Washington went to Washington") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
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|
|
|
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``` |
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@inproceedings{akbik2018coling, |
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title={Contextual String Embeddings for Sequence Labeling}, |
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author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, |
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booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, |
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pages = {1638--1649}, |
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year = {2018} |
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} |
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``` |