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import gradio as gr |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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tagger = SequenceTagger.load('best-model.pt') |
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def run_ner(input_text): |
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sentence = Sentence(input_text) |
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tagger.predict(sentence) |
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entities = [] |
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for entity in sentence.get_spans('ner'): |
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entities.append((entity.text, entity.get_label('ner').value, entity.get_label('ner').score)) |
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return entities |
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demo = gr.Interface(fn=run_ner, |
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title='Named Entity Recognition Demo', |
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description='This demo performs **Named Entity Recognition** by tagging user-inputted sentence(s). Give it a try by entering a sentence or using one of the provided examples. Common tags include **geo** (geographical entity), **org** (organization), **per** (person), and **tim** (time). In the box on the right, the results will show the tagged words and their corresponding confidence scores.', |
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article='*This demo is based on a Named Entity Recognition model trained by Curtis Pond and Julia Nickerson as part of their FourthBrain capstone project. For more information, check out their [GitHub repo](https://github.com/nickersonj/glg-capstone).*', |
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inputs=gr.Textbox(label='Input Text', lines=2, placeholder='Type some text here...'), |
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outputs=gr.Textbox(label='Named Entity Recognition Results', lines=2, placeholder=''), |
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examples=['The indictments were announced Tuesday by the Justice Department in Cairo.', "In 2019, the men's singles winner was Novak Djokovic who defeated Roger Federer in a tournament taking place in the United Kingdom.", 'In a study published by the American Heart Association on January 18, researchers at the Johns Hopkins School of Medicine found that meal timing did not impact weight.'], |
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allow_flagging='never' |
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) |
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demo.launch() |
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