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import gradio as gr |
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from tner import TransformersNER |
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from spacy import displacy |
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model = TransformersNER("tner/roberta-large-ontonotes5") |
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examples = [ |
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"Jacob Collier is a Grammy awarded artist from England.", |
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"When Sebastian Thrun started working on self-driving cars at Google in 2007 , few people outside of the company took him seriously.", |
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"But Google is starting from behind. The company made a late push into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer adoption." |
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] |
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def predict(text): |
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output = model.predict([text]) |
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tokens = output['input'][0] |
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def retain_char_position(p): |
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if p == 0: |
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return 0 |
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return len(' '.join(tokens[:p])) + 1 |
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doc = { |
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"text": text, |
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"ents": [{ |
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"start": retain_char_position(entity['position'][0]), |
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"end": retain_char_position(entity['position'][-1]) + len(entity['entity'][-1]), |
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"label": entity['type'] |
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} for entity in output['entity_prediction'][0]], |
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"title": None |
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} |
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html = displacy.render(doc, style="ent", page=True, manual=True, minify=True) |
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html = ( |
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"<div style='max-width:100%; max-height:360px; overflow:auto'>" |
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+ html |
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+ "</div>" |
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) |
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return html |
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demo = gr.Interface( |
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fn=predict, |
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inputs=gr.inputs.Textbox( |
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lines=5, |
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placeholder="Input sentence...", |
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), |
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outputs="html", |
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examples=examples |
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) |
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demo.launch() |
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