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from transformers import pipeline |
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import gradio as gr |
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get_completion = pipeline("ner", model="dslim/bert-base-NER") |
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def merge_tokens(tokens): |
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merged_tokens = [] |
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for token in tokens: |
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if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]): |
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last_token = merged_tokens[-1] |
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last_token['word'] += token['word'].replace('##', '') |
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last_token['end'] = token['end'] |
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last_token['score'] = (last_token['score'] + token['score']) / 2 |
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else: |
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merged_tokens.append(token) |
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return merged_tokens |
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def ner(input): |
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output = get_completion(input) |
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merged_tokens = merge_tokens(output) |
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return {"text": input, "entities": merged_tokens} |
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gr.close_all() |
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demo = gr.Interface(fn=ner, |
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inputs=[gr.Textbox(label="Text to find entities", lines=2)], |
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outputs=[gr.HighlightedText(label="Text with entities")], |
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title="NER with dslim/bert-base-NER", |
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description="Find entities using the `dslim/bert-base-NER` model under the hood!", |
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allow_flagging="never", |
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examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"]) |
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demo.launch(inline=False) |
