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run.ipynb
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: ner_pipeline"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "ner_pipeline = pipeline(\"ner\")\n", "\n", "examples = [\n", " \"Does Chicago have any stores and does Joe live here?\",\n", "]\n", "\n", "def ner(text):\n", " output = ner_pipeline(text)\n", " return {\"text\": text, \"entities\": output}
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: ner_pipeline"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "ner_pipeline = pipeline(\"ner\")\n", "\n", "examples = [\n", " \"Does Chicago have any stores and does Joe live here?\",\n", "]\n", "\n", "def ner(text):\n", " output = ner_pipeline(text)\n", " return {\"text\": text, \"entities\": output}\n", "\n", "demo = gr.Interface(ner,\n", " gr.Textbox(placeholder=\"Enter sentence here...\"),\n", " gr.HighlightedText(),\n", " examples=examples)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
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run.py
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def ner(text):
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output = ner_pipeline(text)
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return {"text": text, "entities": output}
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demo = gr.Interface(ner,
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gr.Textbox(placeholder="Enter sentence here..."),
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gr.HighlightedText(),
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examples=examples)
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def ner(text):
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output = ner_pipeline(text)
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return {"text": text, "entities": output}
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demo = gr.Interface(ner,
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gr.Textbox(placeholder="Enter sentence here..."),
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gr.HighlightedText(),
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examples=examples)
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