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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "aa1a8952",
"metadata": {},
"outputs": [],
"source": [
"#import libraries\n",
"from transformers import pipeline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5493cee5",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b94e18c676540d1ad8312149bdbcc7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/431M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "586b43b1b9e2484c9b8636f9b41ab5e2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/1.19k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6c722044e074fd58b9cf1c67fa48551",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95002c472f794e3b9b96dcfba697395e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0%| | 0.00/669k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b71e5e8241de454c9bd5f7066b3f5413",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/125 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reference appropriate Hugging Face model\n",
"model_name = 'koakande/bert-finetuned-ner'\n",
"\n",
"# Load token classification pipeline modelfrom Hugging Face\n",
"model = pipeline(\"token-classification\", model=model_name, aggregation_strategy=\"simple\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f59488e3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'koakande/bert-finetuned-ner'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_name"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e6b97a0e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'entity_group': 'PER',\n",
" 'score': 0.99741244,\n",
" 'word': 'Kabeer',\n",
" 'start': 12,\n",
" 'end': 18},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9985826,\n",
" 'word': 'OVO',\n",
" 'start': 61,\n",
" 'end': 64},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99884343,\n",
" 'word': 'UK',\n",
" 'start': 72,\n",
" 'end': 74}]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"msg = \"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\"\n",
"model(msg)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c54c0ca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Hello, I am <span style='border: 2px solid green;'>Kabeer</span>. I work as a machine learning engineer at <span style='border: 2px solid green;'>OVO</span> in the <span style='border: 2px solid green;'>UK</span>\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# write a prediction method for the model\n",
"def predict_entities(text):\n",
" # Use the loaded model to identify entities in the text\n",
" entities = model(text)\n",
" # Highlight identified entities in the input text\n",
" highlighted_text = text\n",
" for entity in entities:\n",
" entity_text = text[entity['start']:entity['end']]\n",
" replacement = f\"<span style='border: 2px solid green;'>{entity_text}</span>\"\n",
" highlighted_text = highlighted_text.replace(entity_text, replacement)\n",
" return highlighted_text\n",
"\n",
"predict_entities(msg)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4d784554",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# gradio interface\n",
"import gradio as gr\n",
"\n",
"title = \"Named Entity Recognizer\"\n",
"\n",
"description = \"\"\"\n",
"This model has been trained to identify entities in a given text. It returns the input text with the entities highlighted in green. Give it a try!\n",
"\"\"\"\n",
"\n",
"article = \"The model is trained using bert-finetuned-ner.\"\n",
"\n",
"iface = gr.Interface(\n",
" fn=predict_entities,\n",
" inputs=gr.Textbox(lines=5, placeholder=\"Enter text...\"),\n",
" outputs=gr.HTML(),\n",
" title=title,\n",
" description=description,\n",
" article=article,\n",
" examples=[[\"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\"], [\"This is Maryam who is a Leicester based NHS Doctor\"]],\n",
")\n",
"\n",
"iface.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f930b57",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "named_entity__kernel",
"language": "python",
"name": "named_entity__kernel"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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