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"Collecting datasets\n",
" Downloading datasets-2.16.0-py3-none-any.whl (507 kB)\n",
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"\u001b[?25hCollecting evaluate\n",
" Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
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"Collecting pyarrow-hotfix (from datasets)\n",
" Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
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" Found existing installation: typing_extensions 4.5.0\n",
" Uninstalling typing_extensions-4.5.0:\n",
" Successfully uninstalled typing_extensions-4.5.0\n",
" Attempting uninstall: pydantic\n",
" Found existing installation: pydantic 1.10.13\n",
" Uninstalling pydantic-1.10.13:\n",
" Successfully uninstalled pydantic-1.10.13\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"lida 0.0.10 requires kaleido, which is not installed.\n",
"llmx 0.0.15a0 requires cohere, which is not installed.\n",
"llmx 0.0.15a0 requires openai, which is not installed.\n",
"llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
"tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.108.0 ffmpy-0.3.1 gradio-4.12.0 gradio-client-0.8.0 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 orjson-3.9.10 pydantic-2.5.3 pydantic-core-2.14.6 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.32.0.post1 tomlkit-0.12.0 typing-extensions-4.9.0 uvicorn-0.25.0 websockets-11.0.3\n"
]
}
],
"source": [
"!pip install datasets evaluate transformers[sentencepiece]\n",
"!pip install gradio"
]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline\n",
"\n",
"#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\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209,
"referenced_widgets": [
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},
"id": "wToN1liKoA71",
"outputId": "8cfe1f61-5e7a-4998-8157-86241e13b07a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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},
{
"output_type": "display_data",
"data": {
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],
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"version_major": 2,
"version_minor": 0,
"model_id": "4a775028df08459e94593e0dc31d11c8"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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"version_minor": 0,
"model_id": "431eac7103494de78ece147ed1e049d6"
}
},
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}
]
},
{
"cell_type": "code",
"source": [
"msg = \"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\"\n",
"model(msg)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ns0Kz-vKrqlC",
"outputId": "fd7bfc7d-76b8-4afb-e7d8-4e52b486ce26"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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}]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"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\"{entity_text}\"\n",
" highlighted_text = highlighted_text.replace(entity_text, replacement)\n",
" return highlighted_text\n",
"\n",
"predict_entities(msg)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"id": "uPLzs_Tzryh6",
"outputId": "bdad59ff-7749-4d23-9bc1-f1ae233e112f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\""
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK"
],
"metadata": {
"id": "PtmvwIFktEQn"
}
},
{
"cell_type": "code",
"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",
"# iface = gr.Interface(\n",
"# fn=predict_entities,\n",
"# inputs=gr.Textbox(lines=5, placeholder=\"Enter text...\"),\n",
"# outputs=gr.HTML(),\n",
"# title=\"Named Entity Identification\",\n",
"# description=\"Enter text to identify entities using the model.\",\n",
"# )\n",
"\n",
"iface.launch()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 648
},
"id": "R613Bgf1s8nd",
"outputId": "afd76cf7-19da-49f3-d614-18d0bfdf42fc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
"\n",
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Running on public URL: https://9f2912e80f49313cf1.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
""
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "JQhxCqjK2vh-"
},
"execution_count": null,
"outputs": []
}
]
}