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{
"cells": [
{
"cell_type": "markdown",
"id": "b4614723-e45f-426e-a545-d0ed9685557d",
"metadata": {},
"source": [
"# Text Generation\n",
"\n",
"Experimenting with a Text Generation Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b249c274-f0ab-43cb-9c44-b4de64045cac",
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "09955617-16bb-4d6e-8546-91549c3fe296",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2bbb30f80b274ff0b8c50a075f0cdd73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some parameters are on the meta device because they were offloaded to the disk.\n",
"Device set to use mps\n"
]
}
],
"source": [
"# Use a pre-quantized model from the Hub\n",
"pipe = pipeline(\n",
" \"text-generation\",\n",
" model=\"meta-llama/Llama-3.2-3B\",\n",
" device_map=\"auto\",\n",
" do_sample=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e7f1608-b823-4cee-ad31-c9a9c5feae4d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"first_name\n",
"email\n",
"phone\n"
]
}
],
"source": [
"def get_faker_mapping(properties):\n",
" # Create a structured prompt\n",
" prompt = \"\"\"\n",
"You are a data helper.\n",
"\n",
"Function Names:\n",
"name\n",
"first_name\n",
"last_name\n",
"email\n",
"phone\n",
"address\n",
"city\n",
"state\n",
"zip_code\n",
"country\n",
"company\n",
"job_title\n",
"credit_card_number\n",
"date_of_birth\n",
"text\n",
"random_number\n",
"\n",
"EXAMPLE 1:\n",
"Properties:\n",
"fullName: Full name of person\n",
"businessEmail: Business email address\n",
"phoneNum: US phone number\n",
"\n",
"Response:\n",
"fullName: name\n",
"businessEmail: email\n",
"phoneNum: phone\n",
"\n",
"EXAMPLE 2:\n",
"Properties:\n",
"name: name of person\n",
"ccn: Credit card number\n",
"personalPhone: US phone number\n",
"\n",
"Response:\n",
"name: name\n",
"ccn: credit_card_number\n",
"personalPhone: phone\n",
"\n",
"EXAMPLE 3:\n",
"Properties:\n",
"firstName: first name of person\n",
"city: City of residence\n",
"personalPhone: US phone number\n",
"\n",
"Response:\n",
"firstName: first_name\n",
"city: city\n",
"personalPhone: phone\n",
"\n",
"Complete EXAMPLE 4:\n",
"Properties:\n",
"\"\"\"\n",
" # Add properties to prompt\n",
" for prop, desc in properties:\n",
" prompt += f\"{prop}: {desc}\\n\"\n",
" \n",
" prompt += \"\\nResponse:\"\n",
" \n",
" # Generate response\n",
" response = pipe(\n",
" prompt,\n",
" max_new_tokens=50\n",
" )\n",
" \n",
" result = response[0]['generated_text']\n",
" \n",
" # Logic to convert to a dict\n",
" response_text = result.split(\"Complete EXAMPLE 4:\")[1].split(\"Complete EXAMPLE 5:\")[0].split(\"Response:\")[1].strip()\n",
" \n",
" return {line.split(\":\", 1)[0].strip(): line.split(\":\", 1)[1].strip() for line in response_text.splitlines() if \":\" in line}\n",
" \n",
"# Example use\n",
"properties = [\n",
" (\"name\", \"users first name\"),\n",
" (\"email\", \"Business email address\"),\n",
" (\"phone\", \"US phone number\")\n",
"]\n",
"\n",
"result = get_faker_mapping(properties)\n",
"\n",
"for key, desc in properties:\n",
" print(result.get(key))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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