{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# đ¨ Data Designer 101: The Basics\n",
"\n",
"[Click here](https://raw.githubusercontent.com/NVIDIA-NeMo/DataDesigner/refs/heads/main/docs/notebooks/1-the-basics.ipynb) to download this notebook to your computer.",
"\n",
"#### đ What you'll learn\n",
"\n",
"This notebook demonstrates the basics of Data Designer by generating a simple product review dataset.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### đĻ Import the essentials\n",
"\n",
"- The `essentials` module provides quick access to the most commonly used objects.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from data_designer.essentials import (\n",
" CategorySamplerParams,\n",
" DataDesigner,\n",
" DataDesignerConfigBuilder,\n",
" InferenceParameters,\n",
" LLMTextColumnConfig,\n",
" ModelConfig,\n",
" PersonSamplerParams,\n",
" SamplerColumnConfig,\n",
" SamplerType,\n",
" SubcategorySamplerParams,\n",
" UniformSamplerParams,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### âī¸ Initialize the Data Designer interface\n",
"\n",
"- `DataDesigner` is the main object that is used to interface with the library.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_designer = DataDesigner()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### đī¸ Define model configurations\n",
"\n",
"- Each `ModelConfig` defines a model that can be used during the generation process.\n",
"\n",
"- The \"model alias\" is used to reference the model in the Data Designer config (as we will see below).\n",
"\n",
"- The \"model provider\" is the external service that hosts the model (see the [model config](/models/default-model-settings.md) docs for more details).\n",
"\n",
"- By default, we use [build.nvidia.com](https://build.nvidia.com/models) as the model provider.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# This name is set in the model provider configuration.\n",
"MODEL_PROVIDER = \"nvidia\"\n",
"\n",
"# The model ID is from build.nvidia.com.\n",
"MODEL_ID = \"nvidia/nvidia-nemotron-nano-9b-v2\"\n",
"\n",
"# We choose this alias to be descriptive for our use case.\n",
"MODEL_ALIAS = \"nemotron-nano-v2\"\n",
"\n",
"# This sets reasoning to False for the nemotron-nano-v2 model.\n",
"SYSTEM_PROMPT = \"/no_think\"\n",
"\n",
"model_configs = [\n",
" ModelConfig(\n",
" alias=MODEL_ALIAS,\n",
" model=MODEL_ID,\n",
" provider=MODEL_PROVIDER,\n",
" inference_parameters=InferenceParameters(\n",
" temperature=0.5,\n",
" top_p=1.0,\n",
" max_tokens=1024,\n",
" ),\n",
" )\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### đī¸ Initialize the Data Designer Config Builder\n",
"\n",
"- The Data Designer config defines the dataset schema and generation process.\n",
"\n",
"- The config builder provides an intuitive interface for building this configuration.\n",
"\n",
"- The list of model configs is provided to the builder at initialization.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"config_builder = DataDesignerConfigBuilder(model_configs=model_configs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## đ˛ Getting started with sampler columns\n",
"\n",
"- Sampler columns offer non-LLM based generation of synthetic data.\n",
"\n",
"- They are particularly useful for **steering the diversity** of the generated data, as we demonstrate below.\n",
"\n",
"
\n",
"\n",
"You can view available samplers using the config builder's `info` property:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"âââââââââââââââââââââââââââââââââââââââââââ NeMo Data Designer Samplers âââââââââââââââââââââââââââââââââââââââââââ\n",
"\n",
"âââââââââââââââââââââŗââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââŗâââââââââââŗâââââââââââââââââââ\n",
"â Type â Parameter â Data Type â Required â Constraints â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â bernoulli â p â number â â â >= 0.0, <= 1.0 â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â bernoulli_mixture â p â number â â â >= 0.0, <= 1.0 â\n",
"â â dist_name â string â â â â\n",
"â â dist_params â dict â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â binomial â n â integer â â â â\n",
"â â p â number â â â >= 0.0, <= 1.0 â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â category â values â string[] | integer[] | â â â len > 1 â\n",
"â â â number[] â â â\n",
"â â weights â number[] | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â datetime â start â string â â â â\n",
"â â end â string â â â â\n",
"â â unit â string â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â gaussian â mean â number â â â â\n",
"â â stddev â number â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â person â locale â string â â â\n",
"â â sex â string | null â â â\n",
"â â city â string | string[] | null â â â\n",
"â â age_range â integer[] â â len > 2, len < 2 â\n",
"â â state â string | string[] | null â â â\n",
"â â with_synthetic_personas â boolean â â â\n",
"â â sample_dataset_when_available â boolean â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â poisson â mean â number â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â scipy â dist_name â string â â â â\n",
"â â dist_params â dict â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â subcategory â category â string â â â â\n",
"â â values â dict â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â timedelta â dt_min â integer â â â >= 0 â\n",
"â â dt_max â integer â â â > 0 â\n",
"â â reference_column_name â string â â â â\n",
"â â unit â string â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â uniform â low â number â â â â\n",
"â â high â number â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â uuid â prefix â string | null â â â\n",
"â â short_form â boolean â â â\n",
"â â uppercase â boolean â â â\n",
"âââââââââââââââââââââ´ââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââ´âââââââââââ´âââââââââââââââââââ\n",
"\n"
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"\n",
"âââââââââââââââââââââŗââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââŗâââââââââââŗâââââââââââââââââââ\n",
"â\u001b[1m \u001b[0m\u001b[1mType \u001b[0m\u001b[1m \u001b[0mâ\u001b[1m \u001b[0m\u001b[1mParameter \u001b[0m\u001b[1m \u001b[0mâ\u001b[1m \u001b[0m\u001b[1mData Type \u001b[0m\u001b[1m \u001b[0mâ\u001b[1m \u001b[0m\u001b[1mRequired\u001b[0m\u001b[1m \u001b[0mâ\u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â bernoulli â p â number â â â >= 0.0, <= 1.0 â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â bernoulli_mixture â p â number â â â >= 0.0, <= 1.0 â\n",
"â â dist_name â string â â â â\n",
"â â dist_params â dict â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â binomial â n â integer â â â â\n",
"â â p â number â â â >= 0.0, <= 1.0 â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â category â values â string[] | integer[] | â â â len > 1 â\n",
"â â â number[] â â â\n",
"â â weights â number[] | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â datetime â start â string â â â â\n",
"â â end â string â â â â\n",
"â â unit â string â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â gaussian â mean â number â â â â\n",
"â â stddev â number â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â person â locale â string â â â\n",
"â â sex â string | null â â â\n",
"â â city â string | string[] | null â â â\n",
"â â age_range â integer[] â â len > 2, len < 2 â\n",
"â â state â string | string[] | null â â â\n",
"â â with_synthetic_personas â boolean â â â\n",
"â â sample_dataset_when_available â boolean â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â poisson â mean â number â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â scipy â dist_name â string â â â â\n",
"â â dist_params â dict â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â subcategory â category â string â â â â\n",
"â â values â dict â â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â timedelta â dt_min â integer â â â >= 0 â\n",
"â â dt_max â integer â â â > 0 â\n",
"â â reference_column_name â string â â â â\n",
"â â unit â string â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â uniform â low â number â â â â\n",
"â â high â number â â â â\n",
"â â decimal_places â integer | null â â â\n",
"âââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧââââââââââââââââââââââââââââââââŧâââââââââââŧâââââââââââââââââââ¤\n",
"â uuid â prefix â string | null â â â\n",
"â â short_form â boolean â â â\n",
"â â uppercase â boolean â â â\n",
"âââââââââââââââââââââ´ââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââ´âââââââââââ´âââââââââââââââââââ\n"
]
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"output_type": "display_data"
}
],
"source": [
"config_builder.info.display(\"samplers\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start designing our product review dataset by adding product category and subcategory columns.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[19:00:10] [INFO] â
Validation passed\n"
]
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"\n",
"\n",
"\n",
"DataDesignerConfigBuilder(\n",
" sampler_columns: ['product_category', 'product_subcategory', 'target_age_range']\n",
")\n",
"DataDesignerConfigBuilder(\n",
" sampler_columns: [\n",
" "product_category",\n",
" "product_subcategory",\n",
" "target_age_range",\n",
" "customer",\n",
" "number_of_stars",\n",
" "review_style"\n",
" ]\n",
")\n",
"DataDesignerConfigBuilder(\n",
" sampler_columns: [\n",
" "product_category",\n",
" "product_subcategory",\n",
" "target_age_range",\n",
" "customer",\n",
" "number_of_stars",\n",
" "review_style"\n",
" ]\n",
" llm_text_columns: ['product_name', 'customer_review']\n",
")\n",
" \n",
" Generated Columns \n",
"âââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\n",
"â Name â Value â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_category â Home & Kitchen â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â product_subcategory â Organization â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â target_age_range â 50-65 â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â customer â { â\n",
"â â 'uuid': '67c0a53a-ddfb-49c4-adea-69a220eef85f', â\n",
"â â 'locale': 'en_GB', â\n",
"â â 'first_name': 'Angela', â\n",
"â â 'last_name': 'Atkins', â\n",
"â â 'middle_name': None, â\n",
"â â 'sex': 'Female', â\n",
"â â 'street_number': '3', â\n",
"â â 'street_name': 'Ashley turnpike', â\n",
"â â 'city': 'Mohammedview', â\n",
"â â 'state': None, â\n",
"â â 'postcode': 'WV02 7UH', â\n",
"â â 'age': 31, â\n",
"â â 'birth_date': '1994-05-17', â\n",
"â â 'country': 'Bahamas', â\n",
"â â 'marital_status': 'widowed', â\n",
"â â 'education_level': 'doctorate', â\n",
"â â 'unit': '', â\n",
"â â 'occupation': 'Metallurgist', â\n",
"â â 'phone_number': '0117 496 0534', â\n",
"â â 'bachelors_field': 'arts_humanities' â\n",
"â â } â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â number_of_stars â 4 â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â review_style â detailed â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â product_name â CozyNest Organizer â\n",
"â â â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â customer_review â **Product Review: CozyNest Organizer (4 Stars)** â\n",
"â â *By Angela, 31* â\n",
"â â â\n",
"â â I recently purchased the CozyNest Organizer from Mohammedview, and Iâm thrilled to share â\n",
"â â my experience. As someone whoâs always struggled with clutter in my workspace and home, I â\n",
"â â was excited to try this product, and Iâm glad I didâthough I have a few thoughts to â\n",
"â â share. â\n",
"â â â\n",
"â â **Design and Build Quality** â\n",
"â â The CozyNest Organizer is a sleek, modern piece that immediately caught my eye. Its â\n",
"â â minimalist design blends well with my existing decor, and the materials feel durableâno â\n",
"â â flimsy plastic or cheap finishes. The organizers are made of a sturdy, eco-friendly â\n",
"â â fabric thatâs both soft to the touch and resistant to stains, which is a huge plus. I â\n",
"â â appreciate the attention to detail, like the reinforced stitching and the way the â\n",
"â â compartments are thoughtfully sized for different items. Itâs not just a pretty box; itâs â\n",
"â â built to last. â\n",
"â â â\n",
"â â **Functionality** â\n",
"â â This is where the CozyNest truly shines. The organizer comes with multiple compartments, â\n",
"â â each designed for specific purposesâpens, papers, small electronics, and even a hidden â\n",
"â â pocket for receipts or keys. Iâve used it in my office to keep my desk tidy, and itâs â\n",
"â â been a game-changer. The layout is intuitive, and I can easily find what I need without â\n",
"â â rummaging through a pile of items. However, I did find that the largest compartment is a â\n",
"â â bit smaller than I expected, which made it a bit tight when storing bulkier items. That â\n",
"â â said, itâs perfect for everyday essentials. â\n",
"â â â\n",
"â â **Ease of Use and Assembly** â\n",
"â â Setting up the CozyNest was straightforward. The instructions were clear, and it took me â\n",
"â â about 10 minutes to assemble. The pieces fit together securely, and there were no tools â\n",
"â â required, which is a big win. Once assembled, itâs easy to move around or adjust as â\n",
"â â needed. I also love that itâs lightweight yet stable, so it doesnât tip over easily. â\n",
"â â â\n",
"â â **Comfort and Practicality** â\n",
"â â One of the standout features is how comfortable it is to use. The fabric is soft enough â\n",
"â â that I donât mind touching it frequently, and the compartments are deep enough to hold â\n",
"â â items without feeling cramped. Iâve used it to organize my craft supplies at home, and â\n",
"â â itâs made my creative sessions much more enjoyable. The only minor downside is that the â\n",
"â â fabric isnât waterproof, so I had to be careful with spills. But for general use, this â\n",
"â â hasnât been an issue. â\n",
"â â â\n",
"â â **Value for Money** â\n",
"â â At the price point, I feel the CozyNest Organizer is well worth it. Itâs not the cheapest â\n",
"â â option on the market, but the quality and functionality justify the cost. Iâve already â\n",
"â â noticed a significant reduction in clutter, and itâs helped me stay more organized in â\n",
"â â both my personal and professional life. â\n",
"â â â\n",
"â â **Final Thoughts** â\n",
"â â While I gave it 4 stars instead of 5, itâs because of the slightly smaller main â\n",
"â â compartment and the lack of waterproofing. These are minor flaws, though, and they donât â\n",
"â â detract from the overall value. If youâre looking for a stylish, functional organizer â\n",
"â â thatâs easy to use and durable, the CozyNest is a fantastic choice. Iâd definitely â\n",
"â â recommend it to anyone needing a reliable way to declutter their space. â\n",
"â â â\n",
"â â Thanks to Mohammedview for offering such a great productâIâm already considering â\n",
"â â purchasing another one for my home! â\n",
"â â â\n",
"âââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\n",
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"â product_category â Home & Kitchen â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
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"â target_age_range â 50-65 â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
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"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â number_of_stars â 4 â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â review_style â detailed â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â product_name â CozyNest Organizer â\n",
"â â â\n",
"âââââââââââââââââââââââŧââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤\n",
"â customer_review â **Product Review: CozyNest Organizer (4 Stars)** â\n",
"â â *By Angela, 31* â\n",
"â â â\n",
"â â I recently purchased the CozyNest Organizer from Mohammedview, and Iâm thrilled to share â\n",
"â â my experience. As someone whoâs always struggled with clutter in my workspace and home, I â\n",
"â â was excited to try this product, and Iâm glad I didâthough I have a few thoughts to â\n",
"â â share. â\n",
"â â â\n",
"â â **Design and Build Quality** â\n",
"â â The CozyNest Organizer is a sleek, modern piece that immediately caught my eye. Its â\n",
"â â minimalist design blends well with my existing decor, and the materials feel durableâno â\n",
"â â flimsy plastic or cheap finishes. The organizers are made of a sturdy, eco-friendly â\n",
"â â fabric thatâs both soft to the touch and resistant to stains, which is a huge plus. I â\n",
"â â appreciate the attention to detail, like the reinforced stitching and the way the â\n",
"â â compartments are thoughtfully sized for different items. Itâs not just a pretty box; itâs â\n",
"â â built to last. â\n",
"â â â\n",
"â â **Functionality** â\n",
"â â This is where the CozyNest truly shines. The organizer comes with multiple compartments, â\n",
"â â each designed for specific purposesâpens, papers, small electronics, and even a hidden â\n",
"â â pocket for receipts or keys. Iâve used it in my office to keep my desk tidy, and itâs â\n",
"â â been a game-changer. The layout is intuitive, and I can easily find what I need without â\n",
"â â rummaging through a pile of items. However, I did find that the largest compartment is a â\n",
"â â bit smaller than I expected, which made it a bit tight when storing bulkier items. That â\n",
"â â said, itâs perfect for everyday essentials. â\n",
"â â â\n",
"â â **Ease of Use and Assembly** â\n",
"â â Setting up the CozyNest was straightforward. The instructions were clear, and it took me â\n",
"â â about 10 minutes to assemble. The pieces fit together securely, and there were no tools â\n",
"â â required, which is a big win. Once assembled, itâs easy to move around or adjust as â\n",
"â â needed. I also love that itâs lightweight yet stable, so it doesnât tip over easily. â\n",
"â â â\n",
"â â **Comfort and Practicality** â\n",
"â â One of the standout features is how comfortable it is to use. The fabric is soft enough â\n",
"â â that I donât mind touching it frequently, and the compartments are deep enough to hold â\n",
"â â items without feeling cramped. Iâve used it to organize my craft supplies at home, and â\n",
"â â itâs made my creative sessions much more enjoyable. The only minor downside is that the â\n",
"â â fabric isnât waterproof, so I had to be careful with spills. But for general use, this â\n",
"â â hasnât been an issue. â\n",
"â â â\n",
"â â **Value for Money** â\n",
"â â At the price point, I feel the CozyNest Organizer is well worth it. Itâs not the cheapest â\n",
"â â option on the market, but the quality and functionality justify the cost. Iâve already â\n",
"â â noticed a significant reduction in clutter, and itâs helped me stay more organized in â\n",
"â â both my personal and professional life. â\n",
"â â â\n",
"â â **Final Thoughts** â\n",
"â â While I gave it 4 stars instead of 5, itâs because of the slightly smaller main â\n",
"â â compartment and the lack of waterproofing. These are minor flaws, though, and they donât â\n",
"â â detract from the overall value. If youâre looking for a stylish, functional organizer â\n",
"â â thatâs easy to use and durable, the CozyNest is a fantastic choice. Iâd definitely â\n",
"â â recommend it to anyone needing a reliable way to declutter their space. â\n",
"â â â\n",
"â â Thanks to Mohammedview for offering such a great productâIâm already considering â\n",
"â â purchasing another one for my home! â\n",
"â â â\n",
"âââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\n",
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],
"source": [
"# Run this cell multiple times to cycle through the 10 preview records.\n",
"preview.display_sample_record()"
]
},
{
"cell_type": "code",
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" Fiction | \n",
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" **Product Review: WorkNest Pro â A Game-Change... | \n",
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\n",
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" **Product Review: Mindful Moments â 4 Stars** ... | \n",
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\n",
"
ââââââââââââââââââââââââââââââââââââââââ đ¨ Data Designer Dataset Profile âââââââââââââââââââââââââââââââââââââââââ\n",
"\n",
" \n",
" Dataset Overview \n",
"âââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââââââââââââââ\n",
"â number of records â number of columns â percent complete records â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â 10 â 8 â 100.0% â\n",
"âââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââ\n",
" \n",
" \n",
" đ˛ Sampler Columns \n",
"ââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââŗâââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââââââ\n",
"â column name â data type â number unique values â sampler type â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_category â string â 5 (50.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â product_subcategory â string â 9 (90.0%) â subcategory â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â target_age_range â string â 5 (50.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â customer â dict â 10 (100.0%) â person â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â number_of_stars â int â 3 (30.0%) â uniform â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â review_style â string â 4 (40.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââ´âââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââââââ\n",
" \n",
" \n",
" đ LLM-Text Columns \n",
"âââââââââââââââââââââââââŗââââââââââââââââŗâââââââââââââââââââââââââââââŗââââââââââââââââââââŗâââââââââââââââââââââââââ\n",
"â â â â prompt tokens â completion tokens â\n",
"â column name â data type â number unique values â per record â per record â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_name â string â 10 (100.0%) â 77.0 +/- 0.7 â 4.5 +/- 1.0 â\n",
"âââââââââââââââââââââââââŧââââââââââââââââŧâââââââââââââââââââââââââââââŧââââââââââââââââââââŧâââââââââââââââââââââââââ¤\n",
"â customer_review â string â 10 (100.0%) â 63.0 +/- 1.5 â 625.0 +/- 281.1 â\n",
"âââââââââââââââââââââââââ´ââââââââââââââââ´âââââââââââââââââââââââââââââ´ââââââââââââââââââââ´âââââââââââââââââââââââââ\n",
" \n",
" \n",
"âââââââââââââââââââââââââââââââââââââââââââââââââââ Table Notes âââââââââââââââââââââââââââââââââââââââââââââââââââŽ\n",
"â â\n",
"â 1. All token statistics are based on a sample of max(1000, len(dataset)) records. â\n",
"â 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. â\n",
"â â\n",
"â°ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¯\n",
" \n",
"âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\n",
"\n"
],
"text/plain": [
"\u001b[1;38;2;118;185;0mââââââââââââââââââââââââââââââââââââââââ \u001b[0mđ¨ Data Designer Dataset Profile\u001b[1;38;2;118;185;0m âââââââââââââââââââââââââââââââââââââââââ\u001b[0m\n",
"\n",
" \n",
"\u001b[1;38;2;118;185;0m Dataset Overview \u001b[0m\n",
"âââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mnumber of records \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2mnumber of columns \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2mpercent complete records \u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â 10 â 8 â 100.0% â\n",
"âââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[1;38;2;118;185;0m đ˛ Sampler Columns \u001b[0m\n",
"ââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââŗâââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mcolumn name \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m data type\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m number unique values\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m sampler type\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_category â string â 5 (50.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â product_subcategory â string â 9 (90.0%) â subcategory â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â target_age_range â string â 5 (50.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â customer â dict â 10 (100.0%) â person â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â number_of_stars â int â 3 (30.0%) â uniform â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â review_style â string â 4 (40.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââ´âââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[1;38;2;118;185;0m đ LLM-Text Columns \u001b[0m\n",
"âââââââââââââââââââââââââŗââââââââââââââââŗâââââââââââââââââââââââââââââŗââââââââââââââââââââŗâââââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m prompt tokens\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m completion tokens\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mcolumn name \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m data type\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m number unique values\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m per record\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m per record\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_name â string â 10 (100.0%) â 77.0 +/- 0.7 â 4.5 +/- 1.0 â\n",
"âââââââââââââââââââââââââŧââââââââââââââââŧâââââââââââââââââââââââââââââŧââââââââââââââââââââŧâââââââââââââââââââââââââ¤\n",
"â customer_review â string â 10 (100.0%) â 63.0 +/- 1.5 â 625.0 +/- 281.1 â\n",
"âââââââââââââââââââââââââ´ââââââââââââââââ´âââââââââââââââââââââââââââââ´ââââââââââââââââââââ´âââââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[2mââ\u001b[0m\u001b[2mâââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\u001b[2m Table Notes \u001b[0m\u001b[2mâââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\u001b[2mââŽ\u001b[0m\n",
"\u001b[2mâ\u001b[0m \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m 1. All token statistics are based on a sample of max(1000, len(dataset)) records. \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ°ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¯\u001b[0m\n",
" \n",
"\u001b[1;38;2;118;185;0mâââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Print the analysis as a table.\n",
"preview.analysis.to_report()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### đ Scale up!\n",
"\n",
"- Happy with your preview data?\n",
"\n",
"- Use the `create` method to submit larger Data Designer generation jobs.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[19:00:31] [INFO] đ¨ Creating Data Designer dataset\n",
"[19:00:31] [INFO] â
Validation passed\n",
"[19:00:31] [INFO] âī¸ Sorting column configs into a Directed Acyclic Graph\n",
"[19:00:31] [INFO] đŠē Running health checks for models...\n",
"[19:00:31] [INFO] |-- đ Checking 'nvidia/nvidia-nemotron-nano-9b-v2' in provider named 'nvidia' for model alias 'nemotron-nano-v2'...\n",
"[19:00:32] [INFO] |-- â
Passed!\n",
"[19:00:32] [INFO] âŗ Processing batch 1 of 1\n",
"[19:00:32] [INFO] đ˛ Preparing samplers to generate 20 records across 6 columns\n",
"[19:00:32] [INFO] đ Preparing llm-text column generation\n",
"[19:00:32] [INFO] |-- column name: 'product_name'\n",
"[19:00:32] [INFO] |-- model config:\n",
"{\n",
" \"alias\": \"nemotron-nano-v2\",\n",
" \"model\": \"nvidia/nvidia-nemotron-nano-9b-v2\",\n",
" \"inference_parameters\": {\n",
" \"temperature\": 0.5,\n",
" \"top_p\": 1.0,\n",
" \"max_tokens\": 1024,\n",
" \"max_parallel_requests\": 4,\n",
" \"timeout\": null,\n",
" \"extra_body\": null\n",
" },\n",
" \"provider\": \"nvidia\"\n",
"}\n",
"[19:00:32] [INFO] đ Processing llm-text column 'product_name' with 4 concurrent workers\n",
"[19:00:43] [INFO] đ Preparing llm-text column generation\n",
"[19:00:43] [INFO] |-- column name: 'customer_review'\n",
"[19:00:43] [INFO] |-- model config:\n",
"{\n",
" \"alias\": \"nemotron-nano-v2\",\n",
" \"model\": \"nvidia/nvidia-nemotron-nano-9b-v2\",\n",
" \"inference_parameters\": {\n",
" \"temperature\": 0.5,\n",
" \"top_p\": 1.0,\n",
" \"max_tokens\": 1024,\n",
" \"max_parallel_requests\": 4,\n",
" \"timeout\": null,\n",
" \"extra_body\": null\n",
" },\n",
" \"provider\": \"nvidia\"\n",
"}\n",
"[19:00:43] [INFO] đ Processing llm-text column 'customer_review' with 4 concurrent workers\n",
"[19:01:10] [INFO] đ Model usage summary:\n",
"{\n",
" \"nvidia/nvidia-nemotron-nano-9b-v2\": {\n",
" \"token_usage\": {\n",
" \"prompt_tokens\": 3263,\n",
" \"completion_tokens\": 9038,\n",
" \"total_tokens\": 12301\n",
" },\n",
" \"request_usage\": {\n",
" \"successful_requests\": 40,\n",
" \"failed_requests\": 0,\n",
" \"total_requests\": 40\n",
" },\n",
" \"tokens_per_second\": 323,\n",
" \"requests_per_minute\": 63\n",
" }\n",
"}\n",
"[19:01:10] [INFO] đ Measuring dataset column statistics:\n",
"[19:01:10] [INFO] |-- đ˛ column: 'product_category'\n",
"[19:01:10] [INFO] |-- đ˛ column: 'product_subcategory'\n",
"[19:01:10] [INFO] |-- đ˛ column: 'target_age_range'\n",
"[19:01:10] [INFO] |-- đ˛ column: 'customer'\n",
"[19:01:10] [INFO] |-- đ˛ column: 'number_of_stars'\n",
"[19:01:10] [INFO] |-- đ˛ column: 'review_style'\n",
"[19:01:10] [INFO] |-- đ column: 'product_name'\n",
"[19:01:10] [INFO] |-- đ column: 'customer_review'\n"
]
}
],
"source": [
"results = data_designer.create(config_builder, num_records=20)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
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\n",
" \n",
" \n",
" | \n",
" product_category | \n",
" product_subcategory | \n",
" target_age_range | \n",
" customer | \n",
" number_of_stars | \n",
" review_style | \n",
" product_name | \n",
" customer_review | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Electronics | \n",
" Headphones | \n",
" 35-50 | \n",
" {'age': 44, 'bachelors_field': 'arts_humanitie... | \n",
" 3 | \n",
" detailed | \n",
" SoundSage | \n",
" **Review Title: A Mixed Experience with SoundS... | \n",
"
\n",
" \n",
" | 1 | \n",
" Home Office | \n",
" Storage | \n",
" 50-65 | \n",
" {'age': 52, 'bachelors_field': 'no_degree', 'b... | \n",
" 3 | \n",
" structured with bullet points | \n",
" TimelessTote | \n",
" **Review of TimelessTote (3 Stars)** \n",
"\n",
"- **De... | \n",
"
\n",
" \n",
" | 2 | \n",
" Electronics | \n",
" Accessories | \n",
" 25-35 | \n",
" {'age': 56, 'bachelors_field': 'no_degree', 'b... | \n",
" 4 | \n",
" detailed | \n",
" NeonPulse | \n",
" **Product Review: NeonPulse by Mathew from Kie... | \n",
"
\n",
" \n",
" | 3 | \n",
" Electronics | \n",
" Smartphones | \n",
" 65+ | \n",
" {'age': 48, 'bachelors_field': 'no_degree', 'b... | \n",
" 4 | \n",
" detailed | \n",
" SeniorSense Smartphone | \n",
" **Product Review: SeniorSense Smartphone â A T... | \n",
"
\n",
" \n",
" | 4 | \n",
" Home Office | \n",
" Desks | \n",
" 35-50 | \n",
" {'age': 48, 'bachelors_field': 'no_degree', 'b... | \n",
" 2 | \n",
" brief | \n",
" WorkspaceElevate | \n",
" **Review:** Disappointing. Expected more from ... | \n",
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ââââââââââââââââââââââââââââââââââââââââ đ¨ Data Designer Dataset Profile âââââââââââââââââââââââââââââââââââââââââ\n",
"\n",
" \n",
" Dataset Overview \n",
"âââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââââââââââââââ\n",
"â number of records â number of columns â percent complete records â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â 20 â 8 â 100.0% â\n",
"âââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââ\n",
" \n",
" \n",
" đ˛ Sampler Columns \n",
"ââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââŗâââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââââââ\n",
"â column name â data type â number unique values â sampler type â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_category â string â 5 (25.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â product_subcategory â string â 14 (70.0%) â subcategory â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â target_age_range â string â 5 (25.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â customer â dict â 20 (100.0%) â person â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â number_of_stars â int â 4 (20.0%) â uniform â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â review_style â string â 4 (20.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââ´âââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââââââ\n",
" \n",
" \n",
" đ LLM-Text Columns \n",
"âââââââââââââââââââââââââŗââââââââââââââââŗâââââââââââââââââââââââââââââŗââââââââââââââââââââŗâââââââââââââââââââââââââ\n",
"â â â â prompt tokens â completion tokens â\n",
"â column name â data type â number unique values â per record â per record â\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_name â string â 20 (100.0%) â 77.0 +/- 0.8 â 4.5 +/- 0.9 â\n",
"âââââââââââââââââââââââââŧââââââââââââââââŧâââââââââââââââââââââââââââââŧââââââââââââââââââââŧâââââââââââââââââââââââââ¤\n",
"â customer_review â string â 20 (100.0%) â 62.0 +/- 2.1 â 406.5 +/- 322.5 â\n",
"âââââââââââââââââââââââââ´ââââââââââââââââ´âââââââââââââââââââââââââââââ´ââââââââââââââââââââ´âââââââââââââââââââââââââ\n",
" \n",
" \n",
"âââââââââââââââââââââââââââââââââââââââââââââââââââ Table Notes âââââââââââââââââââââââââââââââââââââââââââââââââââŽ\n",
"â â\n",
"â 1. All token statistics are based on a sample of max(1000, len(dataset)) records. â\n",
"â 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. â\n",
"â â\n",
"â°ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¯\n",
" \n",
"âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\n",
"\n"
],
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"\u001b[1;38;2;118;185;0mââââââââââââââââââââââââââââââââââââââââ \u001b[0mđ¨ Data Designer Dataset Profile\u001b[1;38;2;118;185;0m âââââââââââââââââââââââââââââââââââââââââ\u001b[0m\n",
"\n",
" \n",
"\u001b[1;38;2;118;185;0m Dataset Overview \u001b[0m\n",
"âââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââŗââââââââââââââââââââââââââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mnumber of records \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2mnumber of columns \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2mpercent complete records \u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â 20 â 8 â 100.0% â\n",
"âââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââ´ââââââââââââââââââââââââââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[1;38;2;118;185;0m đ˛ Sampler Columns \u001b[0m\n",
"ââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââŗâââââââââââââââââââââââââââââââââââââŗâââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mcolumn name \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m data type\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m number unique values\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m sampler type\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_category â string â 5 (25.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â product_subcategory â string â 14 (70.0%) â subcategory â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â target_age_range â string â 5 (25.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â customer â dict â 20 (100.0%) â person â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â number_of_stars â int â 4 (20.0%) â uniform â\n",
"ââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââŧâââââââââââââââââââââââââââââââââââââŧâââââââââââââââââââââââ¤\n",
"â review_style â string â 4 (20.0%) â category â\n",
"ââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââ´âââââââââââââââââââââââââââââââââââââ´âââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[1;38;2;118;185;0m đ LLM-Text Columns \u001b[0m\n",
"âââââââââââââââââââââââââŗââââââââââââââââŗâââââââââââââââââââââââââââââŗââââââââââââââââââââŗâââââââââââââââââââââââââ\n",
"â\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m prompt tokens\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m completion tokens\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"â\u001b[1;2m \u001b[0m\u001b[1;2mcolumn name \u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m data type\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m number unique values\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m per record\u001b[0m\u001b[1;2m \u001b[0mâ\u001b[1;2m \u001b[0m\u001b[1;2m per record\u001b[0m\u001b[1;2m \u001b[0mâ\n",
"âĄââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââŠ\n",
"â product_name â string â 20 (100.0%) â 77.0 +/- 0.8 â 4.5 +/- 0.9 â\n",
"âââââââââââââââââââââââââŧââââââââââââââââŧâââââââââââââââââââââââââââââŧââââââââââââââââââââŧâââââââââââââââââââââââââ¤\n",
"â customer_review â string â 20 (100.0%) â 62.0 +/- 2.1 â 406.5 +/- 322.5 â\n",
"âââââââââââââââââââââââââ´ââââââââââââââââ´âââââââââââââââââââââââââââââ´ââââââââââââââââââââ´âââââââââââââââââââââââââ\n",
" \n",
" \n",
"\u001b[2mââ\u001b[0m\u001b[2mâââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\u001b[2m Table Notes \u001b[0m\u001b[2mâââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\u001b[2mââŽ\u001b[0m\n",
"\u001b[2mâ\u001b[0m \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m 1. All token statistics are based on a sample of max(1000, len(dataset)) records. \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ\u001b[0m \u001b[2mâ\u001b[0m\n",
"\u001b[2mâ°ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¯\u001b[0m\n",
" \n",
"\u001b[1;38;2;118;185;0mâââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the analysis results into memory.\n",
"analysis = results.load_analysis()\n",
"\n",
"analysis.to_report()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## âī¸ Next Steps\n",
"\n",
"Now that you've seen the basics of Data Designer, check out the following notebooks to learn more about:\n",
"\n",
"- [Structured outputs and jinja expressions](/notebooks/2-structured-outputs-and-jinja-expressions/)\n",
"\n",
"- [Seeding synthetic data generation with an external dataset](/notebooks/3-seeding-with-a-dataset/)\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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}