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sync generated colab notebooks
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‎docs/colab_notebooks/1-the-basics.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "e9bc2aab",
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"metadata": {},
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"id": "f5bc03e0",
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"metadata": {
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"nemo_colab_inject": true
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/NVIDIA-NeMo/DataDesigner/blob/main/docs/colab_notebooks/1-the-basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"cell_type": "markdown",
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"id": "33dcb5be",
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"id": "3454d676",
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"metadata": {},
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"source": [
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"# 🎨 Data Designer Tutorial: The Basics\n",
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},
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{
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"cell_type": "markdown",
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"id": "4737bc0d",
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"metadata": {},
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"source": [
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"### 📦 Import Data Designer\n",
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},
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{
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"cell_type": "markdown",
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"id": "170ce1ea",
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"id": "cf21b784",
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"metadata": {
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"nemo_colab_inject": true
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},
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"source": [
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"### ⚡ Colab Setup\n",
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"\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67e478f9",
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"metadata": {},
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"id": "a87ee38a",
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"metadata": {
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"nemo_colab_inject": true
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"outputs": [],
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"%%capture\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "533fc40d",
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"id": "d6c4cc8a",
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"metadata": {
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"nemo_colab_inject": true
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},
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"outputs": [],
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"import getpass\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "b97786a8",
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"metadata": {},
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"source": [
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"### ⚙️ Initialize the Data Designer interface\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"id": "2fef5ae8",
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"source": [
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"### 🎛️ Define model configurations\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"id": "1d0a178f",
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"metadata": {},
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"source": [
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"### 🏗️ Initialize the Data Designer Config Builder\n",
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{
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"cell_type": "code",
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"id": "4be3497f",
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"## 🎲 Getting started with sampler columns\n",
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{
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"execution_count": null,
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"id": "c28350d3",
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"Let's start designing our product review dataset by adding product category and subcategory columns.\n"
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{
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"Next, let's add samplers to generate data related to the customer and their review.\n"
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"cell_type": "code",
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"## 🦜 LLM-generated columns\n",
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{
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"### 🔁 Iteration is key – preview the dataset!\n",
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{
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"### 📊 Analyze the generated data\n",
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"### 🆙 Scale up!\n",
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"## ⏭️ Next Steps\n",

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