modified healthcare notebooks for 25.10

Author: raosukrit67

nemo/NeMo-Data-Designer/self-hosted-tutorials/community-contributions/forms/w2-dataset.ipynb

@@ -599,18 +599,20 @@
     "\n",
     "#### 📚 What you'll learn\n",
     "\n",
-    "The notebook demonstrates how you can combine numerical samplers, the person sampler and LLMs to create a synthetic dataset of W-2 forms (US Wage & Tax Statements).\n",
+    "The notebook demonstrates how you can combine numerical samplers, the person sampler and LLMs to create a synthetic\\\n",
+    " dataset of W-2 forms (US Wage & Tax Statements).\n",
     "\n",
     "- We will use generate numerical fields using [statistics published by the IRS](https://www.irs.gov/pub/irs-pdf/p5385.pdf) for the year 2021:\n",
     "\n",
-    "- We will use the person sampler to generate realistic US taxpayers. When the US locale is chosen, statistics for generated persons reflect real-world census data.\n",
+    "- We will use the person sampler to generate realistic US taxpayers. When the US locale is chosen, statistics\\\n",
+    " for generated persons reflect real-world census data.\n",
     "\n",
     "\n",
     "<br>\n",
     "\n",
     "> 👋 **IMPORTANT** – Environment Setup\n",
     ">\n",
-    "> - If you haven't already, follow the instructions in the [README](../README.md) to install the necessary dependencies.\n",
+    "> - If you haven't already, follow the instructions in the [README](../../../README.md) to install the necessary dependencies.\n",
     ">\n",
     "> - You may need to restart your notebook's kernel after setting up the environment.\n",
     "> - In this notebook, we assume you have a self-hosted instance of Data Designer up and running.\n",
@@ -755,13 +757,14 @@
    "id": "bbcb3538",
    "metadata": {},
    "source": [
-    "### 🎲  Setting Up Taxpayer and Employer Sampling\n",
+    "## 🎲  Setting Up Taxpayer and Employer Sampling\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",
-    "- The persona samplers allow you to sample realistic details of individuals using a model trained on the US Census. If the locale of the persona you are generating is anything other than `en_US`, then the personas will be generated using Faker"
+    "- The persona samplers allow you to sample realistic details of individuals using a model trained on the US Census.\\\n",
+    " If the locale of the persona you are generating is anything other than `en_US`, then the personas will be generated using Faker"
    ]
   },
   {
@@ -800,7 +803,7 @@
    "id": "28397d74",
    "metadata": {},
    "source": [
-    "### ⚡️ Defining the Fields\n",
+    "## ⚡️ Defining the Fields\n",
     "\n",
     "We will focus on the following:\n",
     "- Box 1 (Wages, tips, and other compensation)\n",
@@ -819,7 +822,8 @@
     "\n",
     "### Numerical fields\n",
     "\n",
-    "Here, we'll define how to generate numerical samples for the currency fields of the W-2 (Boxes 1-7). We'll use the W-2 statistics from the IRS linked above to generate realistic samples."
+    "Here, we'll define how to generate numerical samples for the currency fields of the W-2 (Boxes 1-7). \\\n",
+    "We'll use the W-2 statistics from the IRS linked above to generate realistic samples."
    ]
   },
   {
@@ -999,7 +1003,8 @@
    "source": [
     "### 🦜 Non-numerical Fields\n",
     "\n",
-    "The remaining fields contain information about the employee (taxpayer) and the employer. We'll use the person sampler in combination with an LLM to generate values here."
+    "The remaining fields contain information about the employee (taxpayer) and the employer. \\\n",
+    "We'll use the person sampler in combination with an LLM to generate values here."
    ]
   },
   {
@@ -1162,7 +1167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "job_results = data_designer_client.create(config_builder, num_records=2)\n",
+    "job_results = data_designer_client.create(config_builder, num_records=20)\n",
     "\n",
     "# This will block until the job is complete.\n",
     "job_results.wait_until_done()"
@@ -1206,7 +1211,7 @@
     "# Download the job artifacts and save them to disk.\n",
     "job_results.download_artifacts(\n",
     "    output_path=TUTORIAL_OUTPUT_PATH,\n",
-    "    artifacts_folder_name=\"artifacts-community-contributions-w2-dataset\",\n",
+    "    artifacts_folder_name=\"artifacts-community-contributions-forms-w2-dataset\",\n",
     ");"
    ]
   }