{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf2cde26",
   "metadata": {},
   "source": [
    "# First LLM Classifier\n",
    "\n",
    "Learn how journalists use large-language models to organize and analyze massive datasets\n",
    "\n",
    "## What you will learn\n",
    "\n",
    "This class will give you hands-on experience creating a machine-learning model that can read and categorize the text recorded in newsworthy datasets.\n",
    "\n",
    "It will teach you how to:\n",
    "\n",
    "- Submit large-language model prompts with the Python programming language\n",
    "- Write structured prompts that can classify text into predefined categories\n",
    "- Submit dozens of prompts at once as part of an automated routine\n",
    "- Evaluate results using a rigorous, scientific approach\n",
    "- Improve results by training the model with rules and examples\n",
    "\n",
    "By the end, you will understand how LLM classifiers can outperform traditional machine-learning methods with significantly less code. And you will be ready to write a classifier on your own.\n",
    "\n",
    "## Who can take it\n",
    "\n",
    "This course is free. Anyone who has dabbled with code and AI is qualified to work through the materials. A curious mind and good attitude are all that’s required, but a familiarity with Python will certainly come in handy.\n",
    "\n",
    "💬 Need help or want to connect with others? Join the **Journalists on Hugging Face** community by signing up for our Slack group [here](https://forms.gle/JMCULh3jEdgFEsJu5).\n",
    "\n",
    "## Table of contents\n",
    "\n",
    "- [1. What we’ll do](ch1-what-we-will-do.ipynb)  \n",
    "- [2. The LLM advantage](ch2-the-LLM-advantage.ipynb)  \n",
    "- [3. Getting started with Hugging Face](ch3-getting-started-with-hf.ipynb)  \n",
    "- [4. Installing JupyterLab (optional)](ch4-installing-jupyterlab.ipynb)  \n",
    "- [5. Prompting with Python](ch5-prompting-with-python.ipynb)  \n",
    "- [6. Structured responses](ch6-structured-responses.ipynb)  \n",
    "- [7. Bulk prompts](ch7-bulk-prompts.ipynb)  \n",
    "- [8. Evaluating prompts](ch8-evaluating-prompts.ipynb)  \n",
    "- [9. Improving prompts](ch9-improving-prompts.ipynb)  \n",
    "- [10. Sharing your app with Gradio](ch10-sharing-with-gradio.ipynb)\n",
    "\n",
    "## About this class\n",
    "[Ben Welsh](https://palewi.re/who-is-ben-welsh/) and [Derek Willis](https://thescoop.org/about/) prepared this guide for [a training session](https://schedules.ire.org/nicar-2025/index.html#2045) at the National Institute for Computer-Assisted Reporting’s 2025 conference in Minneapolis.  \n",
    "The project was adapted to run on Hugging Face by [Florent Daudens](https://www.linkedin.com/in/fdaudens/).  \n",
    "\n",
    "Some of the copy was written with the assistance of GitHub’s Copilot, an AI-powered text generator. The materials are available as free and open source.\n",
    "\n",
    "**[1. What we’ll do →](ch1-what-we-will-do.ipynb)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02477b14-edff-4380-ad41-9954b6c80863",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}