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docs/chapter6/Session_6_1_BERTopic_Topic_Modeling.ipynb

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"--- \n",
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"## 1️⃣ Load the BBC News Dataset\n",
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"## 💾 Load the BBC News Dataset\n",
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"We’ll start by loading the **BBC News** dataset using the Hugging Face `datasets` library.\n",
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"Each article is represented as a short paragraph of text, and labeled with its corresponding topic. While we won’t use the labels for **unsupervised topic modeling**, they are useful later for evaluation and visualization.\n",
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"Let’s load the data and preview a few examples. We will concatenate the test and train sets to have a larger dataset to play with.\n"
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"Let’s load the data and preview a few examples. We will concatenate the test\n"
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"cell_type": "markdown",
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"## 2️⃣ Quick Intro to BERTopic + LiteLLM\n",
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"## ℹ️ Quick Intro to BERTopic + LiteLLM\n",
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"Now let’s briefly introduce the key tools we’ll use to extract and **interpret** topics:\n",
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"## 3️⃣ Fit BERTopic and Explore Topics\n",
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"## 💬 Fit BERTopic and Explore Topics\n",
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"Now let’s apply BERTopic to our dataset.\n",
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"## 📊 4️⃣ Visualize Topic Clusters with LLM-Generated Titles\n",
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"## 📊 Visualize Topic Clusters with LLM-Generated Titles\n",
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"After fitting our BERTopic model on the corpus, the next step is to explore the **semantic structure** of our dataset by visualizing:\n",
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"## 📋 5️⃣ Topic Summary Table & Exploration\n",
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"## 📋 Topic Summary Table & Exploration\n",
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"Let’s now inspect what each topic is about.\n",
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"## 🔍 6️⃣ Step-by-Step Breakdown: How BERTopic Works\n",
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"## 🔍 Step-by-Step Breakdown: How BERTopic Works\n",
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"In the first half of this notebook, we focused on using BERTopic like a black box. Now it’s time to **open that box** and understand how it really works.\n",
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