Topic Modeling with Llama 2

<p>With the advent of&nbsp;<strong>Llama 2</strong>, running strong LLMs locally has become more and more a reality. Its accuracy approaches OpenAI&rsquo;s GPT-3.5, which serves well for many use cases.</p> <p>In this article, we will explore how we can use Llama2 for Topic Modeling without the need to pass every single document to the model. Instead, we are going to leverage&nbsp;<a href="https://github.com/MaartenGr/BERTopic" rel="noopener ugc nofollow" target="_blank"><strong>BERTopic</strong></a>, a modular topic modeling technique that can use any LLM for fine-tuning topic representations.</p> <p>BERTopic works rather straightforward. It consists of 5 sequential steps:</p> <ol> <li>Embedding documents</li> <li>Reducing the dimensionality of embeddings</li> <li>Cluster reduced embeddings</li> <li>Tokenize documents per cluster</li> <li>Extract best-representing words per cluster</li> </ol> <p><a href="https://towardsdatascience.com/topic-modeling-with-llama-2-85177d01e174"><strong>Learn More</strong></a></p>
Tags: Llama Modeling