<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/ad942dff91364257aa9d638a1c9f26ad&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/ad942dff91364257aa9d638a1c9f26ad-16d3009bb939498c.gif</thumbnail_url><duration>244.88</duration><title>Hugging Face Sentiment Model</title><description>In this video, I walk you through the process of setting up a sentiment analysis model using HuggingFace&apos;s pipeline. We create a folder, set up a Python file, and import the necessary libraries to analyze sentiment from various text inputs. I also emphasize the importance of specifying a particular model to avoid variability in results. Please make sure to follow along and run the script as I demonstrate the output.</description></oembed>