<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Structure on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/data-structure/</link><description>Recent content in Data Structure on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/data-structure/index.xml" rel="self" type="application/rss+xml"/><item><title>Model Index</title><link>https://terms-en.ai-term-hub.com/en/terms/model_index/</link><pubDate>Sat, 18 Jul 2026 10:07:39 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/model_index/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>The index file, typically named &amp;lsquo;model_index.json&amp;rsquo;, contains structured information about a model&amp;rsquo;s architecture, including pipeline type, sub-models, and configuration paths. It enables the Hub to correctly load and instantiate complex pipelines by mapping abstract identifiers to specific model files, ensuring interoperability between different libraries and versions within the ecosystem.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>A Model Index is a metadata file used by the Hugging Face Hub to describe and organize model components and configurations.&lt;/p></description></item><item><title>high-dimensional</title><link>https://terms-en.ai-term-hub.com/en/terms/high_dimensional/</link><pubDate>Sat, 18 Jul 2026 09:38:34 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/high_dimensional/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>High-dimensional refers to datasets or vector spaces containing a vast number of attributes or features. In AI, this is common in text embeddings, image pixels, or gene expression data. While rich in information, high dimensionality can cause the &amp;lsquo;curse of dimensionality,&amp;rsquo; where data becomes sparse, distances between points lose meaning, and models require significantly more data and computational power to learn effectively.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Describes data spaces with a large number of features or dimensions, often leading to sparsity and computational challenges.&lt;/p></description></item></channel></rss>