<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Fasttext on Xabi Ezpeleta</title><link>https://xezpeleta.github.io/en/tags/fasttext/</link><description>Recent content in Fasttext on Xabi Ezpeleta</description><generator>Hugo -- 0.163.3</generator><language>en</language><copyright>Copyright © 2021, Xabier Ezpeleta. License CC BY-SA 4.0.</copyright><lastBuildDate>Sun, 28 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://xezpeleta.github.io/en/tags/fasttext/index.xml" rel="self" type="application/rss+xml"/><item><title>Identifying Basque dialects</title><link>https://xezpeleta.github.io/en/blog/nongoeuskara/</link><pubDate>Sun, 28 Jun 2026 00:00:00 +0000</pubDate><guid>https://xezpeleta.github.io/en/blog/nongoeuskara/</guid><description>&lt;h2 id="tldr"&gt;TL;DR&lt;/h2&gt;
&lt;p&gt;I&amp;rsquo;ve created an experiment called &lt;a href="https://itzune.eus/nongoeuskara/"&gt;NongoEuskara&lt;/a&gt;. To do this, I gathered data, trained a &lt;em&gt;fastText&lt;/em&gt; classification/embedding model, and built this little demo website. Using &lt;em&gt;WebAssembly&lt;/em&gt;, &lt;a href="https://huggingface.co/itzune/zeineuski"&gt;these models&lt;/a&gt; run directly in the browser.&lt;/p&gt;
&lt;h2 id="the-long-explanation"&gt;The long explanation&lt;/h2&gt;
&lt;p&gt;We know that large language models (LLMs) are capable of doing pretty much everything. Even though they weren&amp;rsquo;t originally designed for it, today they can program, translate, transcribe audio, summarize long texts, and a long etcetera. Sometimes, however, the best solution isn&amp;rsquo;t always using an LLM. For example, for quick and simple translations, there are lighter neural translators that require far fewer resources than LLMs.&lt;/p&gt;</description></item></channel></rss>