1.0Glosarixhttps://glosarix.com/en/Team Glosarixhttps://glosarix.com/en/author/adm_glosarix/Pre-trained model - Glosarixrich600338<blockquote class="wp-embedded-content" data-secret="SpRhVrL9GZ"><a href="https://glosarix.com/en/glossary/pre-trained-model-en/">Pre-trained model</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/pre-trained-model-en/embed/#?secret=SpRhVrL9GZ" width="600" height="338" title="“Pre-trained model” — Glosarix" data-secret="SpRhVrL9GZ" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
/*! This file is auto-generated */
!function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document);
//# sourceURL=https://glosarix.com/wp-includes/js/wp-embed.min.js
</script>
Description: A pre-trained model is a type of machine learning model that has been previously trained on a massive dataset before being fine-tuned for specific tasks. This approach allows the model to acquire general knowledge about language, patterns, and structures, making it easier to adapt to concrete tasks with less data and training time. Pre-trained […]