{"version":"1.0","provider_name":"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8","provider_url":"https:\/\/atmokpo.com\/w","author_name":"root","author_url":"https:\/\/atmokpo.com\/w\/author\/root\/","title":"Using Hugging Face Transformers, BERT Vector Dimensions, Word Tokenization and Decoding - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"CP6jhDERVY\"><a href=\"https:\/\/atmokpo.com\/w\/36069\/\">Using Hugging Face Transformers, BERT Vector Dimensions, Word Tokenization and Decoding<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/atmokpo.com\/w\/36069\/embed\/#?secret=CP6jhDERVY\" width=\"600\" height=\"338\" title=\"&#8220;Using Hugging Face Transformers, BERT Vector Dimensions, Word Tokenization and Decoding&#8221; &#8212; \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" data-secret=\"CP6jhDERVY\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!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);\n\/\/# sourceURL=https:\/\/atmokpo.com\/w\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Natural language processing is a very important field in deep learning, and Hugging Face&#8217;s Transformer library helps to perform these tasks more easily. In this article, we will explore in detail the BERT (Bidirectional Encoder Representations from Transformers) model, vector dimensions, word tokenization, and decoding. Overview of the BERT Model BERT is a pre-trained language &hellip; \ub354 \ubcf4\uae30 \"\""}