{"id":3329,"date":"2025-10-06T16:10:26","date_gmt":"2025-10-06T14:10:26","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/bert-la-guia-completa-para-entender-y-utilizar-modelos-preentrenados\/"},"modified":"2026-08-05T11:06:58","modified_gmt":"2026-08-05T09:06:58","slug":"bert","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/en\/bert","title":{"rendered":"BERT: The complete guide to understanding and using pre-trained models"},"content":{"rendered":"

The natural language processing (NLP)<\/strong> has undergone a radical transformation with the advent of pre-trained models based on deep neural networks. Among these, BERT (Bidirectional Encoder Representations from Transformers)<\/strong> It has been one of the most influential advances, enabling machines to understand language in a way that is much closer to that of humans.<\/p>\n

An Introduction to BERT and Natural Language Processing<\/h2>\n

Google unveiled BERT in 2018<\/strong> as an open-source model that revolutionised the way algorithms process text. Thanks to its architecture based on Transformers<\/strong>, BERT can analyse the full context of a word within a sentence in both directions (left and right), significantly improving accuracy in NLP tasks.<\/p>\n

Why BERT is important<\/h3>\n

Before the advent of BERT, most NLP models processed text sequentially or unidirectionally, which limited their ability to understand it. BERT introduced a bidirectional approach that allows words to be interpreted within their full context, which has significantly improved tasks such as Google search<\/a>, text classification and the generation of automated responses.<\/p>\n

Some of the reasons why BERT has marked a turning point in NLP include:<\/p>\n