{"id":47753,"date":"2023-04-07T15:39:41","date_gmt":"2023-04-07T15:39:41","guid":{"rendered":"https:\/\/www.arimetrics.com\/glosario-digital\/google-bert"},"modified":"2026-09-14T10:47:12","modified_gmt":"2026-09-14T10:47:12","slug":"google-bert","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/google-bert","title":{"rendered":"Google BERT"},"content":{"rendered":"

\"GoogleDefinition:<\/strong><\/p>\n

Google BERT<\/strong> is a language model developed by Google that represents words by taking into account the context in which they appear. Its name stands for Bidirectional Encoder Representations from Transformers<\/em>.<\/p>\n

BERT is used in natural language processing tasks. Google also applies this technology in Search to understand how combinations of words express different meanings and intentions.<\/p>\n\n

Origins of BERT<\/h2>\n

Google introduced BERT in 2018 and released its code and pre-trained models to support its use in research and development.<\/p>\n

Its contribution built on the transformer architecture and the training of contextual language representations. Rather than always assigning the same representation to a word, BERT takes the surrounding words into account.<\/p>\n

In October 2019, Google announced the application of BERT models to ranking results and featured snippets. It is important to distinguish these dates: the model was developed before it was incorporated into the search engine.<\/p>\n

How bidirectional understanding works<\/h2>\n

BERT uses information before and after a word to represent its meaning within a sequence. It does not simply read a sentence twice in opposite directions<\/strong>; it considers the relationships between its elements together.<\/p>\n

For example, the Spanish word \u201cbanco\u201d does not mean the same thing in \u201csentarse en un banco\u201d (sitting on a bench) as in \u201cabrir una cuenta en el banco\u201d (opening an account at the bank). Context distinguishes these uses without treating the word as a unit with a fixed meaning.<\/p>\n

During the original pre-training process, one task involves masking certain elements of the text and learning to predict them from those that remain visible. Another works with the relationship between pairs of sentences.<\/p>\n

The model can then be adapted to a specific task through fine-tuning<\/a>. This adaptation requires additional training; it does not happen automatically when BERT is used to analyse a text.<\/p>\n

What BERT is used for<\/h2>\n

BERT provides language representations that can be used in different applications. Its uses include the following:<\/p>\n