The Complete Guide to BERT Language Architecture & Model Variations | deepset Blog

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 back to resources BLOG AI Fundamentals The Complete Guide to BERT Language Architecture & Model Variations

 The BERT language model greatly improved the standard for language models. This article explains BERT’s history and the language models derived from it.

 By Tuana Çelik , Published on January 16, 2023 12 min read 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 Anyone who has studied natural language processing (NLP) can tell you that the state of the art moves exceptionally fast. Big players like Google, Facebook, or OpenAI employ large teams of experts to come up with new solutions that bring computers ever closer to a seemingly human-like understanding of language. This results in model architectures and other approaches quickly becoming obsolete, and what was considered cutting-edge technology six months ago may almost seem outdated today. Nevertheless, some models make such an impact that they become foundational knowledge even as they are eclipsed by their successors.

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 One model architecture for which this is true is BERT (short for B idirectional E ncoder R epresentations from T ransformers, an unwieldy name almost certainly picked for its friendly acronym). Although the first BERT model — born late 2018 — is rarely used in its original form today, the adaptability of this model architecture in terms of tasks, languages, ​​and even sizes means that direct BERT offspring are still thriving in all sorts of fields.

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 In the high-churn world of language models , it can be difficult to keep up and find the best option for your project. This post aims to refresh your knowledge of BERT, provide a survey of the various models that have iterated past the BERT baseline, and help you find the right BERT-like model for you.

 Who is BERT?

 Google researchers designed BERT as a general language model, adapting the Transformer architecture, which had made an enormous impact on the field of NLP just a year earlier. Aside from improving Google’s search results through its deep understanding of semantics, BERT’s main function is as a basis for specific “downstream” tasks like question answering or sentiment analysis. That’s because its ability to process written language at a near-human level greatly aids the BERT language model in solving other language-based tasks.

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 The impression BERT made on the NLP landscape in 2018 was incredible. After it was shown in the original paper that models based on BERT’s pre-trained architecture could outperform their competitors on many different tasks, industry observers predicted that this new model paradigm would be a game changer, one blog post even going so far as calling BERT “one model to rule them all.”

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