What Is the BERT Model and How Does It Work?

Written by Jessica Schulze • Updated on

Meet BERT: An overview of how this language model is used, how it works, and how it's trained.

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As natural language processing (NLP) continues to advance, human-machine interaction has become more prevalent, meaningful, and convincing than ever. In the following article, you can take a closer look at how machines work to understand and generate human language. More specifically, you’ll learn what was so revolutionary about the emergence of the BERT model, as well as its architecture, use cases, and training methods. 

BERT model explained

BERT is a deep learning language model designed to improve the efficiency of natural language processing (NLP) tasks. It is famous for its ability to consider context by analyzing the relationships between words in a sentence bidirectionally. It was introduced by Google researchers in a 2018 paper titled “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” Since then, the BERT model has been fine-tuned for use in a variety of fields, including biology, data science, and medicine. 

Is BERT a large language model (LLM)?

LLM is a broad term describing large-scale language models designed for NLP tasks. BERT is an example of an LLM. GPT models are another notable example of LLMs. 


What is the BERT language model used for?

BERT is widely used in AI for language processing pre-training. For example, it can be used to discern context for better results in search queries. BERT outperforms many other architectures in a variety of token-level and sentence-level NLP tasks:

  • Token-level task examples. Tokens refer to labels that are assigned to specific and semantically meaningful groups of characters, like words. Examples of token-level tasks include part of speech (POS) tagging and named entity recognition (NER). 

  • Sentence-level task examples. Processing each token or word and discerning the context from surrounding words can be computationally exhausting for some NLP tasks. Examples of sentence-level tasks include semantic search and sentiment analysis.

BERT model applications

From industry to industry, BERT is being fine-tuned for specific needs. Here are a few examples of specialized BERT model architectures:

  • bioBERT: Used for biomedical text mining, bioBERT is a pre-trained biomedical language representation model. 

  • patentBERT: This BERT model version is used to perform patent classification.

  • VideoBERT: VideoBERT is a visual-linguistic model used to leverage the abundance of unlabeled data on platforms such as YouTube. 

How does BERT model work?

Two initial BERT model sizes were compared in Google’s 2018 paper: BERTlarge and BERTbase. BERTbase was made with the same model size as OpenAI’s GPT for performance comparison purposes. Both were trained on an enormous 3.3 billion word data set, including Wikipedia and Google’s BooksCorpus. This level of training can be time consuming, but 64 of Google’s custom-built tensor processing units (TPUs) managed to train BERTlarge in just four days. BERT’s pre-training methods differ from other language models (LMs) because they are bidirectional, meaning that data is processed forwards and backwards. 

Bidirectional pre-training helps the model better understand the relationships between words by analyzing both preceding and following words in a sentence. This type of bidirectional pre-training relies on masked language models (MLM). MLMs facilitate bidirectional learning by masking a word in a sentence and forcing BERT to infer what it is based on the context to the left and right of the hidden word. 

Bert model architecture

BERT stands for Bidirectional Encoder Representations from Transformers. We’ve already discussed how bidirectional pre-training with MLMs enables BERT to function, so let’s cover the remaining letters in the acronym to get a better understanding of its architecture.

Encoder Representations: Encoders are neural network components that translate input data into representations that are easier for machine learning algorithms to process. Once an encoder reads input text, it generates a hidden state vector. Hidden state vectors are like lists of values and internal parameters that provide additional context. This packaged representation of information is then passed on to the transformer.

Transformer: The transformer uses the information above to infer patterns or make predictions. A transformer is a deep learning architecture that transforms an input into another type of output. Nearly all NLP applications use transformers. If you’ve ever used Chat-GPT, you’ve seen transformer architecture in action. Typically, transformers consist of an encoder and a decoder. However, BERT uses only the encoder part of the transformer. 

How to train a BERT model

BERT is open-source and accessible via GitHub. According to Google, its users can train a sophisticated question and answer system within hours on a graphic processing unit (GPU) and minutes on a cloud tensor processing unit (TPU). 


Get hands-on BERT model practice with Coursera

You can build a foundational knowledge of core NLP concepts with the Natural Language Processing Specialization offered on Coursera by DeepLearning.AI. In as little as three months, you’ll learn to use encoder-decoder and self-attention mechanisms to machine translate complete sentences and build your own chatbot. 

If you’re ready for a shorter, more actionable project involving BERT, consider enrolling in this Guided Project you can complete in a few hours: Fine Tune BERT for Text Classification with TensorFlow.

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Jessica is a technical writer who specializes in computer science and information technology. Equipp...

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