BERT

As explained in Build a Large Language Model (From Scratch)

A transformer-based model built on the encoder stack and trained for bidirectional masked-word prediction.

BERT (bidirectional encoder representations from transformers) is a transformer-based model built on the encoder stack. It predicts masked or hidden words using bidirectional context, making it especially useful for text classification. section 1.4

BERT differs from GPT in architecture, training objective, and typical applications:

• Uses the transformer encoder rather than the decoder. section 1.4
• Learns through masked-word prediction rather than text completion. section 1.4
• Supports tasks such as sentiment prediction, document categorization, and toxic-content detection. section 1.4

Figure 1.5 A visual representation of the transformer’s encoder and decoder submodules. On the left, the encoder segment exemplifies BERT-like LLMs, which focus on masked word prediction and are primarily used for tasks like text classification. On the right, the decoder segment showcases GPT-like LLMs, designed for generative tasks and producing coherent text sequences.

Compare the encoder segment associated with BERT-like models with the decoder segment associated with GPT-like generative models. section 1.4

How Build a Large Language Model (From Scratch) covers it

How deep it goes

Defines BERT’s encoder basis and training objective, while deferring self-attention implementation and input preprocessing details.

Examples

Uses masked sentences, sentiment prediction, document categorization, and toxic-content detection as concrete examples.

What it assumes

Assumes familiarity with transformer encoder-decoder structure while postponing detailed self-attention and input preprocessing.

What you can do after

Apply it via text classification for sentiment, document categorization, or toxic-content detection.

How it connects

Connects BERT to transformer architecture, encoder-decoder models, self-attention, GPT, and text classification.

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Also explained in

• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionBERT is a transformer language model pretrained with bidirectional contextual objectives for representing and understanding text.
• 

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  LLMs in ProductionA transformer-based language representation model that uses bidirectional context to learn text embeddings.
• 

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  AI-Powered SearchBERT is a transformer-based language model pretrained to produce contextual representations of text using bidirectional attention.
• 

  liveBookmanning.com
  Machine Learning System DesignBERT is a pretrained bidirectional transformer language model that produces contextual representations of text.
• 

  liveBookmanning.com
  Domain-Specific Small Language ModelsA transformer-based language model that uses the encoder stack for understanding tasks such as classification.
• 

  liveBookmanning.com
  Machine Learning Algorithms in DepthBERT is a pretrained bidirectional transformer language model designed to learn contextual representations of text.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA pretrained transformer-based language model used for language understanding and transfer learning.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA pretrained bidirectional Transformer encoder model for language understanding, used here as an NLP example rather than a generation model.

Compare how 9 books explain BERT →

Related concepts

• encoder-only transformerkind of · An encoder-only Transformer is a Transformer model composed of encoder layers that process an input sequence into contextual representations without a separate decoder.
• bidirectional language modelkind of · A bidirectional language model represents or predicts tokens using contextual information from both preceding and following tokens in a sequence.
• transformer architecturekind of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• masked language modelinguses · Masked language modeling is a self-supervised training objective in which a model predicts tokens hidden or replaced in an input sequence using surrounding context.
• DistilBERTincludes · DistilBERT is a smaller, faster Transformer language model distilled from BERT to retain much of its language-understanding capability with fewer parameters and lower resource requirements.

This explainer was generated by Manning's knowledge graph from the text of Build a Large Language Model (From Scratch). The section links point to the parts of the book it draws on.