Transformer architecture

As explained in Hugging Face in Action

The Transformer architecture is a neural-network architecture based on attention mechanisms for processing sequences and other structured inputs.

from transformers import pipeline

classifier = pipeline('text-classification',
             model = 'distilbert-base-uncased-finetuned-sst-2-english',
             revision = 'af0f99b')

How Hugging Face in Action covers it

How deep it goes

Explains self-attention, embeddings, positions, and encoder-decoder roles briefly, while stopping short of detailed Transformer internals.

Examples

Demonstrates IMDb sentiment analysis with a pretrained DistilBERT model and Hugging Face’s pipeline API.

What you can do after

Apply it via Hugging Face pipelines to run pretrained models for classification and other NLP workflows.

How it connects

Connects self-attention, token embeddings, positional encodings, encoder-decoder models, recurrent neural networks, and NLP tasks.

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Also explained in

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  Learn Generative AI with PyTorchThe internal design and structural organization of a Transformer model built around decoder blocks or self-attention components.
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  Natural Language Processing in Action, Second EditionA transformer is a neural network architecture that uses attention mechanisms to process sequences and model relationships among their elements.
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  Sutskever's ListA Transformer is a neural network architecture based primarily on attention mechanisms for modeling relationships among sequence elements.
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  Deep Learning with Python, Third EditionA neural-network architecture based on attention mechanisms, used here as the core architecture for large language models.
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  Build a Text-to-Image Generator (from Scratch)A transformer architecture is a neural-network architecture that uses attention mechanisms to process relationships among elements in a sequence or other structured input.
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  Deep Learning with R, Third EditionA Transformer is a neural-network architecture designed to process sets or sequences using attention-based contextual representations.
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  How Large Language Models WorkA transformer is a neural-network architecture that uses attention mechanisms to process relationships among elements in a sequence.
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  AI-Powered SearchA transformer is a neural-network architecture that models relationships among sequence elements using attention and can encode and decode representations.
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  Transformers in ActionA transformer is a neural network architecture that processes sequential and other structured data using attention mechanisms.
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  Deep Learning with PyTorch, Second EditionA neural network architecture for sequence modeling and content understanding based on attention rather than recurrence or convolution.
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  LLMs in ProductionA transformer architecture is a neural-network architecture that models sequence relationships primarily through attention rather than recurrence or convolution.
• 

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  Grokking AI ApplicationsA transformer is a neural-network architecture for sequential data that uses attention to relate elements of an input sequence.
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  The AI Pocket BookA neural network architecture that contextualizes each token using surrounding tokens rather than compressing the whole prompt into one summary vector.
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  Time Series Forecasting Using Foundation ModelsA neural-network architecture built from stacked attention and feed-forward processing components, typically organized into encoder and decoder parts.
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  Machine Learning Algorithms in DepthA transformer model is a neural network architecture that uses attention-based mechanisms to model contextual relationships in structured sequences or other data.
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  Inside Deep Learning: Math, Algorithms, ModelsA sequence model built from positional encoding and multiheaded attention, used here as the standard original design for NLP and other tasks.
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  Grokking AI Algorithms, Second EditionThe Transformer is a neural-network architecture based primarily on attention mechanisms for processing sequences and modeling context.
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  Domain-Specific Small Language ModelsA neural network architecture for sequence modeling built around self-attention and parallel processing, underlying both large and small language models.
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  Introduction to Generative AI, Second EditionA transformer is a neural-network architecture for sequence modeling that uses attention, especially self-attention, rather than recurrence.
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  Build AI-Enhanced Web AppsA transformer model is a neural-network architecture that uses attention mechanisms to model contextual relationships in sequence data.
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  Build a Large Language Model (From Scratch)a neural network architecture based on self-attention mechanisms that enables processing of sequential data and underpins many large language models.
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  Build a Website with ChatGPTA transformer is a neural-network architecture that processes sequences using attention mechanisms to model relationships among their components.
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  Data Analysis with LLMsThe Transformer is a neural-network architecture designed to model relationships in sequences, especially sequences with variable-length inputs or outputs.
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  Generative AI in ActionThe Transformer architecture is a neural-network architecture based primarily on attention mechanisms for processing sequences.
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  Coding with AIA Transformer is a neural-network architecture that processes sequence elements using attention and can process them in parallel.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsA transformer architecture is a neural-network architecture that processes sequences primarily through attention mechanisms rather than recurrence.
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  Essential GraphRAGA transformer architecture is a neural-network architecture that processes sequences using attention-based representations and supports efficient language modeling.

Compare how 28 books explain transformer architecture →

Related concepts

• attentionuses · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
• encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
• decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
• Vision Transformerincludes · A Vision Transformer is a Transformer-based neural-network architecture that processes images as sequences of tokenized regions using attention.
• model architecturekind of · The structural organization of a computational system’s components, relationships, and operations that determines how it processes information or behaves.
• GPT-2includes · GPT-2 is a family of autoregressive, decoder-only Transformer language models developed by OpenAI that generate text by predicting successive tokens from preceding context.

This explainer was generated by Manning's knowledge graph from the text of Hugging Face in Action. The section links point to the parts of the book it draws on.