Encoder

As explained in Natural Language Processing in Action, Second Edition

An encoder is the transformer component that transforms an input sequence into contextual representations through stacked attention and feedforward layers.

An encoder transforms a token sequence into contextual representations by passing embeddings through a stack of reusable transformer layers. section 9.2.2

The input receives positional encoding before entering the encoder. Each encoder layer combines multi-head attention with a positionwise fully connected feedforward network, while linear projections and layer normalization help organize the outputs. section 9.2.2

Its place in transformer designs is easiest to see through these contrasts:

• The original Transformer used six encoder layers alongside six decoder layers. section 9.2.2
• Decoder layers share the encoder’s attention, normalization, and feedforward structure but add mechanisms for generated-token input. section 9.2.2
• Later GPT architectures removed encoder layers and instead stacked many decoder layers. section 9.2.2

BERT as a bidirectional encoder

BERT stacks encoder transformers and trains them with masked language modeling: randomly masked tokens are predicted from both left and right context. Fine-tuning then adapts the resulting representations to sentence-level and token-level tasks. section 9.3

Figure 9.5 Transformer architecture

Use the transformer architecture diagram to trace positional encoding, encoder layers, decoder layers, normalization, and output-token flow. section 9.2.2

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Defines stacked attention and feedforward layers with linear projections and normalization, but stops at architectural description.

Examples

Uses the original six-layer Transformer and BERT’s bidirectional masked-token prediction as concrete architectural examples.

What it assumes

Assumes familiarity with BPE, embeddings, positional encoding, and self-attention before assembling them into transformer blocks.

What you can do after

Evaluate encoder placement, layer composition, and its contrast with decoder-only GPT architectures.

How it connects

Links token embeddings, positional encoding, multi-head attention, feedforward networks, layer normalization, decoders, and BERT.

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Related concepts

• transformer architecturepart of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• autoencoderpart of · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
• variational autoencoderpart of · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• CLIPpart of · CLIP is a multimodal model and pretraining method that aligns image and text representations in a shared embedding space.
• U-Netpart of · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.