Padding token
A padding token is a special placeholder inserted into shorter sequences to give batched sequences a uniform length without representing meaningful content.
Natural Language Processing in Action, Second Edition
A padding token is a special token used to extend shorter sequences to a common length within a batch.
- How it is used
- Fills short sequences to a fixed input length for text CNNs and transformers.
- How deep it goes
- Shows marker selection, clipping, placement, a Python padding function, and transformer masking and loss handling.
- Examples
- Uses a Python clipping-and-padding function, news-post classification data, and a PyTorch translation model.
- What you can do after
- Build it from scratch with a Python padding function, then configure padding IDs and masks in a PyTorch transformer.
Read how Natural Language Processing in Action, Second Edition explains padding token →
Build a Text-to-Image Generator (from Scratch)
A padding token is a special marker appended to shorter sequences so that sequences in a batch have equal length.
- How it is used
- Supports fixed-shape batching for transformer training and inference by extending shorter token sequences.
- How deep it goes
- Explains vocabulary IDs, batch padding, attention masks, and fixed-length tensors, but does not derive padding internals.
- Examples
- Demonstrates padded German-English sequences, caption batches, DistilBERT outputs, and a manually padded 64-token tensor.
- What you can do after
- Apply it via tokenizers or PyTorch tensors, and mask padded positions during transformer processing.
Read how Build a Text-to-Image Generator (from Scratch) explains padding token →
Deep Learning with R, Third Edition
A padding token is a placeholder token inserted to make sequences conform to a common length and carries no sequence content.
- How it is used
- Standardizes variable-length token sequences for batched embedding, recurrent modeling, and Transformer fine-tuning.
- How deep it goes
- Explains placeholder placement, fixed-length packing, truncation, masking, and framework configuration without detailing mask propagation internals.
- Examples
- Demonstrates padded movie-review sequences, RoBERTa start-end packing, and batched token IDs paired with Boolean padding masks.
- What you can do after
- Apply it via KerasHub’s StartEndPacker or Keras Embedding masking when preparing variable-length text for neural models.
Read how Deep Learning with R, Third Edition explains padding token →
Hugging Face in Action
A padding token is a special token inserted into shorter sequences so batched sequences have a uniform length.
- How it is used
- Standardizes tokenized sequence lengths for efficient transformer batching during training or inference.
- How deep it goes
- Explains insertion, visible token IDs, converted [PAD] tokens, and attention-mask indicators without descending into model internals.
- Examples
- Demonstrates trailing zero input IDs, converted [PAD] tokens, and matching zero values in an attention mask.
- What you can do after
- Apply padding through Hugging Face AutoTokenizer and inspect its input IDs, tokens, and attention mask.
Related concepts
- tokenkind of · A token is a discrete unit of text, such as a word, word fragment, character, or symbol, represented and processed as an element by a language model.
- batchingconfigures · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
- batch sizepart of · Batch size is the number of items grouped together for processing or computation as a single batch.
- attention_maskcontrasts with · An attention mask is a tensor or indicator structure specifying which sequence positions may participate in attention or other model computations, often excluding padding.
- Vectorization layerconfigured by · A Keras preprocessing layer that tokenizes text and converts it into numerical representations using a vocabulary and configurable encoding.
- paddingpart of · Padding is the addition of extra space, values, or data around or within a structure to satisfy size, alignment, layout, or processing requirements.
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