12.04 · Walkthrough
Tokenization and Dataset Packing
Tokenize text, estimate token distributions, and pack examples into fixed-length sequences for efficient training.
Tokenization turns text into model IDs, but its practical impact is on length, cost, storage, truncation and batching. Measure real token distributions on your corpus, then pack tokenised examples into fixed-length sequences with correct boundaries and masks so training uses dense tensors without corrupting the data semantics.
What this lesson answers
- how to estimate token counts for an LLM dataset
- how does dataset packing reduce padding waste
- what can go wrong with packed training sequences
Notes
Tokenization is the step that turns raw text into the integer IDs a language model can process. For a working data engineer, the key point is that tokenization is not just a formatting detail: it determines sequence lengths, storage size, throughput, truncation behavior, and ultimately training cost. Different tokenizers split text differently, especially across code, whitespace, punctuation, multilingual text, and rare words, so you should always measure token counts on the actual corpus rather than relying on character or word counts.
Common questions
- Why measure tokens instead of using character or word counts?
- Language model tokenizers split text in ways that depend on punctuation, whitespace, code, rare terms and language. Word or character counts can be badly misleading for storage, context length and cost. Tokenising a representative sample gives the length distribution you actually need for filtering, chunking, truncation and training estimates.
- What is dataset packing for language model training?
- Dataset packing combines tokenised examples into fixed-length blocks instead of padding each example separately. Short records are concatenated, usually with boundary tokens, then sliced to the model’s target length. The result is denser batches, less wasted compute on padding, and more useful tokens processed by the hardware.
- How do I validate a tokenization and packing pipeline?
- Record the tokenizer version, special tokens, sequence length and packing rules, then inspect summary metrics such as total tokens, packed sequence counts, truncation and padding. Decode sampled packed sequences back to text to catch broken boundaries, repeated separators, Unicode corruption, incorrect masks and unintended loss across document boundaries.
Short definition: what is Tokenization and Dataset Packing?