Lesson 20 of 38 · Written learning
Build a tokenization and data pipeline
Turn prepared documents into repeatable sequences that a training loop can consume.
A typical pipeline reads documents, validates records, tokenizes text, groups tokens into sequences, and forms batches. Store document boundaries or metadata when they matter. If unrelated documents are concatenated without separation, the model may learn accidental transitions between them.
Make the pipeline observable
Inspect samples at each step: raw text, cleaned text, tokens, and final sequence. Measure document lengths, empty records, token counts, and duplicate rates. Larger data may be streamed rather than loaded entirely into memory. Shuffling can improve training variety, but keep deterministic seeds and document ordering where reproducibility matters.
- Version the tokenizer alongside the model that uses it.
- Do not silently truncate text; record how much content was dropped.
- Keep a held-out validation set separate from training data.
- Record the parameters used for chunking and sequence length.
Return to the Data engineering and pretraining stage · Suggested timing is flexible; move at your own pace.