Chapter 2
Text as Data
Corpora, documents, sentences, normalization, punctuation, splits, and leakage in language tasks.
What this chapter does
Raw text becomes a dataset only after choices about boundaries, cleanup, splitting, and leakage are made visible.
Lessons
Read these in order.
Start with the chapter introduction, then move through the topic lessons. The order is chosen so each page can reuse ideas from the pages before it.
- 01Introduction
Why text preparation is part of the modeling problem, not clerical cleanup.
- 02Corpus, Document, and Sentence
The basic units used to turn raw text into a language-modeling dataset.
- 03Normalization, Whitespace, and Punctuation
Small cleanup choices that can change token counts and model behavior.
- 04Character Versus Word Views
Two simple ways to read text as a sequence, and what each view makes easy or hard.
- 05Train, Validation, and Test Text
Splitting text so evaluation measures prediction instead of memorized future evidence.
- 06Leakage in Language Tasks
How repeated text, future context, and preprocessing mistakes can make a model look better than it is.
Review and practice
Close the chapter deliberately.
Use the conclusion and revision notes before the chapter exercises. Hints and solutions are collected here, while lesson-level exercises reveal their own help inline.
What text-as-data choices establish before Unicode and tokenization.
Summary and Revision NotesA compact review of corpora, boundaries, normalization, splits, and leakage.
ExercisesChapter-level practice for inspecting text, choosing units, and avoiding leakage.
HintsLow-spoiler nudges for the Chapter 2 exercises.
SolutionsExplained solutions for the Chapter 2 exercises.
Before moving on
- Build and inspect a tiny corpus.
- Explain why preparation choices change the task.
- Split text without leaking future evidence.
Where this leads
- Unicode and Text Normalization
- Tokens and Vocabularies