Visual explanation
A language model starts from a sequence of text. Before tokenization or neural networks exist, the program must locate the dataset, decode its bytes correctly, and verify that it contains readable material.
A text file becomes the learning sequence
Follow the steps in order. Select any card to open its explanation and matching code.
input.txt
Long story short: The text file is the raw dataset.
Its absolute path makes loading reliable from any terminal folder. The visual highlights input.txt at this point. This prepares the next step: read_text().
Code
Load a plain-text dataset and inspect the exact characters the model will learn from.
PROJECT_DIR = Path(__file__).resolve().parents[2]
DATASET_PATH = PROJECT_DIR / "data" / "study_sample.txt"
text = DATASET_PATH.read_text(encoding="utf-8")
print("Number of characters:", len(text))
print(text[:500]) From study/lessons/01_read_text.pyCode syntax and logic
- Step 01 Code reference
PROJECT_DIR = Path(__file__).resolve().parents[2]Explanationrepresents the current script; makes its path absolute, and reaches the repository root without depending on the terminal's current directory.
- Step 02 Code reference
DATASET_PATH = PROJECT_DIR / "data" / "study_sample.txt"ExplanationThe
/operator onPathobjects joins path components portably on macOS, Linux, and Windows. - Step 03 Code reference
text = DATASET_PATH.read_text(encoding="utf-8")Explanationopens, decodes, reads, and closes the file. The explicit encoding makes the byte-to-character conversion deterministic.
- Step 04 Code reference
print("Number of characters:", len(text))Explanationcounts Python Unicode characters rather than encoded UTF-8 bytes.
- Step 05 Code reference
print(text[:500])Explanationis a non-mutating slice from index
0up to, but not including, index500; it provides a short inspection sample without changingtext.