Text Classification

Assign one of a fixed set of labels to a piece of text — the simplest data labeling primitive. Input is a string; output is a label from a closed set.

Files

• basic.py — text → single label.
• with_confidence.py — adds self-reported confidence per prediction. Use when you need to route low-confidence cases to a human or a stronger model.
• with_rationale.py — adds a short rationale string explaining why this label was chosen. Useful for auditability and as training data.

When to use

When the output is one of a fixed, exhaustive set of labels:

• Sentiment: positive / negative / neutral
• Intent: refund / complaint / question / praise
• Topic: sports / politics / tech / health
• Quality bucket: good / mediocre / poor

If multiple labels can apply at once, use _02_text_multilabel_classification/. If the output is structured (entities, fields), use _03_text_extraction/.

Run

python cookbook/data_labeling/_01_text_classification/basic.py
python cookbook/data_labeling/_01_text_classification/with_confidence.py
python cookbook/data_labeling/_01_text_classification/with_rationale.py

Requires OPENAI_API_KEY.