7.5 Text classification end to end
You have a shipped text classifier with an honest evaluation.
Before:06. Deep LearningUnlocks:08. Large Language Models
This is the full text-classification loop shipped honestly: sentiment, topic or intent, imbalance handled, and error analysis — where the real learning happens — done by actually reading the model's mistakes. It composes everything earlier in the module. The ceiling to respect is annotation noise: labels are made by people who disagree, and a model cannot be more right than its labels, so some of what looks like model error is the dataset arguing with itself.
Work through these
Sentiment, topic and intent classification
Three common classification tasks that differ in what the label means and how stable it is over time. Intent classification in particular ages as users change how they write.
Handling class imbalance in text
Rare classes in text have the same problems as elsewhere, with the additional difficulty that examples are expensive to label. The techniques carry over with that constraint attached.
Error analysis on text
Reading the actual mistakes a classifier makes, grouped by kind, is what tells you what to fix. Aggregate scores cannot do this.
Confusion between annotation noise and model error
Some apparent model errors are disagreements about what the correct label was. Separating the two decides whether to improve the model or the annotation guidelines.
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