### IR Recall Recall, also known as sensitivity, measures the model's ability to detect all relevant instances in the dataset. For content extraction, it reflects how much of the actual important content the model was able to extract. High recall means the model missed very little of the relevant content, minimizing false negatives (relevant content not extracted). **Formula:** Recall=True PositivesTrue Positives+False Negatives\text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}Recall=True Positives+False NegativesTrue Positives​