**Measuring System Performance in Question-Answering Tasks Using F1-Score**

F1-score is a widely used metric for evaluating the performance of a system in question-answering tasks. This metric assesses how well the system's answers match the correct ones. It combines two important aspects: precision and recall. Precision measures the accuracy of the system's answers, while recall evaluates how many correct answers the system can identify. By combining these two, F1-score provides a comprehensive evaluation of the system's ability to deliver accurate and complete answers.

The formulas for Precision, Recall, and F1 Score are as follows:

$$
\textbf{Precision} = \frac{\text{Count of tokens predicted exactly}}{\text{Count of tokens predicted}}
$$

$$
\textbf{Recall} = \frac{\text{Count of tokens predicted exactly}}{\text{Count of gold standard tokens}}
$$

$$
\textbf{F1-score} = \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}
$$

The purpose of these metrics is to evaluate how good a system is at providing accurate and complete answers.