Submissions will be evaluated based on **F1 score**, which are standard measures of prediction accuracy for classification and extraction problems.

The F1 score is a performance metric used to evaluate the accuracy of a binary or multi-class classification model. It is the harmonic mean of precision and recall, providing a balance between the two.

F1=2×Precision×RecallPrecision+Recall

Precision: The proportion of true positives out of all predicted positives.
Recall: The proportion of true positives out of all actual positives.
The F1 score ranges from 0 to 1, where 1 indicates perfect precision and recall.