Prepare data by labeling key fields, select a prebuilt model, train using a small dataset, evaluate using accuracy and precision metrics.

Prepare data by segmenting documents, select a custom model template, train using a large dataset, evaluate using F1 score and recall metrics.

Prepare data by normalizing formats, select a generic model, train using a balanced dataset, evaluate using mean absolute error and confusion matrix.

Prepare data by annotating fields, select a custom model, train using a diverse dataset, evaluate using accuracy and ROC curve.