Mean Average Precision (MaP) measures how accurately the model detects field regions (higher is better). This model scored 70.2% on its validation set. MaP is harder to compare across models than CER, because the score depends heavily on how many distinct region types the model must distinguish. A model detecting a handful of simple fields will naturally score higher than one trained to recognise many fine-grained regions, even if both perform well in practice.
This score reflects performance on the model's own validation data. Your results will depend on how closely your documents match the training material and the complexity of the structures you need to detect.