Measure

You can check the overall status of your project and check the areas with improvement potential from the Measure section.

Project Measure

The main measurement on the page is the overall Project score.

This measurement factors in the classifier and extractor scores for all document types. The score of each factor corresponds to the model rating and can be viewed in Classification Measure and Extraction Measure respectively.

The model rating is a functionality intended to help you visualize the performance of a classification model. It is expressed as a model score from 0 to 100 as follows:

Poor (0-49)
Average (50-69)
Good (70-89)
Excellent (90-100)

Regardless of the model score, it is up to you to decide when to stop training, depending on your project needs. Even if a model is rated as Excellent, that doesn't mean that it will meet all business requirements.

Classification Measure

The Classification score factors in the performance of the model as well as the size and quality of the dataset.

Note: The Classification score is only available if you have more than one document type created.

If you click on Classification, two tabs are displayed on the right side:

Factors: Provides recommendations on how to improve the performance of your model. You can get recommendations on dataset size or trained model performance for each document type.
Metrics: Provides useful metrics, such as the number of train and test documents, precision, accuracy, recall, and F1 score for each document type.

Extraction Measure

The Extraction score factors in the overall performance of the model as well as the size and quality of the dataset. This view is split into document types. You can also go straight to the Annotate view of each document type by clicking Annotate.

If you click on any of the available document types from the Extraction view, three tabs are displayed on the right side:

Factors: Provides recommendations on how to improve the performance of your model. You can get recommendations on dataset size (number of uploaded documents, number of annotated documents) or trained model performance (fields accuracy) for the selected document type.
Dataset: Provides information about the documents used for training the model, the total number of imported pages, and the total number of labelled pages.
Metrics: Provides useful information and metrics, such as the field name, the number of training status, and accuracy for the selected document type. The accuracy is obtained from the training pipeline.

Dataset Diagnostics

The Dataset tab helps you build effective datasets by providing feedback and recommendations of the steps needed to acchieve good accuracy for the trained model.