Mesure

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

Mesure du projet

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.

La notation de modèle est une fonctionnalité destinée à vous aider à visualiser les performances d’un modèle de classification. Elle est exprimée sous la forme d’un score de modèle compris entre 0 et 100 :

• Faible (0-49)
• Moyen (50-69)
• Bon (70-89)
• Excellent (90-100)

Quel que soit le score du modèle, c’est à vous de décider quand arrêter l’entraînement, en fonction des besoins de votre projet. Même lorsqu’un modèle est classé comme Excellent, cela ne signifie pas qu’il répondra à toutes les exigences métier.

Mesure de classification

Le score de classification tient compte des performances du modèle ainsi que de la taille et de la qualité de l'ensemble de données.

Remarque :

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

If you select 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.

Mesure des extractions

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 selecting Annotate.

If you select 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. You can also access advanced metrics for your extraction models using the Download advanced metrics button. This feature allows you to download an Excel file with detailed metrics and model results per batch.

Diagnostic du jeu de données

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

Il existe trois niveaux d’état de l’ensemble de données affichés dans la barre de gestion :

• Red - More labelled training data is required.
• Orange - More labelled training data is recommended.
• Green - The needed level of labelled training data is achieved.

Si aucun champ n’est créé dans la session, le niveau d’état de l’ensemble de données est grisé.