UiPath UiPath-SAIv1 Dumps Questions Answers

The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:

Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.

Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.

Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.

Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported.

Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.

References: AI Center - Managing Data Labels, Data Labeling for Text - Public Preview

According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data. However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12. A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.

References: 1: Document Classification Training Overview 2: Document Classification Training Related Activities 3: Training High Performing Models