his is confirmed in the UiPath documentation, where the "Train" feature guides users from the

dataset creation stage through the necessary steps to optimize the training process. The system

recommends next best actions based on progress to ensure efficient and focused training🔍

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.

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

To build custom models supported by AI Center, you can use a Python IDE or an AutoML platform of

your choice. A Python IDE is a software application that provides tools and features for writing,

editing, debugging, and running Python code. An AutoML platform is a service that automates the

process of building and deploying machine learning models, such as data preprocessing, feature

engineering, model selection, hyperparameter tuning, and model evaluation. Some examples of

Python IDEs are PyCharm, Visual Studio Code, and Jupyter Notebook. Some examples of AutoML

platforms are Google Cloud AutoML, Microsoft Azure Machine Learning, and DataRobot.

To use a Python IDE, you need to install the required Python packages and dependencies, write the

code for your model, and test it locally. Then, you need to package your model as a zip file that

follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI

Center and create an ML Skill to deploy and consume your model.

To use an AutoML platform, you need to sign up for the service, upload your data, configure your

model settings, and train your model. Then, you need to export your model as a zip file that follows

the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center

and create an ML Skill to deploy and consume your model.

Reference:🔍AI Center - Building ML Packages,🔍AI Center - ML Package Structure,🔍AI Center - Creating

ML Skills

Recall is a metric that measures the proportion of all possible true positives that the model was able

to identify for a given concept1. A true positive is a case where the model correctly predicts the

presence of a concept in the data. Recall is calculated as the ratio of true positives to the sum of true

positives and false negatives, where a false negative is a case where the model fails to predict the

presence of a concept in the data.🔍Recall can be interpreted as the sensitivity or completeness of the

model for a given concept2. For example, if there are 100 verbatims that should have been labelled

as ‘Request for information’, and the model detects 80 of them, then the recall for this concept is

80% (80 / (80 + 20)). A high recall means that the model is good at finding all the relevant cases for a

concept, while a low recall means that the model misses many of them.

Reference:🔍1:🔍Recall🔍2:🔍Precision and Recall

https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/UIPATH_UIPATH-SAIAV1/page_69_img_2.jpg

to load a taxonomy from a given non-default location text file into a variable, the order of steps

should be as follows:

Create a new variable of type 'DocumentTaxonomy' called 'documentTaxonomy', then create a new

string variable called 'taxonomyPath'.

This step involves setting up the necessary variables that will be used in the process. The

'documentTaxonomy' variable will hold the deserialized taxonomy object, and 'taxonomyPath' will

store the path to the taxonomy file.

Using an Assign activity, assign the given non-default taxonomy text file path to the most recent

variable created.

Here you will assign the path of the taxonomy file to the 'taxonomyPath' variable.

Use the Read Text File activity on the 'taxonomyPath' variable created. Next, create a new variable

'taxonomyText', then save the output of the Read Text File activity to 'taxonomyText'.

This step is where you read the contents of the taxonomy file using the 'Read Text File' activity. The

contents are stored in the 'taxonomyText' variable.

Inside the last Assign activity 'Value to Save' property add the following expression:

"Newtonsoft.Json.JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText)" where

'taxonomyText' is the text read from the file and 'documentTaxonomy' (the most recent variable

created) is the variable you created.

In this final step, you will deserialize the JSON content from the 'taxonomyText' into a

'DocumentTaxonomy' object using the 'JsonConvert.DeserializeObject' method and assign it to the

'documentTaxonomy' variable.

Following these steps in this order will load the taxonomy from a text file into the

'documentTaxonomy' variable in UiPath.

Comprehensive and Detailed Explanation From Exact Extract:

To evaluate this:

Let’s assume:

doubleX = 10.5

intX = 10

stringX = "18"

Then:

CDbl(intX) → 10.0

CDbl(stringX) → 18.0

Sum = 10.5 + 10 + 18 = 38.5

CInt(38.5) > 38.30 → 38 > 38.30 → True

Result of If → 1

CDbl converts string/numeric values to Double

CInt converts Double to Integer (rounding behavior is floor if .5 and below)

UiPath Reference:

Data Type Conversion - Microsoft VB.NET

Model-based data extraction, often involving machine learning models, is particularly effective for

handling complex data structures such as variable-length data or data that spans multiple pages. This

approach adapts better to varying formats and can extract information more accurately in such

scenarios compared to rule-based or manual methods.

Comprehensive and Detailed Explanation From Exact Extract:

The "Step Out" option in UiPath Studio’s Debugging toolbar, as shown in the image, is used when

debugging a process and you have stepped inside a function, invoked workflow, or nested container.

If you want to exit from the current container (e.g., a workflow or sequence) and return to the caller

or parent scope, you use Step Out.

It executes all remaining activities within the current container, and once complete, pauses the

execution back at the point where that container was invoked.

It does not stop execution, nor does it re-execute exceptions or pause after every activity (like Step

Into or Step Over).

🔍Visual Confirmation from the Image:

The "Step Out" button is highlighted in red, indicating it’s active and available during debugging.

It is grouped alongside "Step Into" and "Step Over," all part of debug control options.

🔍Use Case:

Suppose you're debugging a workflow and step into an invoked file or a "Then" branch. If everything

looks fine, you can use Step Out to quickly exit and return control to the parent workflow without

stepping through every line.

UiPath Documentation Reference:

Debugging in Studio – UiPath Docs

In UiPath's Document Understanding Framework, UiPath Document OCR is recommended as the

initial OCR engine for processing documents. This OCR option is optimized for extracting text from a

wide variety of document types and provides good accuracy with structured, semi-structured, and

unstructured documents. It is designed to work seamlessly within the UiPath Document

Understanding process and is highly compatible with other UiPath activities and models, making it a

preferred choice for starting document processing in most projects.

For more details, refer to:

UiPath Document Understanding Framework: UiPath OCR Engines

UiPath Document OCR: UiPath OCR Guide

Reference: UiPath Integration Service Triggers