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Mining, it is recommended that users provide a minimum of 25 pinned examples per label to ensure proper training and accurate predictions by the machine learning models. This number allows the platform to have a sufficient variety of examples to generalize and make reliable predictions for each label in real-world scenarios.
The minimum number of pinned examples per label is crucial because it enhances both precision and recall, helping the model effectively differentiate between labels and improving overall model performance. If fewer examples are provided, the model may struggle with generalization and might not perform well in distinguishing between similar or overlapping categories.
This standard of 25 pinned examples is outlined in several UiPath documentation sections and best practices for training models in Communications Mining UiPath Documentation UiPath Documentation UiPath Community Forum For further details, refer to UiPath’s official Communications Mining User Guide on their documentation portal.

The “Wait for Classification Validation Task and Resume” activity in UiPath’s Document Understanding Framework is primarily used to halt or suspend the workflow until a specified document classification validation task is completed by a human. This activity is part of the broader workflow to ensure that when automatic classification of documents cannot be confidently achieved, a human-in-the-loop (HITL) approach is followed to validate or correct classifications. Once the validation is performed in UiPath’s Action Center by a human, the workflow is resumed, ensuring the proper handling of documents that require review and correction.
This is aligned with the design of the Action Center, which is integrated into UiPath’s Document Understanding Framework. When dealing with document classification or extraction confidence issues, manual human validation tasks are often required, which is what this activity manages. It facilitates human oversight, preventing the automation from proceeding with potentially incorrect classifications.
Reference from UiPath documentation:
* UiPath Action Center explains how humans are involved in validation tasks to handle cases where classification or extraction needs manual review.
* Wait for Task and Resume Activity in UiPath Documentation explains how it waits for a task (such as document validation) to be completed in the Action Center before resuming the workflow.
For more details, you can consult the official UiPath documents:
* UiPath Document Understanding Framework
* Wait for Classification Validation Task and Resume
This functionality ensures that incorrect data processing due to automation can be caught and rectified by a human, improving accuracy in document handling workflows.

In UiPath AI Center, the platform supports several types of machine learning (ML) models, including:
* Out-of-the-box models from UiPath: Pre-built models designed for common automation tasks.
* Models from UiPath technology partners: External models developed by UiPath’s partners.
* Open-source models: Community-contributed models that can be used and adapted for various use cases.
* Custom models: Models that users build and train specifically for their projects using their datasets.
This flexibility in model support ensures that organizations can leverage a wide range of machine learning capabilities to suit different automation needs.
For more details, refer to:
* UiPath AI Center Documentation: AI Center Models
* Machine Learning Model Types: Types of Models in UiPath AI Center

Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term “deep” refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output.
Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.
References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning – Wikipedia

In UiPath Document Manager, for string fields, the available options for scoring include:
* Exact match: This checks whether the extracted string exactly matches the expected value.
* Levenshtein: This method computes the “distance” between two strings, which is the number of single- character edits (insertions, deletions, or substitutions) required to change one string into the other. It is used to assess the similarity between the extracted string and the expected value.
These scoring methods help evaluate the accuracy of string extraction in the document understanding process.
For more details, refer to:
* UiPath Document Understanding Framework: String Field Scoring
* Levenshtein Distance in UiPath: Scoring String Fields

To configure the Validation Station as an attended activity in UiPath, the minimum required inputs include the Taxonomy, which defines the structure and fields for data extraction, the Document Path, the Document Object Model (DOM), the Document Text obtained during digitization, and the Automatic Extraction Results, which are the results from automatic data extraction activities that need validation. These inputs allow the Validation Station to properly display and validate extracted data

The ML Classifier is a document classifier that uses a machine learning model deployed as an ML Skill in AI Center to perform document classification tasks. The ML Classifier can work by default with Invoices, Purchase Orders, Receipts, and Utility Bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.
According to the best practice, the ML Classifier is the only recommended classifier to be used when the custom document types are very similar and file splitting is not necessary. This is because the ML Classifier can handle complex and ambiguous cases where the document types are hard to distinguish by rules or keywords, and can also learn from feedback and improve over time. File splitting is not necessary when the documents are single-page or have a consistent number of pages per document type3.
The other options are not correct because they are scenarios where other classifiers, such as the Keyword Based Classifier or the Intelligent Keyword Classifier, can be used in combination with the ML Classifier or instead of it. These classifiers are based on rules or keywords that can identify the document types based on their content or metadata, and can also perform file splitting if the documents are multi-page or have a variable number of pages per document type3.
References: 1: Machine Learning Classifier – UiPath Activities 2: Machine Learning Classifier Trainer – UiPath Document Understanding 3: Document Classification – UiPath Document Understanding

A high-level view of the tabs within an AI Center project is as follows:
* Dashboard: This tab provides an overview of the project’s status, such as the number of datasets, pipelines, packages, skills, and logs, as well as the AI Units consumption and quota.
* Datasets: This tab enables you to upload, view, and manage the datasets that are used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub-folders1.
* Data Labeling: This tab enables you to upload raw data, annotate text data in the labeling tool (for classification or entity recognition), and use the labeled data to train ML models. It is also used by the human reviewer to re-label incorrect predictions as part of the feedback process2.
* ML Packages: This tab enables you to upload, view, and manage the ML packages and package versions within the project. An ML package is a group of package versions of the same package type, and a package version is a trained model that can be deployed to a skill3.
* Pipelines: This tab enables you to create, view, and manage the pipelines and pipeline runs within the project. A pipeline is a description of an ML workflow, including the functions and their order of execution, and a pipeline run is an execution of a pipeline based on code provided by the user4.
* ML Skills: This tab enables you to deploy, view, and manage the ML skills within the project. An ML skill is a live deployment of a package version, which can be consumed by an RPA workflow using an ML skill activity in UiPath Studio5.
* ML Logs: This tab enables you to view and filter the logs related to the project, such as the events, messages, and errors that occurred during the pipeline runs, skill deployments, and skill executions6.
References:
1: About Datasets 2: About Data Labeling 3: About ML Packages 4: About Pipelines 5: About ML Skills 6: About ML Logs

According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:
* Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as- is for inference or prediction. They do not require any additional data or training from the user.
Examples of pre-trained models are Language Detection, Sentiment Analysis, and Question Answering.
* Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.
* Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine-tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre-trained model and the flexibility of customizing it to the user’s needs. Examples of fine- tunable models are Object Detection, Text Summarization, and Language Translation.
References:
1: Out-of-the-Box Packages

In UiPath Document Understanding, the Digitized Object Model (DOM) is an XML structure that contains detailed information about the document being processed. This includes data such as the content of each page, the coordinates of each element within the document, and additional metadata. The DOM is created during the digitization process, where scanned or image-based documents are converted into machine-readable formats for further processing, such as classification and data extraction.
For more details, refer to:
* UiPath Document Understanding Documentation: Digitization and the DOM
* Digitize Document Activity: Working with DOM

Explanation:

* Export the package from AI Center. This is the first step where you prepare the package to be moved.
* On the ML Packages page, click the Import ML Package button. This step is where you start the process of importing the package you’ve exported.
* On the Upload package field, add the zip file downloaded using the Downloading ML Packages procedure. After starting the import process, you will upload the actual package.
* Click Create. This is the final step where you finalize the uploading process of your ML package.
Please proceed with these steps in the UiPath AI Center to upload your exported package correctly.

To enable the Taxonomy Manager button in the Ribbon, the UiPath.IntelligentOCR.Activities package must be installed. This package includes all activities related to Document Understanding, including the Taxonomy Manager.