PLEASE NOTE: UiPath Communications Mining's Knowledge Base has been fully migrated to UiPath Docs. Please navigate to equivalent articles in UiPath Docs (here) for up to date guidance, as this site will no longer be updated and maintained.
Model Training & Maintenance
Guides on how to create, improve and maintain Models in Communications Mining, using platform features such as Discover, Explore and Validation
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Model Training & Maintenance
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Getting Started
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Manage Accounts & Access
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Using Analytics & Monitoring
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Automations & Communications Mining
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Technical Support
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FAQs & More
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- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete verbatims via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Preparing Data for .CSV Upload
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- Understanding labels, entities and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Understanding the status of your dataset
- Model training and labelling best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
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- Introduction to 'Refine'
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using 'Check label' and 'Missed label'
- Training using 'Teach label' (Refine)
- Understanding and increasing coverage
- Improving Balance and using 'Rebalance'
- When to stop training your model