- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- 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 messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields 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)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- 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)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Communications Mining User Guide
Train
User permissions required: View Sources AND Review and label.
The main Train page provides useful information about the training done so far, the performance of the model, and a list of prioritized next best training actions to take same as the Validation page. It is a fully guided label training experience.
To train an action:
- Select a training action to go to the specific training batch interface, for short, easy-to-consume training sessions.
Depending on the recommended action, the number of messages or clusters of messages in the batch is 10, but it can vary.
Batch training page for 'Shuffle' training - Apply the labels (and general fields) to the message(s) on the screen.
- Select Done. You can move onto the next message or cluster by clicking Next.
- At the end of the batch, you'll see a summary of the training actions you took. To choose your next session, select another
recommended action.
Summary of training actions completed during a training batch
- Select another recommended action to choose your next session.
If you prefer to train without the platform's guidance, you can disable the Guided toggle icon and select which sessions to complete. For more details, check the Using Train without guidance enabled for labels section.
Train will further become the main place to complete all of your model training from start to finish, but some additional features are still in development (e.g. guided general field training). Right now, it's an add-on to the existing feature set, meaning that all of the functionalities you're used to can be used as-is, and you can train models as you usually do.
It is recommended that you use Train for a guided label training experience, and provide feedback to your UiPath® Account Manager if encountering any issues or challenges.
Label training
Training in Train:
- Guides you right from the moment you create a dataset with the next best actions to take to advance your label training - this includes uploading a taxonomy before you begin training
- Guides you through the usual steps covered elsewhere in this Knowledge Base for the model training process (check Overview), with the exception of recommending
search
- For an effective training mode, use the
Search
action sparingly, to provide the model with a limited set of initial examples for labels that don't have enough training data yet. To use this action, go to Discover, Explore, or by temporarily disabling the guidance in Train (check the Using Train without guidance enabled for labels section for more details).
- For an effective training mode, use the
- Provides need to know performance feedback in the main page and through its recommendations. If you need detailed feedback on model performance, go to the Validation page.
annotation progress
areas to see the additional progress indicators.
General field training
Training general fields in Train:
- Guides you right from the moment you create a dataset with the next best actions to take to advance your general field training.
- Guides you through the usual steps covered elsewhere in this Knowledge Base for training general fields during the model training process.
- Provides need to know performance feedback in the main page and through its recommendations. If you need detailed feedback on general field performance, go to the Validation, then General field Validation pages.
- During the beginning of the model training process, if the platform doesn't have enough examples of general fields to learn
from - it will recommend
shuffle
by default. Once you provide enough examples, it will recommend more targeted training for specific general fields.
The default setting for the Train page is to have platform guidance enabled, as this is our recommendation.