- 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
- Delete a source
- 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)
- Understanding the status of your dataset
- 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
Train
The new 'Train' feature provides a fully guided label training experience for users.
The main page of Train provides useful information regarding the training done so far, the performance of the model, and a list of prioritised next best training actions to take (similar to the Validation page).
When users select a training action to take, they are taken to a specific 'training batch' interface, which breaks up the training into short, easy-to-consume sessions.
At the end of the batch, you're provided with a summary of the training actions taken (see below), and can then choose your next session by selecting another recommended action.
Summary of training actions completed during a training batch
If users prefer to train without the platform's guidance, they can disable this and select themselves which sessions to complete. For more detail, see the section below.
The default setting for the Train page is to have platform guidance enabled, as this is our recommendation.
If you're a confident model trainer, however, and you know the actions that you want to take already, you can disable the guidance using the toggle in the top right-hand of the page (see below).