- Getting Started
- 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, 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
- 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 Analytics & Monitoring
- Automations and Communications Mining
- FAQs and More
Explore
The second phase of model training is known as 'Explore' (as each step is completed in the Explore page). In this phase, you will build on the foundations of the taxonomy that you created by reviewing clusters and searching for different terms and phrases in Discover.
The objective of the Explore phase is to provide each of the labels that are important to you with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset.
Explore is the core phase of model training, and requires the most time and effort. That being said, the more time you spend on completing the Explore phase in a thorough and consistent manner, the less time you will need to spend on the 'Refine' phase, which focuses on improving the performance of your model.
