- 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
Model rollback
Introduction
The model rollback feature allows us to revert back to a previous version of our model, allowing us to reset the training data (for both label and general field annotations) to the annotations used to train this model version.
It is important to note that we can only roll back to pinned versions of models.
How to use this feature
On the 'Models' page, the model rollback icon will be available on all pinned versions of our model. To proceed with the model rollback, click the rollback icon on the model version you want to revert back to.
It is important to note that the current trained model version will automatically be pinned as a backup but any annotations captured by a model version that is currently still training will be lost.
We recommend allowing the current model version to finish training before rolling your model back. A popup module will come up to remind us of this, after clicking the rollback button. If we would like to proceed, we can click the 'Reset' button.
While the model is rolling back, we will not be able to modify the dataset. This means that we will not be able to train our model during this time, and apply any labels or general fields to messages. A warning indicator will show up at the top, letting us know that the model is currently being rolled back.
If we try to modify our dataset, the following banner will appear in the bottom right corner of our screen, and any messages we try to annotate will not have the label or general field applied to it until the model rollback has complete.
Although the rollback feature is here to help us roll back to a previous version of a model if we've made any major mistakes in our model training, we should not rely too heavily on it.
Instead, we should be ensuring that we are following the proper model training methodology correctly the first time, as this will save us time in the long-run.
