- 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)
- 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
Delete messages via the UI
User permissions required: ‘Edit messages’.
There may be instances where messages have been uploaded to sources that you may not want in the platform. This could be because:
- The message has been corrupted in some way and is unusable
- The message contains sensitive information that should not be shared
- The message adds no value to the dataset
Whatever the reason may be, you can delete messages from a source in the UI via a dataset containing that source. This functionality is also available via the API (see here).
Please Note: Deleting a message from a source whilst in one dataset will remove that message from any other dataset containing that source, so do not take this action lightly. The message could be acting as training data in another dataset and removing it would impact the model in that dataset. If a message in a source does not add value for your use case, confirm that it is not providing value for other datasets before deleting it. The sources page gives an indication of other datasets that are connected to the source in question.
To delete a message, simply click the delete button in the bottom right-hand corner of the message as shown below. This button will only show up if you have the required 'Edit messages' permission.
You will then be prompted with a warning and a confirmation request as shown below. If you're happy to proceed despite the warning, click 'Delete'.
