- In the navigation pane of App Studio, click Channels.
- In the Current channel interfaces section, click the icon that represents your existing Email channel.
- In the Email channel, click the Behavior tab.
- In the Text Analyzer section, select the Record training data checkbox.
- Optional: To use an advanced text analyzer configuration, perform the following
- In the Text Analyzer section, select the Use advanced configuration checkbox.
- Click Add text analyzer, or if the iNLP text analyzer type is already displayed in the list, click the Switch to edit mode icon in the iNLP row.
- In the Text analyzer configuration window, in the Text Analyzer type list, select iNLP.
- Select the Enable model training check box.
- Click Submit.
- Click Save.
- Switch to Dev Studio.
- In the navigation pane of Dev Studio, click App, and then search for the Work-Channel-Triage-Email class.
- Expand the section for the Work-Channel-Triage-Email class, and then click pyDefault.
- In the pyDefault data transform rule, in the table row for the pyEnableAttachmentTraining target, in the Source column, enter: true
- Optional: To increase the limit on the amount of text extracted from file attachments in the system, in the table row for the pyExtractedTextLimit target, in the Source column, enter the maximum number of bytes to extract as text from file attachments.
- Save your changes by performing one of the following actions:
- To save the rule that belongs to a locked ruleset to an isolated sandbox
so that you can test your changes, click Private
For more information, see Performing a private edit.
- To save the rule to a ruleset, click Save as.
- To save the rule that belongs to a locked ruleset to an isolated sandbox so that you can test your changes, click Private edit.
- Switch back to App Studio.
By default, you train the text analytics model in your system by using training data from email bodies. By enabling the training of model based on email attachments, you can also use training data from text files, and image-based files if you are using an external optical character recognition (OCR) component in your system.