After you add new training data for topics or entities that you want to detect in
incoming messages, your text prediction contains pending training data. You need to
build the topic and entity extraction models to train them with this new training
The following example shows the pending training data for entity extraction models: three
items of data for the airlines_entities model, and one item
for the pySystemEntities model.
Important: The system builds the models in an asynchronous process by using a
job scheduler. The job scheduler for building models uses the System Runtime Context
(SRC) for rule resolution. To enable the model building process, you must add your
application to the SRC.
In the navigation pane of
App Studio, click Channels.
In the Current channel interfaces section, click
the icon that represents a channel for which you want to configure the
On the channel configuration page, click the
Behavior tab, and then click Open
In the Prediction workspace, click
In the Build models window, select the models that you
want to build.
Result: The training of the model starts. The status of the training is
displayed on the message bar below the prediction header. You can view detailed
progress information by clicking training jobs. At any
time, you can stop the model building process by clicking Cancel all
After the build is complete, click View report to
display a summary of the training.
In the model training report, you can compare the f-scores of the models before and after the training. In the
following example, the f-score of the model improved by 0.03 points after the