Upload a CSV or XLSX (Excel Workbook) file containing utterances and their intents to
create a Natural Language Understanding (NLU) model. Use this method to quickly create models
from your data or other exported models.
始める前に
Make sure that the NLU Workbench plugin, NLU Workbench - Core plugin, and Predictive Intelligence plugin are all installed and activated on your instance.
You can create NLU models for Virtual Agent and AI Search.
Role required: admin or nlu_admin
このタスクについて
In this example procedure, you're building an NLU model to help Virtual Agent understand user requests regarding calendars.
In a CSV file, you've listed the intents and utterances in two columns.図 : 1. Sample CSV setup
Note the following for creating NLU models by CSV import:
A model needs at least 1 intent with a minimum of 5
training utterances in each intent. For optimum performance, aim to have 15
training utterances per intent.
Utterances should not contain a comma.
Importing with a CSV file does not preserve entities. Make sure to annotate utterances as needed after importing.
手順
Set your scope to the application scope you want for your new model.
Navigate to All > NLU Workbench > Models.
The Virtual Agent tab opens by default.
Select the tab for the type of model you want to create, such as AI Search.
Select the Create new model button.
In the How do you want to create your model? window, select
Import data from a CSV.
In the Add some details window, add the
Name and Short description for
the model.
In this example scenario, you enter Calendar Model
for the name and Model for answering and performing calendar
requests for the short description.
Select the language and purpose from the drop-down lists.
In this example scenario, you select English and
Virtual Agent.
Click Next.
On the Import CSV screen, click Select
file.
Choose the CSV or XLSX (Excel Workbook) file from the pop-up.
Select Next.
Your model starts building. After completion, select View model to open the model details page.
次のタスク
Add intents and training utterances to continue building the model. Add entities and vocabulary to help the model understand inputs from your users. For more information, see Build and train your model.
Add test utterances and intents to build the model's default test set. For more information, see Test set creation and management.
