The Class Manager allows to built an Intent Classifier directly in Teneo Studio by adding Classes and Training Data; read more about Intent Classification in Teneo here.
The information related to a class, such as the name, Id and training data, is searchable in the Search tab of the main Teneo Studio window.
The Id of a class is available at the bottom of the training data in the Class details of the Class Manager when selecting the class. To copy the Id, simply right-click it and select copy from the context menu.
For more information, please visit the Search section.
In Teneo Studio Desktop, the Class Manager allows to import several classes and training data from an external file (in .tsv format).
To import a .tsv file, follow the below steps:
- In the Class Manager, click Import Classes
- Browse to the location of the .tsv file, select it and click Open
- Remember to click Save in the Class Manager to conserve the modifications.
The requirements of the file to import are as follows:
- plain text file, saved in the tab-separated values (.tsv) format
- each line must contain a valid class name, separated from the training data example by tab
- one line per training data example
Class name requirements
The requirements for the class names are:
- the class name must be unique
- the class name must be upper case
- the class name cannot contain reserved characters nor spaces
Teneo Studio will automatically suggest a new class the name CLASS, this should be customized by the user. If the user adds more classes (without customizing the names), Teneo Studio will add an incremental number to the suggested names of the next classes, for example, CLASS, CLASS_1, CLASS_2, CLASS_3, etc. If the Class Name field is left empty, the user will not be able to save modifications until a name is provided.
Class name as annotation
At runtime, Teneo Predict uses the names of the classes to create the annotations for the most probable intent class(es) and denotes the annotation with a confidence score. The name of the annotation is the same as the name of a class in the Class Manager.
Therefore, the class name is visible in any place where annotations are usually seen, for example, in Tryout or scripting, and because of this users are encouraged to give each class a unique and easily recognizable class name.
As the class names are applied within the Teneo Platform as annotations, these can also be used within TLML syntaxes using the Teneo Linguistic Modeling Language.
For optimal performance, users are recommended to provide a minimum of ten unique training data examples per class and it is not possible to add two identical examples to a class. To avoid duplicate training data, the Class Manager prevents the user from saving changes if two or more training data entries are exactly the same.
To train a machine learning model, more than one class needs to be added to the solution and on top of this, the Class Manager, displays an error if a user tries to save an empty class (i.e., a class without training data).
To run Class Performance each class must contain a minimum of 5 training data examples