1.0 - 8.00 - NERTrainer Arguments - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
Release Date
May 2019
Content Type
Programming Reference
Publication ID
English (United States)
Specify the name of the model file that the function creates and installs on the ML Engine.
Specify the name of the input table column that contains the text to analyze.
[Optional] Specify the name of the JAR file that contains the Java classes that extract features. You must install this JAR file on the ML Engine before calling the function. The function includes the predefined extractor classes described in the table in NERTrainer Feature Template.
The name jar_file is case-sensitive.
The ML Engine does not support the creation of new extractor classes. However, it does support existing JAR files—for installation instructions, see Teradata Vantage™ User Guide, B700-4002.
Default behavior: The function uses only the predefined extractor classes.
Specify the name of the file that specifies how to create features when training the model. For more information about template_file, see NERTrainer Feature Template.
[Optional] Specify the language of the input text:
Option Description
'en' (Default) English
'zh_CN' Simplified Chinese
'zh_TW' Traditional Chinese
[Optional] Specify the maximum number of iterations.
Default: 1000
[Optional] Specify the tolerance of the termination criterion. Defines the differences of the values of the loss function between two sequential epochs.
When training a model, the function performs n-times iterations. At the end of each epoch, the function calculates the loss or cost function on the training samples. If the loss function value change is very small between two sequential epochs, the function considers the training process to have converged.
The function defines Eta as:


where f(n) is the loss function value of the nth epoch.

Default: 0.0001
[Optional] Specify the minimum number times that a feature must occur in the input text before the function uses the feature to construct the model.
Default: 0