1.1 - 8.10 - NERTrainer Syntax Elements - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Release Date
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)
ModelFileName
Specify the name of the model file that the function creates and installs on ML Engine.
TextColumn
Specify the name of the input table column that contains the text to analyze.
ExtractorJAR
[Optional] Specify the name of the JAR file that contains the Java classes that extract features. You must install this JAR file on ML Engine before calling the function.
The name jar_file is case-sensitive.
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.
FeatureTemplate
Specify the name of the file that specifies how to create features when training the model.
InputLanguage
[Optional] Specify the language of the input text:
Option Description
'en' (Default) English
'zh_CN' Simplified Chinese
'zh_TW' Traditional Chinese
MaxIterNum
[Optional] Specify the maximum number of iterations.
Default: 1000
Eta
[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:

Eta=(f(n)-f(n-1))/f(n-1)

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

Default: 0.0001
MinOccurNum
[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