NLPTrainer

Trains a natural language processing (NLP) classification model based on the user’s specifications and the provided data.

NLPTrainer expects tagged data as input, with each feature bearing a single text and label. Some preprocessing of this learning data may be required, and the AttributeCreator transformer can be useful for this. Based on the set of learning data and the NLP features (specific types of information about the text) that the user specifies, a model will then be created and written to a *.fmd (FME MoDel) file. The companion transformer to this one, NLPClassifier, uses these *.fmd files to perform natural language classification, sorting texts into the categories labelled in the training data.

Usage Notes

  • For more information about natural language processing with FME, see the documentation for the companion NLPClassifier transformer.

Configuration

Input Ports

Output Ports

Parameters

Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.

Reference

Processing Behavior

Feature-Based

Feature Holding

Yes

Dependencies  
FME Licensing Level FME Base Edition and above
Aliases  
History Released FME 2019.0

FME Community

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Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.