tNLPPredict properties for Apache Spark Batch - 7.0

Natural Language Processing

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Natural Language Processing
Data Quality and Preparation > Third-party systems > Natural Language Processing
Design and Development > Third-party systems > Natural Language Processing
EnrichPlatform
Talend Studio

These properties are used to configure tNLPPredict running in the Spark Batch Job framework.

The Spark Batch tNLPPredict component belongs to the Natural Language Processing family.

The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric.

Basic settings

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

Click Sync columns to retrieve the schema from the previous component connected in the Job.

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the [Repository Content] window.

Read-only columns are added to the output schema:

  • outputsent: This column holds the labeled text.

  • outputlabel: This column holds the labels.

 

Built-In: You create and store the schema locally for this component only. Related topic: see Talend Studio User Guide.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

Define a storage configuration component

Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local system.

The configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.

Original text column

Select the column to be labeled in the input schema.

Token column

Select the column used for feature construction and prediction.

Additional Features

Select this check box to add additional features to the Additional feature template.

When you add features, the order must be the same as the additional features used in the TNLPModel component to generate the model file.

NLP model path

Set the path to the folder from where you want to retrieve the model files.

If the model is stored in a single file, select the Use the model file check box and set the path to the model file.

For example: "/opt/model/<model_name>"

If you want to store the model in a specific file system, for example S3 or HDFS, you must use the corresponding component in the Job and select the Define a storage configuration component check box in the component basic settings.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

Usage

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Spark Batch Connection

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):
    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.