Text Classification (Multi-label) - Amazon SageMaker

Text Classification (Multi-label)

To categorize articles and text into multiple predefined categories, use the multi-label text classification task type. For example, you can use this task type to identify more than one emotion conveyed in text.

When working on a multi-label text classification task, workers should choose all applicable labels, but must choose at least one. When creating a job using this task type, you can provide up to 50 label categories.

Amazon SageMaker Ground Truth doesn't provide a "none" category for when none of the labels applies. To provide this option to workers, include a label similar to "none" or "other" when you create a multi-label text classification job.

To restrict workers to choosing a single label for each document or text selection, use the Text Classification (Single Label) task type.

Important

For this task type, if you create your own manifest file, use "source-ref" to identify the location of text files in Amazon S3 that you want labeled. If you provide the text that you want labeled directly in the input manifest file, use "source". For more information, see Input Data.

Create a Multi-Label Text Classification Labeling Job (Console)

You can follow the instructions Create a Labeling Job (Console) to learn how to create a multi-label text classification labeling job in the Amazon SageMaker console. In Step 10, choose Text from the Task category drop down menu, and choose Text Classification (Multi-label) as the task type.

Ground Truth provides a worker UI similar to the following for labeling tasks. When you create the labeling job with the console, you specify instructions to help workers complete the job and labels that workers can choose from.

Create a Multi-Label Text Classification Labeling Job (API)

To create a multi-label text classification labeling job, use the Amazon SageMaker API operation CreateLabelingJob. This API defines this operation for all AWS SDKs. To see a list of language-specific SDKs supported for this operation, review the See Also section of CreateLabelingJob.

Follow the instructions on Create a Labeling Job (API) and do the following while you configure your request:

  • Pre-annotation Lambda functions for this task type end with PRE-TextMultiClassMultiLabel. To find the pre-annotation Lambda ARN for your Region, see PreHumanTaskLambdaArn .

  • Annotation-consolidation Lambda functions for this task type end with ACS-TextMultiClassMultiLabel. To find the annotation-consolidation Lambda ARN for your Region, see AnnotationConsolidationLambdaArn.

The following is an example of an AWS Python SDK (Boto3) request to create a labeling job in the US East (N. Virginia) Region. All parameters in red should be replaced with your specifications and resources.

response = client.create_labeling_job( LabelingJobName='example-multi-label-text-classification-labeling-job, LabelAttributeName='label', InputConfig={ 'DataSource': { 'S3DataSource': { 'ManifestS3Uri': 's3://bucket/path/manifest-with-input-data.json' } }, 'DataAttributes': { 'ContentClassifiers': [ 'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent', ] } }, OutputConfig={ 'S3OutputPath': 's3://bucket/path/file-to-store-output-data', 'KmsKeyId': 'string' }, RoleArn='arn:aws:iam::*:role/*, LabelCategoryConfigS3Uri='s3://bucket/path/label-categories.json', StoppingConditions={ 'MaxHumanLabeledObjectCount': 123, 'MaxPercentageOfInputDatasetLabeled': 123 }, HumanTaskConfig={ 'WorkteamArn': 'arn:aws:sagemaker:region:*:workteam/private-crowd/*', 'UiConfig': { 'UiTemplateS3Uri': 's3://bucket/path/custom-worker-task-template.html' }, 'PreHumanTaskLambdaArn': 'arn:aws:lambda::function:PRE-TextMultiClassMultiLabel, 'TaskKeywords': [ 'Text Classification', ], 'TaskTitle': 'Multi-label text classification task', 'TaskDescription': 'Select all labels that apply to the text shown', 'NumberOfHumanWorkersPerDataObject': 123, 'TaskTimeLimitInSeconds': 123, 'TaskAvailabilityLifetimeInSeconds': 123, 'MaxConcurrentTaskCount': 123, 'AnnotationConsolidationConfig': { 'AnnotationConsolidationLambdaArn': 'arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel' }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] )

Create a Template for Multi-label Text Classification

If you create a labeling job using the API, you must supply a worker task template in UiTemplateS3Uri. Copy and modify the following template. Only modify the short-instructions, full-instructions, and header.

Upload this template to S3, and provide the S3 URI for this file in UiTemplateS3Uri.

<script src="https://assets.crowd.aws/crowd-html-elements.js"></script> <crowd-form> <crowd-classifier-multi-select name="crowd-classifier-multi-select" categories="{{ task.input.labels | to_json | escape }}" header="Please identify all classes in the below text" > <classification-target style="white-space: pre-wrap"> {{ task.input.taskObject }} </classification-target> <full-instructions header="Classifier instructions"> <ol><li><strong>Read</strong> the text carefully.</li> <li><strong>Read</strong> the examples to understand more about the options.</li> <li><strong>Choose</strong> the appropriate labels that best suit the text.</li></ol> </full-instructions> <short-instructions> <p>Enter description of the labels that workers have to choose from</p> <p><br></p> <p><br></p><p>Add examples to help workers understand the label</p> <p><br></p><p><br></p><p><br></p><p><br></p><p><br></p> </short-instructions> </crowd-classifier-multi-select> </crowd-form>

To learn how to create a custom template, see Creating Custom Labeling Workflows.

Multi-label Text Classification Output Data

Once you have created a multi-label text classification labeling job, your output data will be located in the Amazon S3 bucket specified in the S3OutputPath parameter when using the API or in the Output dataset location field of the Job overview section of the console.

To learn more about the output manifest file generated by Ground Truth and the file structure the Ground Truth uses to store your output data, see Output Data.

To see an example of output manifest files for multi-label text classification labeling job, see Multi-label Classification Job Output.