# Categorize text with 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. The following sections give information about how to create a multi-label text classification task from the console and API.
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 [Categorize text with text classification (Single Label)](sms-text-classification.md) task type.
**Important**
If you manually create an input manifest file, use `"source"` to identify the text that you want labeled. For more information, see [Input data](sms-data-input.md).
## Create a Multi-Label Text Classification Labeling Job (Console)
You can follow the instructions [Create a Labeling Job (Console)](sms-create-labeling-job-console.md) to learn how to create a multi-label text classification labeling job in the Amazon SageMaker AI 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.
![\[Gif showing how to create a multi-label text classification labeling job in the Amazon SageMaker AI console.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/sms/gifs/multi-label-text.gif)
## Create a Multi-Label Text Classification Labeling Job (API)
To create a multi-label text classification labeling job, use the 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 [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html).
Follow the instructions on [Create a Labeling Job (API)](sms-create-labeling-job-api.md) 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](https://docs.aws.amazon.com/sagemaker/latest/dg/API_HumanTaskConfig.html#SageMaker-Type-HumanTaskConfig-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](https://docs.aws.amazon.com/sagemaker/latest/dg/API_AnnotationConsolidationConfig.html#SageMaker-Type-AnnotationConsolidationConfig-AnnotationConsolidationLambdaArn).
The following is an example of an [AWS Python SDK (Boto3) request](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_labeling_job) 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 [https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-quick-instructions](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-quick-instructions), [https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-full-instructions](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-full-instructions), and `header`.
Upload this template to S3, and provide the S3 URI for this file in `UiTemplateS3Uri`.
```
{{ task.input.taskObject }}