@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateLabelingJobRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
|---|
CreateLabelingJobRequest() |
| Modifier and Type | Method and Description |
|---|---|
CreateLabelingJobRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
HumanTaskConfig |
getHumanTaskConfig()
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords,
and batch size (task count).
|
LabelingJobInputConfig |
getInputConfig()
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the
manifest file that describes the data objects.
|
String |
getLabelAttributeName()
The attribute name to use for the label in the output manifest file.
|
String |
getLabelCategoryConfigS3Uri()
The S3 URI of the file, referred to as a label category configuration file, that defines the categories
used to label the data objects.
|
LabelingJobAlgorithmsConfig |
getLabelingJobAlgorithmsConfig()
Configures the information required to perform automated data labeling.
|
String |
getLabelingJobName()
The name of the labeling job.
|
LabelingJobOutputConfig |
getOutputConfig()
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to
encrypt the output data, if any.
|
String |
getRoleArn()
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data
labeling.
|
LabelingJobStoppingConditions |
getStoppingConditions()
A set of conditions for stopping the labeling job.
|
List<Tag> |
getTags()
An array of key/value pairs.
|
int |
hashCode() |
void |
setHumanTaskConfig(HumanTaskConfig humanTaskConfig)
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords,
and batch size (task count).
|
void |
setInputConfig(LabelingJobInputConfig inputConfig)
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the
manifest file that describes the data objects.
|
void |
setLabelAttributeName(String labelAttributeName)
The attribute name to use for the label in the output manifest file.
|
void |
setLabelCategoryConfigS3Uri(String labelCategoryConfigS3Uri)
The S3 URI of the file, referred to as a label category configuration file, that defines the categories
used to label the data objects.
|
void |
setLabelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig)
Configures the information required to perform automated data labeling.
|
void |
setLabelingJobName(String labelingJobName)
The name of the labeling job.
|
void |
setOutputConfig(LabelingJobOutputConfig outputConfig)
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to
encrypt the output data, if any.
|
void |
setRoleArn(String roleArn)
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data
labeling.
|
void |
setStoppingConditions(LabelingJobStoppingConditions stoppingConditions)
A set of conditions for stopping the labeling job.
|
void |
setTags(Collection<Tag> tags)
An array of key/value pairs.
|
String |
toString()
Returns a string representation of this object.
|
CreateLabelingJobRequest |
withHumanTaskConfig(HumanTaskConfig humanTaskConfig)
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords,
and batch size (task count).
|
CreateLabelingJobRequest |
withInputConfig(LabelingJobInputConfig inputConfig)
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the
manifest file that describes the data objects.
|
CreateLabelingJobRequest |
withLabelAttributeName(String labelAttributeName)
The attribute name to use for the label in the output manifest file.
|
CreateLabelingJobRequest |
withLabelCategoryConfigS3Uri(String labelCategoryConfigS3Uri)
The S3 URI of the file, referred to as a label category configuration file, that defines the categories
used to label the data objects.
|
CreateLabelingJobRequest |
withLabelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig)
Configures the information required to perform automated data labeling.
|
CreateLabelingJobRequest |
withLabelingJobName(String labelingJobName)
The name of the labeling job.
|
CreateLabelingJobRequest |
withOutputConfig(LabelingJobOutputConfig outputConfig)
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to
encrypt the output data, if any.
|
CreateLabelingJobRequest |
withRoleArn(String roleArn)
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data
labeling.
|
CreateLabelingJobRequest |
withStoppingConditions(LabelingJobStoppingConditions stoppingConditions)
A set of conditions for stopping the labeling job.
|
CreateLabelingJobRequest |
withTags(Collection<Tag> tags)
An array of key/value pairs.
|
CreateLabelingJobRequest |
withTags(Tag... tags)
An array of key/value pairs.
|
addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeoutpublic void setLabelingJobName(String labelingJobName)
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job
names must be unique within an Amazon Web Services account and region. LabelingJobName is not case
sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
labelingJobName - The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling
job names must be unique within an Amazon Web Services account and region. LabelingJobName is
not case sensitive. For example, Example-job and example-job are considered the same labeling job name by
Ground Truth.public String getLabelingJobName()
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job
names must be unique within an Amazon Web Services account and region. LabelingJobName is not case
sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
LabelingJobName
is not case sensitive. For example, Example-job and example-job are considered the same labeling job name
by Ground Truth.public CreateLabelingJobRequest withLabelingJobName(String labelingJobName)
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job
names must be unique within an Amazon Web Services account and region. LabelingJobName is not case
sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
labelingJobName - The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling
job names must be unique within an Amazon Web Services account and region. LabelingJobName is
not case sensitive. For example, Example-job and example-job are considered the same labeling job name by
Ground Truth.public void setLabelAttributeName(String labelAttributeName)
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair
formed with the label that a worker assigns to the object. The LabelAttributeName must meet the
following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation)
labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (
Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job is the
Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about
adjustment and verification labeling jobs, see Verify and Adjust Labels.
labelAttributeName - The attribute name to use for the label in the output manifest file. This is the key for the key/value
pair formed with the label that a worker assigns to the object. The LabelAttributeName must
meet the following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (
VerificationSemanticSegmentation) labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification
(Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job
is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn
more about adjustment and verification labeling jobs, see Verify and Adjust
Labels.
public String getLabelAttributeName()
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair
formed with the label that a worker assigns to the object. The LabelAttributeName must meet the
following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation)
labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (
Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job is the
Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about
adjustment and verification labeling jobs, see Verify and Adjust Labels.
LabelAttributeName must
meet the following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (
VerificationSemanticSegmentation) labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification
(Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job
is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn
more about adjustment and verification labeling jobs, see Verify and Adjust
Labels.
public CreateLabelingJobRequest withLabelAttributeName(String labelAttributeName)
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair
formed with the label that a worker assigns to the object. The LabelAttributeName must meet the
following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation)
labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (
Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job is the
Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about
adjustment and verification labeling jobs, see Verify and Adjust Labels.
labelAttributeName - The attribute name to use for the label in the output manifest file. This is the key for the key/value
pair formed with the label that a worker assigns to the object. The LabelAttributeName must
meet the following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (
VerificationSemanticSegmentation) labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification
(Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job
is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn
more about adjustment and verification labeling jobs, see Verify and Adjust
Labels.
public void setInputConfig(LabelingJobInputConfig inputConfig)
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify
and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in
the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs.
Adding an S3DataSource is optional if you use SnsDataSource to create a streaming
labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information,
personal information or protected health information. Use ContentClassifiers to specify that your
data is free of personally identifiable information and adult content.
inputConfig - Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of
the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not
specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all
data objects in the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling
jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a
streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential
information, personal information or protected health information. Use ContentClassifiers to
specify that your data is free of personally identifiable information and adult content.
public LabelingJobInputConfig getInputConfig()
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify
and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in
the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs.
Adding an S3DataSource is optional if you use SnsDataSource to create a streaming
labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information,
personal information or protected health information. Use ContentClassifiers to specify that your
data is free of personally identifiable information and adult content.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not
specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all
data objects in the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling
jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a
streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential
information, personal information or protected health information. Use ContentClassifiers to
specify that your data is free of personally identifiable information and adult content.
public CreateLabelingJobRequest withInputConfig(LabelingJobInputConfig inputConfig)
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify
and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in
the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs.
Adding an S3DataSource is optional if you use SnsDataSource to create a streaming
labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information,
personal information or protected health information. Use ContentClassifiers to specify that your
data is free of personally identifiable information and adult content.
inputConfig - Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of
the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not
specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all
data objects in the input manifest file have been labeled.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling
jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a
streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential
information, personal information or protected health information. Use ContentClassifiers to
specify that your data is free of personally identifiable information and adult content.
public void setOutputConfig(LabelingJobOutputConfig outputConfig)
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
outputConfig - The location of the output data and the Amazon Web Services Key Management Service key ID for the key used
to encrypt the output data, if any.public LabelingJobOutputConfig getOutputConfig()
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
public CreateLabelingJobRequest withOutputConfig(LabelingJobOutputConfig outputConfig)
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
outputConfig - The location of the output data and the Amazon Web Services Key Management Service key ID for the key used
to encrypt the output data, if any.public void setRoleArn(String roleArn)
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
roleArn - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data
labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully
complete data labeling.public String getRoleArn()
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
public CreateLabelingJobRequest withRoleArn(String roleArn)
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
roleArn - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data
labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully
complete data labeling.public void setLabelCategoryConfigS3Uri(String labelCategoryConfigS3Uri)
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker instructions in
the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a
Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify the
labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.
labelCategoryConfigS3Uri - The S3 URI of the file, referred to as a label category configuration file, that defines the
categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker
instructions in the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify
the labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the
LabelAttributeName of the labeling job you want to adjust or verify annotations of.
public String getLabelCategoryConfigS3Uri()
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker instructions in
the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a
Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify the
labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker
instructions in the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify
the labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the
LabelAttributeName of the labeling job you want to adjust or verify annotations of.
public CreateLabelingJobRequest withLabelCategoryConfigS3Uri(String labelCategoryConfigS3Uri)
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker instructions in
the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a
Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify the
labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.
labelCategoryConfigS3Uri - The S3 URI of the file, referred to as a label category configuration file, that defines the
categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker
instructions in the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify
the labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the
LabelAttributeName of the labeling job you want to adjust or verify annotations of.
public void setStoppingConditions(LabelingJobStoppingConditions stoppingConditions)
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
stoppingConditions - A set of conditions for stopping the labeling job. If any of the conditions are met, the job is
automatically stopped. You can use these conditions to control the cost of data labeling.public LabelingJobStoppingConditions getStoppingConditions()
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
public CreateLabelingJobRequest withStoppingConditions(LabelingJobStoppingConditions stoppingConditions)
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
stoppingConditions - A set of conditions for stopping the labeling job. If any of the conditions are met, the job is
automatically stopped. You can use these conditions to control the cost of data labeling.public void setLabelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig)
Configures the information required to perform automated data labeling.
labelingJobAlgorithmsConfig - Configures the information required to perform automated data labeling.public LabelingJobAlgorithmsConfig getLabelingJobAlgorithmsConfig()
Configures the information required to perform automated data labeling.
public CreateLabelingJobRequest withLabelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig)
Configures the information required to perform automated data labeling.
labelingJobAlgorithmsConfig - Configures the information required to perform automated data labeling.public void setHumanTaskConfig(HumanTaskConfig humanTaskConfig)
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
humanTaskConfig - Configures the labeling task and how it is presented to workers; including, but not limited to price,
keywords, and batch size (task count).public HumanTaskConfig getHumanTaskConfig()
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
public CreateLabelingJobRequest withHumanTaskConfig(HumanTaskConfig humanTaskConfig)
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
humanTaskConfig - Configures the labeling task and how it is presented to workers; including, but not limited to price,
keywords, and batch size (task count).public List<Tag> getTags()
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
public void setTags(Collection<Tag> tags)
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
tags - An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.public CreateLabelingJobRequest withTags(Tag... tags)
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
NOTE: This method appends the values to the existing list (if any). Use
setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the
existing values.
tags - An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.public CreateLabelingJobRequest withTags(Collection<Tag> tags)
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
tags - An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.public String toString()
toString in class ObjectObject.toString()public CreateLabelingJobRequest clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()