Package cloud.google.com/go/automl/apiv1/automlpb (v1.8.0)

Variables

ClassificationType_name, ClassificationType_value

var (
	ClassificationType_name = map[int32]string{
		0: "CLASSIFICATION_TYPE_UNSPECIFIED",
		1: "MULTICLASS",
		2: "MULTILABEL",
	}
	ClassificationType_value = map[string]int32{
		"CLASSIFICATION_TYPE_UNSPECIFIED": 0,
		"MULTICLASS":                      1,
		"MULTILABEL":                      2,
	}
)

Enum value maps for ClassificationType.

DocumentDimensions_DocumentDimensionUnit_name, DocumentDimensions_DocumentDimensionUnit_value

var (
	DocumentDimensions_DocumentDimensionUnit_name = map[int32]string{
		0: "DOCUMENT_DIMENSION_UNIT_UNSPECIFIED",
		1: "INCH",
		2: "CENTIMETER",
		3: "POINT",
	}
	DocumentDimensions_DocumentDimensionUnit_value = map[string]int32{
		"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED": 0,
		"INCH":                                1,
		"CENTIMETER":                          2,
		"POINT":                               3,
	}
)

Enum value maps for DocumentDimensions_DocumentDimensionUnit.

Document_Layout_TextSegmentType_name, Document_Layout_TextSegmentType_value

var (
	Document_Layout_TextSegmentType_name = map[int32]string{
		0: "TEXT_SEGMENT_TYPE_UNSPECIFIED",
		1: "TOKEN",
		2: "PARAGRAPH",
		3: "FORM_FIELD",
		4: "FORM_FIELD_NAME",
		5: "FORM_FIELD_CONTENTS",
		6: "TABLE",
		7: "TABLE_HEADER",
		8: "TABLE_ROW",
		9: "TABLE_CELL",
	}
	Document_Layout_TextSegmentType_value = map[string]int32{
		"TEXT_SEGMENT_TYPE_UNSPECIFIED": 0,
		"TOKEN":                         1,
		"PARAGRAPH":                     2,
		"FORM_FIELD":                    3,
		"FORM_FIELD_NAME":               4,
		"FORM_FIELD_CONTENTS":           5,
		"TABLE":                         6,
		"TABLE_HEADER":                  7,
		"TABLE_ROW":                     8,
		"TABLE_CELL":                    9,
	}
)

Enum value maps for Document_Layout_TextSegmentType.

Model_DeploymentState_name, Model_DeploymentState_value

var (
	Model_DeploymentState_name = map[int32]string{
		0: "DEPLOYMENT_STATE_UNSPECIFIED",
		1: "DEPLOYED",
		2: "UNDEPLOYED",
	}
	Model_DeploymentState_value = map[string]int32{
		"DEPLOYMENT_STATE_UNSPECIFIED": 0,
		"DEPLOYED":                     1,
		"UNDEPLOYED":                   2,
	}
)

Enum value maps for Model_DeploymentState.

File_google_cloud_automl_v1_annotation_payload_proto

var File_google_cloud_automl_v1_annotation_payload_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_annotation_spec_proto

var File_google_cloud_automl_v1_annotation_spec_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_classification_proto

var File_google_cloud_automl_v1_classification_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_data_items_proto

var File_google_cloud_automl_v1_data_items_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_dataset_proto

var File_google_cloud_automl_v1_dataset_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_detection_proto

var File_google_cloud_automl_v1_detection_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_geometry_proto

var File_google_cloud_automl_v1_geometry_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_image_proto

var File_google_cloud_automl_v1_image_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_io_proto

var File_google_cloud_automl_v1_io_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_model_evaluation_proto

var File_google_cloud_automl_v1_model_evaluation_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_model_proto

var File_google_cloud_automl_v1_model_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_operations_proto

var File_google_cloud_automl_v1_operations_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_prediction_service_proto

var File_google_cloud_automl_v1_prediction_service_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_service_proto

var File_google_cloud_automl_v1_service_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_text_extraction_proto

var File_google_cloud_automl_v1_text_extraction_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_text_proto

var File_google_cloud_automl_v1_text_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_text_segment_proto

var File_google_cloud_automl_v1_text_segment_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_text_sentiment_proto

var File_google_cloud_automl_v1_text_sentiment_proto protoreflect.FileDescriptor

File_google_cloud_automl_v1_translation_proto

var File_google_cloud_automl_v1_translation_proto protoreflect.FileDescriptor

Functions

func RegisterAutoMlServer

func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer)

func RegisterPredictionServiceServer

func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer)

AnnotationPayload

type AnnotationPayload struct {

	// Output only . Additional information about the annotation
	// specific to the AutoML domain.
	//
	// Types that are assignable to Detail:
	//
	//	*AnnotationPayload_Translation
	//	*AnnotationPayload_Classification
	//	*AnnotationPayload_ImageObjectDetection
	//	*AnnotationPayload_TextExtraction
	//	*AnnotationPayload_TextSentiment
	Detail isAnnotationPayload_Detail `protobuf_oneof:"detail"`
	// Output only . The resource ID of the annotation spec that
	// this annotation pertains to. The annotation spec comes from either an
	// ancestor dataset, or the dataset that was used to train the model in use.
	AnnotationSpecId string `protobuf:"bytes,1,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. The value of
	// [display_name][google.cloud.automl.v1.AnnotationSpec.display_name]
	// when the model was trained. Because this field returns a value at model
	// training time, for different models trained using the same dataset, the
	// returned value could be different as model owner could update the
	// `display_name` between any two model training.
	DisplayName string `protobuf:"bytes,5,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// contains filtered or unexported fields
}

Contains annotation information that is relevant to AutoML.

func (*AnnotationPayload) Descriptor

func (*AnnotationPayload) Descriptor() ([]byte, []int)

Deprecated: Use AnnotationPayload.ProtoReflect.Descriptor instead.

func (*AnnotationPayload) GetAnnotationSpecId

func (x *AnnotationPayload) GetAnnotationSpecId() string

func (*AnnotationPayload) GetClassification

func (x *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (*AnnotationPayload) GetDetail

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (*AnnotationPayload) GetDisplayName

func (x *AnnotationPayload) GetDisplayName() string

func (*AnnotationPayload) GetImageObjectDetection

func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (*AnnotationPayload) GetTextExtraction

func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (*AnnotationPayload) GetTextSentiment

func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (*AnnotationPayload) GetTranslation

func (x *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) ProtoMessage

func (*AnnotationPayload) ProtoMessage()

func (*AnnotationPayload) ProtoReflect

func (x *AnnotationPayload) ProtoReflect() protoreflect.Message

func (*AnnotationPayload) Reset

func (x *AnnotationPayload) Reset()

func (*AnnotationPayload) String

func (x *AnnotationPayload) String() string

AnnotationPayload_Classification

type AnnotationPayload_Classification struct {
	// Annotation details for content or image classification.
	Classification *ClassificationAnnotation `protobuf:"bytes,3,opt,name=classification,proto3,oneof"`
}

AnnotationPayload_ImageObjectDetection

type AnnotationPayload_ImageObjectDetection struct {
	// Annotation details for image object detection.
	ImageObjectDetection *ImageObjectDetectionAnnotation `protobuf:"bytes,4,opt,name=image_object_detection,json=imageObjectDetection,proto3,oneof"`
}

AnnotationPayload_TextExtraction

type AnnotationPayload_TextExtraction struct {
	// Annotation details for text extraction.
	TextExtraction *TextExtractionAnnotation `protobuf:"bytes,6,opt,name=text_extraction,json=textExtraction,proto3,oneof"`
}

AnnotationPayload_TextSentiment

type AnnotationPayload_TextSentiment struct {
	// Annotation details for text sentiment.
	TextSentiment *TextSentimentAnnotation `protobuf:"bytes,7,opt,name=text_sentiment,json=textSentiment,proto3,oneof"`
}

AnnotationPayload_Translation

type AnnotationPayload_Translation struct {
	// Annotation details for translation.
	Translation *TranslationAnnotation `protobuf:"bytes,2,opt,name=translation,proto3,oneof"`
}

AnnotationSpec

type AnnotationSpec struct {

	// Output only. Resource name of the annotation spec.
	// Form:
	// 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the annotation spec to show in the interface. The name can be
	// up to 32 characters long and must match the regexp `[a-zA-Z0-9_]+`.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. The number of examples in the parent dataset
	// labeled by the annotation spec.
	ExampleCount int32 `protobuf:"varint,9,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// contains filtered or unexported fields
}

A definition of an annotation spec.

func (*AnnotationSpec) Descriptor

func (*AnnotationSpec) Descriptor() ([]byte, []int)

Deprecated: Use AnnotationSpec.ProtoReflect.Descriptor instead.

func (*AnnotationSpec) GetDisplayName

func (x *AnnotationSpec) GetDisplayName() string

func (*AnnotationSpec) GetExampleCount

func (x *AnnotationSpec) GetExampleCount() int32

func (*AnnotationSpec) GetName

func (x *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage

func (*AnnotationSpec) ProtoMessage()

func (*AnnotationSpec) ProtoReflect

func (x *AnnotationSpec) ProtoReflect() protoreflect.Message

func (*AnnotationSpec) Reset

func (x *AnnotationSpec) Reset()

func (*AnnotationSpec) String

func (x *AnnotationSpec) String() string

AutoMlClient

type AutoMlClient interface {
	// Creates a dataset.
	CreateDataset(ctx context.Context, in *CreateDatasetRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets a dataset.
	GetDataset(ctx context.Context, in *GetDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(ctx context.Context, in *ListDatasetsRequest, opts ...grpc.CallOption) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(ctx context.Context, in *UpdateDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(ctx context.Context, in *DeleteDatasetRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1.InputConfig.params]
	//
	//	parameter must be explicitly set.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(ctx context.Context, in *ImportDataRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(ctx context.Context, in *ExportDataRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(ctx context.Context, in *GetAnnotationSpecRequest, opts ...grpc.CallOption) (*AnnotationSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(ctx context.Context, in *CreateModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets a model.
	GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Lists models.
	ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Updates a model.
	UpdateModel(ctx context.Context, in *UpdateModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	// [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//
	//	will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(ctx context.Context, in *DeployModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(ctx context.Context, in *UndeployModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(ctx context.Context, in *ExportModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(ctx context.Context, in *GetModelEvaluationRequest, opts ...grpc.CallOption) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(ctx context.Context, in *ListModelEvaluationsRequest, opts ...grpc.CallOption) (*ListModelEvaluationsResponse, error)
}

AutoMlClient is the client API for AutoMl service.

For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.

func NewAutoMlClient

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

AutoMlServer

type AutoMlServer interface {
	// Creates a dataset.
	CreateDataset(context.Context, *CreateDatasetRequest) (*longrunning.Operation, error)
	// Gets a dataset.
	GetDataset(context.Context, *GetDatasetRequest) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(context.Context, *ListDatasetsRequest) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(context.Context, *UpdateDatasetRequest) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(context.Context, *DeleteDatasetRequest) (*longrunning.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1.InputConfig.params]
	//
	//	parameter must be explicitly set.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(context.Context, *ImportDataRequest) (*longrunning.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(context.Context, *ExportDataRequest) (*longrunning.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(context.Context, *GetAnnotationSpecRequest) (*AnnotationSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(context.Context, *CreateModelRequest) (*longrunning.Operation, error)
	// Gets a model.
	GetModel(context.Context, *GetModelRequest) (*Model, error)
	// Lists models.
	ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(context.Context, *DeleteModelRequest) (*longrunning.Operation, error)
	// Updates a model.
	UpdateModel(context.Context, *UpdateModelRequest) (*Model, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	// [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//
	//	will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(context.Context, *DeployModelRequest) (*longrunning.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(context.Context, *UndeployModelRequest) (*longrunning.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(context.Context, *ExportModelRequest) (*longrunning.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)
}

AutoMlServer is the server API for AutoMl service.

BatchPredictInputConfig

type BatchPredictInputConfig struct {

	// The source of the input.
	//
	// Types that are assignable to Source:
	//
	//	*BatchPredictInputConfig_GcsSource
	Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
	// contains filtered or unexported fields
}

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

gs://folder/image1.jpeg
gs://folder/image2.gif
gs://folder/image3.png

Object Detection

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

  gs://folder/image1.jpeg
  gs://folder/image2.gif
  gs://folder/image3.png
</section>

AutoML Video Intelligence

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

gs://folder/video1.mp4,10,40
gs://folder/video1.mp4,20,60
gs://folder/vid2.mov,0,inf

Object Tracking

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

  gs://folder/video1.mp4,10,40
  gs://folder/video1.mp4,20,60
  gs://folder/vid2.mov,0,inf
</section>

AutoML Natural Language

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 10MB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

Sentiment Analysis

One or more CSV files where each line is a single column:

GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 128kB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

Entity Extraction

One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict].

Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique.

Each document JSONL file contains, per line, a proto that wraps a Document proto with input_config set. Each document cannot exceed 2MB in size.

Supported document extensions: .PDF, .TIF, .TIFF

Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed.

Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".):

{
   "id": "my_first_id",
   "text_snippet": { "content": "dog car cat"},
   "text_features": [
     {
       "text_segment": {"start_offset": 4, "end_offset": 6},
       "structural_type": PARAGRAPH,
       "bounding_poly": {
         "normalized_vertices": [
           {"x": 0.1, "y": 0.1},
           {"x": 0.1, "y": 0.3},
           {"x": 0.3, "y": 0.3},
           {"x": 0.3, "y": 0.1},
         ]
       },
     }
   ],
 }\n
 {
   "id": "2",
   "text_snippet": {
     "content": "Extended sample content",
     "mime_type": "text/plain"
   }
 }

Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".):

   {
     "document": {
       "input_config": {
         "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
         }
       }
     }
   }\n
   {
     "document": {
       "input_config": {
         "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
         }
       }
     }
   }
</section>

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or [bigquery_source][BatchPredictInputConfig.bigquery_source].

For gcs_source:

CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

The column names must contain the model's [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted.

Sample rows from a CSV file:

"First Name","Last Name","Dob","Addresses"
"John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
"Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}

For bigquery_source:

The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

</section>

Input field definitions:

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example,

"gs://folder/video.avi".

TIME_SEGMENT_START : (TIME_OFFSET)

Expresses a beginning, inclusive, of a time segment
within an example that has a time dimension
(e.g. video).

TIME_SEGMENT_END : (TIME_OFFSET)

Expresses an end, exclusive, of a time segment within
n example that has a time dimension (e.g. video).

TIME_OFFSET : A number of seconds as measured from the start of an

 example (e.g. video). Fractions are allowed, up to a
 microsecond precision. "inf" is allowed, and it means the end
 of the example.

**Errors:**

If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
prediction does not happen. Regardless of overall success or failure the
per-row failures, up to a certain count cap, will be listed in
Operation.metadata.partial_failures.

func (*BatchPredictInputConfig) Descriptor

func (*BatchPredictInputConfig) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictInputConfig) GetGcsSource

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (*BatchPredictInputConfig) GetSource

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage

func (*BatchPredictInputConfig) ProtoMessage()

func (*BatchPredictInputConfig) ProtoReflect

func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Message

func (*BatchPredictInputConfig) Reset

func (x *BatchPredictInputConfig) Reset()

func (*BatchPredictInputConfig) String

func (x *BatchPredictInputConfig) String() string

BatchPredictInputConfig_GcsSource

type BatchPredictInputConfig_GcsSource struct {
	// Required. The Google Cloud Storage location for the input content.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

BatchPredictOperationMetadata

type BatchPredictOperationMetadata struct {

	// Output only. The input config that was given upon starting this
	// batch predict operation.
	InputConfig *BatchPredictInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// Output only. Information further describing this batch predict's output.
	OutputInfo *BatchPredictOperationMetadata_BatchPredictOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

Details of BatchPredict operation.

func (*BatchPredictOperationMetadata) Descriptor

func (*BatchPredictOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictOperationMetadata.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata) GetInputConfig

func (x *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictOperationMetadata) GetOutputInfo

func (x *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage

func (*BatchPredictOperationMetadata) ProtoMessage()

func (*BatchPredictOperationMetadata) ProtoReflect

func (x *BatchPredictOperationMetadata) ProtoReflect() protoreflect.Message

func (*BatchPredictOperationMetadata) Reset

func (x *BatchPredictOperationMetadata) Reset()

func (*BatchPredictOperationMetadata) String

func (x *BatchPredictOperationMetadata) String() string

BatchPredictOperationMetadata_BatchPredictOutputInfo

type BatchPredictOperationMetadata_BatchPredictOutputInfo struct {

	// The output location into which prediction output is written.
	//
	// Types that are assignable to OutputLocation:
	//
	//	*BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
	OutputLocation isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	// contains filtered or unexported fields
}

Further describes this batch predict's output. Supplements [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictOperationMetadata_BatchPredictOutputInfo.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect() protoreflect.Message

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() string

BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory struct {
	// The full path of the Google Cloud Storage directory created, into which
	// the prediction output is written.
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}

BatchPredictOutputConfig

type BatchPredictOutputConfig struct {

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//
	//	*BatchPredictOutputConfig_GcsDestination
	Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
	// contains filtered or unexported fields
}

Output configuration for BatchPredict Action.

As destination the [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-

For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,...,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,...,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created.

The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty.

Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by classifaction_types parameter in [PredictService.BatchPredictRequest.params][]).
For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)).

The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty.

Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set.
For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,...,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files.

If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,...,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files.

If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,...,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly one google.rpc.Status containing only code and message.
For Tables: Output depends on whether [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination] or [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). Google Cloud Storage case: In the created directory files tables_1.csv, tables_2.csv,..., tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] given on input followed by M target column names in the format of "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]><[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,..., errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having google.rpc.Status represented as a JSON string, and containing only code and message. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name prediction_<model-display-name>_<timestamp-of-prediction-call> where

func (*BatchPredictOutputConfig) Descriptor

func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictOutputConfig) GetDestination

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (*BatchPredictOutputConfig) GetGcsDestination

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage

func (*BatchPredictOutputConfig) ProtoMessage()

func (*BatchPredictOutputConfig) ProtoReflect

func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Message

func (*BatchPredictOutputConfig) Reset

func (x *BatchPredictOutputConfig) Reset()

func (*BatchPredictOutputConfig) String

func (x *BatchPredictOutputConfig) String() string

BatchPredictOutputConfig_GcsDestination

type BatchPredictOutputConfig_GcsDestination struct {
	// Required. The Google Cloud Storage location of the directory where the output is to
	// be written to.
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

BatchPredictRequest

type BatchPredictRequest struct {
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`

	OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`

	Params map[string]string "" /* 153 byte string literal not displayed */

}

Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

func (*BatchPredictRequest) Descriptor

func (*BatchPredictRequest) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictRequest.ProtoReflect.Descriptor instead.

func (*BatchPredictRequest) GetInputConfig

func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictRequest) GetName

func (x *BatchPredictRequest) GetName() string

func (*BatchPredictRequest) GetOutputConfig

func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (*BatchPredictRequest) GetParams

func (x *BatchPredictRequest) GetParams() map[string]string

func (*BatchPredictRequest) ProtoMessage

func (*BatchPredictRequest) ProtoMessage()

func (*BatchPredictRequest) ProtoReflect

func (x *BatchPredictRequest) ProtoReflect() protoreflect.Message

func (*BatchPredictRequest) Reset

func (x *BatchPredictRequest) Reset()

func (*BatchPredictRequest) String

func (x *BatchPredictRequest) String() string

BatchPredictResult

type BatchPredictResult struct {
	Metadata map[string]string "" /* 157 byte string literal not displayed */

}

Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

func (*BatchPredictResult) Descriptor

func (*BatchPredictResult) Descriptor() ([]byte, []int)

Deprecated: Use BatchPredictResult.ProtoReflect.Descriptor instead.

func (*BatchPredictResult) GetMetadata

func (x *BatchPredictResult) GetMetadata() map[string]string

func (*BatchPredictResult) ProtoMessage

func (*BatchPredictResult) ProtoMessage()

func (*BatchPredictResult) ProtoReflect

func (x *BatchPredictResult) ProtoReflect() protoreflect.Message

func (*BatchPredictResult) Reset

func (x *BatchPredictResult) Reset()

func (*BatchPredictResult) String

func (x *BatchPredictResult) String() string

BoundingBoxMetricsEntry

type BoundingBoxMetricsEntry struct {
	IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`

	MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`

	ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry "" /* 135 byte string literal not displayed */

}

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

func (*BoundingBoxMetricsEntry) Descriptor

func (*BoundingBoxMetricsEntry) Descriptor() ([]byte, []int)

Deprecated: Use BoundingBoxMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries

func (x *BoundingBoxMetricsEntry) GetConfidenceMetricsEntries() []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry

func (*BoundingBoxMetricsEntry) GetIouThreshold

func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision

func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry) ProtoReflect

func (x *BoundingBoxMetricsEntry) ProtoReflect() protoreflect.Message

func (*BoundingBoxMetricsEntry) Reset

func (x *BoundingBoxMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry) String

func (x *BoundingBoxMetricsEntry) String() string

BoundingBoxMetricsEntry_ConfidenceMetricsEntry

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry struct {

	// Output only. The confidence threshold value used to compute the metrics.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Recall under the given confidence threshold.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision under the given confidence threshold.
	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// contains filtered or unexported fields
}

Metrics for a single confidence threshold.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

Deprecated: Use BoundingBoxMetricsEntry_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() string

BoundingPoly

type BoundingPoly struct {

	// Output only . The bounding polygon normalized vertices.
	NormalizedVertices []*NormalizedVertex `protobuf:"bytes,2,rep,name=normalized_vertices,json=normalizedVertices,proto3" json:"normalized_vertices,omitempty"`
	// contains filtered or unexported fields
}

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

func (*BoundingPoly) Descriptor

func (*BoundingPoly) Descriptor() ([]byte, []int)

Deprecated: Use BoundingPoly.ProtoReflect.Descriptor instead.

func (*BoundingPoly) GetNormalizedVertices

func (x *BoundingPoly) GetNormalizedVertices() []*NormalizedVertex

func (*BoundingPoly) ProtoMessage

func (*BoundingPoly) ProtoMessage()

func (*BoundingPoly) ProtoReflect

func (x *BoundingPoly) ProtoReflect() protoreflect.Message

func (*BoundingPoly) Reset

func (x *BoundingPoly) Reset()

func (*BoundingPoly) String

func (x *BoundingPoly) String() string

ClassificationAnnotation

type ClassificationAnnotation struct {

	// Output only. A confidence estimate between 0.0 and 1.0. A higher value
	// means greater confidence that the annotation is positive. If a user
	// approves an annotation as negative or positive, the score value remains
	// unchanged. If a user creates an annotation, the score is 0 for negative or
	// 1 for positive.
	Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	// contains filtered or unexported fields
}

Contains annotation details specific to classification.

func (*ClassificationAnnotation) Descriptor

func (*ClassificationAnnotation) Descriptor() ([]byte, []int)

Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.

func (*ClassificationAnnotation) GetScore

func (x *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage

func (*ClassificationAnnotation) ProtoMessage()

func (*ClassificationAnnotation) ProtoReflect

func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Message

func (*ClassificationAnnotation) Reset

func (x *ClassificationAnnotation) Reset()

func (*ClassificationAnnotation) String

func (x *ClassificationAnnotation) String() string

ClassificationEvaluationMetrics

type ClassificationEvaluationMetrics struct {
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`

	AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`

	LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`

	ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry "" /* 129 byte string literal not displayed */

	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`

	AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`

}

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

func (*ClassificationEvaluationMetrics) Descriptor

func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int)

Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics) GetAnnotationSpecId

func (x *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string

func (*ClassificationEvaluationMetrics) GetAuPrc

func (x *ClassificationEvaluationMetrics) GetAuPrc() float32

func (*ClassificationEvaluationMetrics) GetAuRoc

func (x *ClassificationEvaluationMetrics) GetAuRoc() float32

func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry

func (x *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry

func (*ClassificationEvaluationMetrics) GetConfusionMatrix

func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*ClassificationEvaluationMetrics) GetLogLoss

func (x *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (*ClassificationEvaluationMetrics) ProtoReflect

func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics) Reset

func (x *ClassificationEvaluationMetrics) Reset()

func (*ClassificationEvaluationMetrics) String

func (x *ClassificationEvaluationMetrics) String() string

ClassificationEvaluationMetrics_ConfidenceMetricsEntry

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`

	PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`

	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`

	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`

	FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`

	F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`

	RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`

	PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`

	FalsePositiveRateAt1 float32 "" /* 127 byte string literal not displayed */

	F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`

	TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`

	FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`

	FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`

	TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`

}

Metrics for a single confidence threshold.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string

ClassificationEvaluationMetrics_ConfusionMatrix

type ClassificationEvaluationMetrics_ConfusionMatrix struct {

	// Output only. IDs of the annotation specs used in the confusion matrix.
	// For Tables CLASSIFICATION
	// [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
	// only list of [annotation_spec_display_name-s][] is populated.
	AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. Display name of the annotation specs used in the confusion
	// matrix, as they were at the moment of the evaluation. For Tables
	// CLASSIFICATION
	// [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
	// distinct values of the target column at the moment of the model
	// evaluation are populated here.
	DisplayName []string `protobuf:"bytes,3,rep,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. Rows in the confusion matrix. The number of rows is equal to
	// the size of `annotation_spec_id`.
	// `row[i].example_count[j]` is the number of examples that have ground
	// truth of the `annotation_spec_id[i]` and are predicted as
	// `annotation_spec_id[j]` by the model being evaluated.
	Row []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"`
	// contains filtered or unexported fields
}

Confusion matrix of the model running the classification.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int)

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []string

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) String

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string

ClassificationEvaluationMetrics_ConfusionMatrix_Row

type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct {

	// Output only. Value of the specific cell in the confusion matrix.
	// The number of values each row has (i.e. the length of the row) is equal
	// to the length of the `annotation_spec_id` field or, if that one is not
	// populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
	ExampleCount []int32 `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// contains filtered or unexported fields
}

Output only. A row in the confusion matrix.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int)

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string

ClassificationType

type ClassificationType int32

Type of the classification problem.

ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED, ClassificationType_MULTICLASS, ClassificationType_MULTILABEL

const (
	// An un-set value of this enum.
	ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0
	// At most one label is allowed per example.
	ClassificationType_MULTICLASS ClassificationType = 1
	// Multiple labels are allowed for one example.
	ClassificationType_MULTILABEL ClassificationType = 2
)

func (ClassificationType) Descriptor

func (ClassificationType) Descriptor() protoreflect.EnumDescriptor

func (ClassificationType) Enum

func (x ClassificationType) Enum() *ClassificationType

func (ClassificationType) EnumDescriptor

func (ClassificationType) EnumDescriptor() ([]byte, []int)

Deprecated: Use ClassificationType.Descriptor instead.

func (ClassificationType) Number

func (x ClassificationType) Number() protoreflect.EnumNumber

func (ClassificationType) String

func (x ClassificationType) String() string

func (ClassificationType) Type

func (ClassificationType) Type() protoreflect.EnumType

CreateDatasetOperationMetadata

type CreateDatasetOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of CreateDataset operation.

func (*CreateDatasetOperationMetadata) Descriptor

func (*CreateDatasetOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use CreateDatasetOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateDatasetOperationMetadata) ProtoMessage

func (*CreateDatasetOperationMetadata) ProtoMessage()

func (*CreateDatasetOperationMetadata) ProtoReflect

func (x *CreateDatasetOperationMetadata) ProtoReflect() protoreflect.Message

func (*CreateDatasetOperationMetadata) Reset

func (x *CreateDatasetOperationMetadata) Reset()

func (*CreateDatasetOperationMetadata) String

func (x *CreateDatasetOperationMetadata) String() string

CreateDatasetRequest

type CreateDatasetRequest struct {

	// Required. The resource name of the project to create the dataset for.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The dataset to create.
	Dataset *Dataset `protobuf:"bytes,2,opt,name=dataset,proto3" json:"dataset,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].

func (*CreateDatasetRequest) Descriptor

func (*CreateDatasetRequest) Descriptor() ([]byte, []int)

Deprecated: Use CreateDatasetRequest.ProtoReflect.Descriptor instead.

func (*CreateDatasetRequest) GetDataset

func (x *CreateDatasetRequest) GetDataset() *Dataset

func (*CreateDatasetRequest) GetParent

func (x *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage

func (*CreateDatasetRequest) ProtoMessage()

func (*CreateDatasetRequest) ProtoReflect

func (x *CreateDatasetRequest) ProtoReflect() protoreflect.Message

func (*CreateDatasetRequest) Reset

func (x *CreateDatasetRequest) Reset()

func (*CreateDatasetRequest) String

func (x *CreateDatasetRequest) String() string

CreateModelOperationMetadata

type CreateModelOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of CreateModel operation.

func (*CreateModelOperationMetadata) Descriptor

func (*CreateModelOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use CreateModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateModelOperationMetadata) ProtoMessage

func (*CreateModelOperationMetadata) ProtoMessage()

func (*CreateModelOperationMetadata) ProtoReflect

func (x *CreateModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*CreateModelOperationMetadata) Reset

func (x *CreateModelOperationMetadata) Reset()

func (*CreateModelOperationMetadata) String

func (x *CreateModelOperationMetadata) String() string

CreateModelRequest

type CreateModelRequest struct {

	// Required. Resource name of the parent project where the model is being created.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The model to create.
	Model *Model `protobuf:"bytes,4,opt,name=model,proto3" json:"model,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].

func (*CreateModelRequest) Descriptor

func (*CreateModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use CreateModelRequest.ProtoReflect.Descriptor instead.

func (*CreateModelRequest) GetModel

func (x *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent

func (x *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) ProtoReflect

func (x *CreateModelRequest) ProtoReflect() protoreflect.Message

func (*CreateModelRequest) Reset

func (x *CreateModelRequest) Reset()

func (*CreateModelRequest) String

func (x *CreateModelRequest) String() string

Dataset

type Dataset struct {
	DatasetMetadata isDataset_DatasetMetadata `protobuf_oneof:"dataset_metadata"`

	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`

	Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"`

	ExampleCount int32 `protobuf:"varint,21,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`

	CreateTime *timestamppb.Timestamp `protobuf:"bytes,14,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`

	Etag string `protobuf:"bytes,17,opt,name=etag,proto3" json:"etag,omitempty"`

	Labels map[string]string "" /* 154 byte string literal not displayed */

}

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

func (*Dataset) Descriptor

func (*Dataset) Descriptor() ([]byte, []int)

Deprecated: Use Dataset.ProtoReflect.Descriptor instead.

func (*Dataset) GetCreateTime

func (x *Dataset) GetCreateTime() *timestamppb.Timestamp

func (*Dataset) GetDatasetMetadata

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (*Dataset) GetDescription

func (x *Dataset) GetDescription() string

func (*Dataset) GetDisplayName

func (x *Dataset) GetDisplayName() string

func (*Dataset) GetEtag

func (x *Dataset) GetEtag() string

func (*Dataset) GetExampleCount

func (x *Dataset) GetExampleCount() int32

func (*Dataset) GetImageClassificationDatasetMetadata

func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (*Dataset) GetImageObjectDetectionDatasetMetadata

func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (*Dataset) GetLabels

func (x *Dataset) GetLabels() map[string]string

func (*Dataset) GetName

func (x *Dataset) GetName() string

func (*Dataset) GetTextClassificationDatasetMetadata

func (x *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadata

func (*Dataset) GetTextExtractionDatasetMetadata

func (x *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadata

func (*Dataset) GetTextSentimentDatasetMetadata

func (x *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadata

func (*Dataset) GetTranslationDatasetMetadata

func (x *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadata

func (*Dataset) ProtoMessage

func (*Dataset) ProtoMessage()

func (*Dataset) ProtoReflect

func (x *Dataset) ProtoReflect() protoreflect.Message

func (*Dataset) Reset

func (x *Dataset) Reset()

func (*Dataset) String

func (x *Dataset) String() string

Dataset_ImageClassificationDatasetMetadata

type Dataset_ImageClassificationDatasetMetadata struct {
	// Metadata for a dataset used for image classification.
	ImageClassificationDatasetMetadata *ImageClassificationDatasetMetadata `protobuf:"bytes,24,opt,name=image_classification_dataset_metadata,json=imageClassificationDatasetMetadata,proto3,oneof"`
}

Dataset_ImageObjectDetectionDatasetMetadata

type Dataset_ImageObjectDetectionDatasetMetadata struct {
	// Metadata for a dataset used for image object detection.
	ImageObjectDetectionDatasetMetadata *ImageObjectDetectionDatasetMetadata `protobuf:"bytes,26,opt,name=image_object_detection_dataset_metadata,json=imageObjectDetectionDatasetMetadata,proto3,oneof"`
}

Dataset_TextClassificationDatasetMetadata

type Dataset_TextClassificationDatasetMetadata struct {
	// Metadata for a dataset used for text classification.
	TextClassificationDatasetMetadata *TextClassificationDatasetMetadata `protobuf:"bytes,25,opt,name=text_classification_dataset_metadata,json=textClassificationDatasetMetadata,proto3,oneof"`
}

Dataset_TextExtractionDatasetMetadata

type Dataset_TextExtractionDatasetMetadata struct {
	// Metadata for a dataset used for text extraction.
	TextExtractionDatasetMetadata *TextExtractionDatasetMetadata `protobuf:"bytes,28,opt,name=text_extraction_dataset_metadata,json=textExtractionDatasetMetadata,proto3,oneof"`
}

Dataset_TextSentimentDatasetMetadata

type Dataset_TextSentimentDatasetMetadata struct {
	// Metadata for a dataset used for text sentiment.
	TextSentimentDatasetMetadata *TextSentimentDatasetMetadata `protobuf:"bytes,30,opt,name=text_sentiment_dataset_metadata,json=textSentimentDatasetMetadata,proto3,oneof"`
}

Dataset_TranslationDatasetMetadata

type Dataset_TranslationDatasetMetadata struct {
	// Metadata for a dataset used for translation.
	TranslationDatasetMetadata *TranslationDatasetMetadata `protobuf:"bytes,23,opt,name=translation_dataset_metadata,json=translationDatasetMetadata,proto3,oneof"`
}

DeleteDatasetRequest

type DeleteDatasetRequest struct {

	// Required. The resource name of the dataset to delete.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].

func (*DeleteDatasetRequest) Descriptor

func (*DeleteDatasetRequest) Descriptor() ([]byte, []int)

Deprecated: Use DeleteDatasetRequest.ProtoReflect.Descriptor instead.

func (*DeleteDatasetRequest) GetName

func (x *DeleteDatasetRequest) GetName() string

func (*DeleteDatasetRequest) ProtoMessage

func (*DeleteDatasetRequest) ProtoMessage()

func (*DeleteDatasetRequest) ProtoReflect

func (x *DeleteDatasetRequest) ProtoReflect() protoreflect.Message

func (*DeleteDatasetRequest) Reset

func (x *DeleteDatasetRequest) Reset()

func (*DeleteDatasetRequest) String

func (x *DeleteDatasetRequest) String() string

DeleteModelRequest

type DeleteModelRequest struct {

	// Required. Resource name of the model being deleted.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].

func (*DeleteModelRequest) Descriptor

func (*DeleteModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.

func (*DeleteModelRequest) GetName

func (x *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) ProtoReflect

func (x *DeleteModelRequest) ProtoReflect() protoreflect.Message

func (*DeleteModelRequest) Reset

func (x *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String

func (x *DeleteModelRequest) String() string

DeleteOperationMetadata

type DeleteOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of operations that perform deletes of any entities.

func (*DeleteOperationMetadata) Descriptor

func (*DeleteOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use DeleteOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeleteOperationMetadata) ProtoMessage

func (*DeleteOperationMetadata) ProtoMessage()

func (*DeleteOperationMetadata) ProtoReflect

func (x *DeleteOperationMetadata) ProtoReflect() protoreflect.Message

func (*DeleteOperationMetadata) Reset

func (x *DeleteOperationMetadata) Reset()

func (*DeleteOperationMetadata) String

func (x *DeleteOperationMetadata) String() string

DeployModelOperationMetadata

type DeployModelOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of DeployModel operation.

func (*DeployModelOperationMetadata) Descriptor

func (*DeployModelOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use DeployModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeployModelOperationMetadata) ProtoMessage

func (*DeployModelOperationMetadata) ProtoMessage()

func (*DeployModelOperationMetadata) ProtoReflect

func (x *DeployModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*DeployModelOperationMetadata) Reset

func (x *DeployModelOperationMetadata) Reset()

func (*DeployModelOperationMetadata) String

func (x *DeployModelOperationMetadata) String() string

DeployModelRequest

type DeployModelRequest struct {

	// The per-domain specific deployment parameters.
	//
	// Types that are assignable to ModelDeploymentMetadata:
	//
	//	*DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata
	//	*DeployModelRequest_ImageClassificationModelDeploymentMetadata
	ModelDeploymentMetadata isDeployModelRequest_ModelDeploymentMetadata `protobuf_oneof:"model_deployment_metadata"`
	// Required. Resource name of the model to deploy.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].

func (*DeployModelRequest) Descriptor

func (*DeployModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use DeployModelRequest.ProtoReflect.Descriptor instead.

func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata

func (x *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadata

func (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata

func (x *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadata

func (*DeployModelRequest) GetModelDeploymentMetadata

func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadata

func (*DeployModelRequest) GetName

func (x *DeployModelRequest) GetName() string

func (*DeployModelRequest) ProtoMessage

func (*DeployModelRequest) ProtoMessage()

func (*DeployModelRequest) ProtoReflect

func (x *DeployModelRequest) ProtoReflect() protoreflect.Message

func (*DeployModelRequest) Reset

func (x *DeployModelRequest) Reset()

func (*DeployModelRequest) String

func (x *DeployModelRequest) String() string

DeployModelRequest_ImageClassificationModelDeploymentMetadata

type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
	ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata "" /* 135 byte string literal not displayed */
}

DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
	ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata "" /* 138 byte string literal not displayed */
}

Document

type Document struct {

	// An input config specifying the content of the document.
	InputConfig *DocumentInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// The plain text version of this document.
	DocumentText *TextSnippet `protobuf:"bytes,2,opt,name=document_text,json=documentText,proto3" json:"document_text,omitempty"`
	// Describes the layout of the document.
	// Sorted by [page_number][].
	Layout []*Document_Layout `protobuf:"bytes,3,rep,name=layout,proto3" json:"layout,omitempty"`
	// The dimensions of the page in the document.
	DocumentDimensions *DocumentDimensions `protobuf:"bytes,4,opt,name=document_dimensions,json=documentDimensions,proto3" json:"document_dimensions,omitempty"`
	// Number of pages in the document.
	PageCount int32 `protobuf:"varint,5,opt,name=page_count,json=pageCount,proto3" json:"page_count,omitempty"`
	// contains filtered or unexported fields
}

A structured text document e.g. a PDF.

func (*Document) Descriptor

func (*Document) Descriptor() ([]byte, []int)

Deprecated: Use Document.ProtoReflect.Descriptor instead.

func (*Document) GetDocumentDimensions

func (x *Document) GetDocumentDimensions() *DocumentDimensions

func (*Document) GetDocumentText

func (x *Document) GetDocumentText() *TextSnippet

func (*Document) GetInputConfig

func (x *Document) GetInputConfig() *DocumentInputConfig

func (*Document) GetLayout

func (x *Document) GetLayout() []*Document_Layout

func (*Document) GetPageCount

func (x *Document) GetPageCount() int32

func (*Document) ProtoMessage

func (*Document) ProtoMessage()

func (*Document) ProtoReflect

func (x *Document) ProtoReflect() protoreflect.Message

func (*Document) Reset

func (x *Document) Reset()

func (*Document) String

func (x *Document) String() string

DocumentDimensions

type DocumentDimensions struct {
	Unit DocumentDimensions_DocumentDimensionUnit "" /* 131 byte string literal not displayed */

	Width float32 `protobuf:"fixed32,2,opt,name=width,proto3" json:"width,omitempty"`

	Height float32 `protobuf:"fixed32,3,opt,name=height,proto3" json:"height,omitempty"`

}

Message that describes dimension of a document.

func (*DocumentDimensions) Descriptor

func (*DocumentDimensions) Descriptor() ([]byte, []int)

Deprecated: Use DocumentDimensions.ProtoReflect.Descriptor instead.

func (*DocumentDimensions) GetHeight

func (x *DocumentDimensions) GetHeight() float32

func (*DocumentDimensions) GetUnit

func (x *DocumentDimensions) GetUnit() DocumentDimensions_DocumentDimensionUnit

func (*DocumentDimensions) GetWidth

func (x *DocumentDimensions) GetWidth() float32

func (*DocumentDimensions) ProtoMessage

func (*DocumentDimensions) ProtoMessage()

func (*DocumentDimensions) ProtoReflect

func (x *DocumentDimensions) ProtoReflect() protoreflect.Message

func (*DocumentDimensions) Reset

func (x *DocumentDimensions) Reset()

func (*DocumentDimensions) String

func (x *DocumentDimensions) String() string

DocumentDimensions_DocumentDimensionUnit

type DocumentDimensions_DocumentDimensionUnit int32

Unit of the document dimension.

DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED, DocumentDimensions_INCH, DocumentDimensions_CENTIMETER, DocumentDimensions_POINT

const (
	// Should not be used.
	DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED DocumentDimensions_DocumentDimensionUnit = 0
	// Document dimension is measured in inches.
	DocumentDimensions_INCH DocumentDimensions_DocumentDimensionUnit = 1
	// Document dimension is measured in centimeters.
	DocumentDimensions_CENTIMETER DocumentDimensions_DocumentDimensionUnit = 2
	// Document dimension is measured in points. 72 points = 1 inch.
	DocumentDimensions_POINT DocumentDimensions_DocumentDimensionUnit = 3
)

func (DocumentDimensions_DocumentDimensionUnit) Descriptor

func (DocumentDimensions_DocumentDimensionUnit) Descriptor() protoreflect.EnumDescriptor

func (DocumentDimensions_DocumentDimensionUnit) Enum

func (x DocumentDimensions_DocumentDimensionUnit) Enum() *DocumentDimensions_DocumentDimensionUnit

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor() ([]byte, []int)

Deprecated: Use DocumentDimensions_DocumentDimensionUnit.Descriptor instead.

func (DocumentDimensions_DocumentDimensionUnit) Number

func (x DocumentDimensions_DocumentDimensionUnit) Number() protoreflect.EnumNumber

func (DocumentDimensions_DocumentDimensionUnit) String

func (x DocumentDimensions_DocumentDimensionUnit) String() string

func (DocumentDimensions_DocumentDimensionUnit) Type

func (DocumentDimensions_DocumentDimensionUnit) Type() protoreflect.EnumType

DocumentInputConfig

type DocumentInputConfig struct {

	// The Google Cloud Storage location of the document file. Only a single path
	// should be given.
	//
	// Max supported size: 512MB.
	//
	// Supported extensions: .PDF.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"`
	// contains filtered or unexported fields
}

Input configuration of a [Document][google.cloud.automl.v1.Document].

func (*DocumentInputConfig) Descriptor

func (*DocumentInputConfig) Descriptor() ([]byte, []int)

Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.

func (*DocumentInputConfig) GetGcsSource

func (x *DocumentInputConfig) GetGcsSource() *GcsSource

func (*DocumentInputConfig) ProtoMessage

func (*DocumentInputConfig) ProtoMessage()

func (*DocumentInputConfig) ProtoReflect

func (x *DocumentInputConfig) ProtoReflect() protoreflect.Message

func (*DocumentInputConfig) Reset

func (x *DocumentInputConfig) Reset()

func (*DocumentInputConfig) String

func (x *DocumentInputConfig) String() string

Document_Layout

type Document_Layout struct {
	TextSegment *TextSegment `protobuf:"bytes,1,opt,name=text_segment,json=textSegment,proto3" json:"text_segment,omitempty"`

	PageNumber int32 `protobuf:"varint,2,opt,name=page_number,json=pageNumber,proto3" json:"page_number,omitempty"`

	BoundingPoly *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_poly,json=boundingPoly,proto3" json:"bounding_poly,omitempty"`

	TextSegmentType Document_Layout_TextSegmentType "" /* 169 byte string literal not displayed */

}

Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.

func (*Document_Layout) Descriptor

func (*Document_Layout) Descriptor() ([]byte, []int)

Deprecated: Use Document_Layout.ProtoReflect.Descriptor instead.

func (*Document_Layout) GetBoundingPoly

func (x *Document_Layout) GetBoundingPoly() *BoundingPoly

func (*Document_Layout) GetPageNumber

func (x *Document_Layout) GetPageNumber() int32

func (*Document_Layout) GetTextSegment

func (x *Document_Layout) GetTextSegment() *TextSegment

func (*Document_Layout) GetTextSegmentType

func (x *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentType

func (*Document_Layout) ProtoMessage

func (*Document_Layout) ProtoMessage()

func (*Document_Layout) ProtoReflect

func (x *Document_Layout) ProtoReflect() protoreflect.Message

func (*Document_Layout) Reset

func (x *Document_Layout) Reset()

func (*Document_Layout) String

func (x *Document_Layout) String() string

Document_Layout_TextSegmentType

type Document_Layout_TextSegmentType int32

The type of TextSegment in the context of the original document.

Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED, Document_Layout_TOKEN, Document_Layout_PARAGRAPH, Document_Layout_FORM_FIELD, Document_Layout_FORM_FIELD_NAME, Document_Layout_FORM_FIELD_CONTENTS, Document_Layout_TABLE, Document_Layout_TABLE_HEADER, Document_Layout_TABLE_ROW, Document_Layout_TABLE_CELL

const (
	// Should not be used.
	Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED Document_Layout_TextSegmentType = 0
	// The text segment is a token. e.g. word.
	Document_Layout_TOKEN Document_Layout_TextSegmentType = 1
	// The text segment is a paragraph.
	Document_Layout_PARAGRAPH Document_Layout_TextSegmentType = 2
	// The text segment is a form field.
	Document_Layout_FORM_FIELD Document_Layout_TextSegmentType = 3
	// The text segment is the name part of a form field. It will be treated
	// as child of another FORM_FIELD TextSegment if its span is subspan of
	// another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_NAME Document_Layout_TextSegmentType = 4
	// The text segment is the text content part of a form field. It will be
	// treated as child of another FORM_FIELD TextSegment if its span is
	// subspan of another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_CONTENTS Document_Layout_TextSegmentType = 5
	// The text segment is a whole table, including headers, and all rows.
	Document_Layout_TABLE Document_Layout_TextSegmentType = 6
	// The text segment is a table's headers. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_HEADER Document_Layout_TextSegmentType = 7
	// The text segment is a row in table. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_ROW Document_Layout_TextSegmentType = 8
	// The text segment is a cell in table. It will be treated as child of
	// another TABLE_ROW TextSegment if its span is subspan of another
	// TextSegment with type TABLE_ROW.
	Document_Layout_TABLE_CELL Document_Layout_TextSegmentType = 9
)

func (Document_Layout_TextSegmentType) Descriptor

func (Document_Layout_TextSegmentType) Descriptor() protoreflect.EnumDescriptor

func (Document_Layout_TextSegmentType) Enum

func (x Document_Layout_TextSegmentType) Enum() *Document_Layout_TextSegmentType

func (Document_Layout_TextSegmentType) EnumDescriptor

func (Document_Layout_TextSegmentType) EnumDescriptor() ([]byte, []int)

Deprecated: Use Document_Layout_TextSegmentType.Descriptor instead.

func (Document_Layout_TextSegmentType) Number

func (x Document_Layout_TextSegmentType) Number() protoreflect.EnumNumber

func (Document_Layout_TextSegmentType) String

func (x Document_Layout_TextSegmentType) String() string

func (Document_Layout_TextSegmentType) Type

func (Document_Layout_TextSegmentType) Type() protoreflect.EnumType

ExamplePayload

type ExamplePayload struct {

	// Required. The example data.
	//
	// Types that are assignable to Payload:
	//
	//	*ExamplePayload_Image
	//	*ExamplePayload_TextSnippet
	//	*ExamplePayload_Document
	Payload isExamplePayload_Payload `protobuf_oneof:"payload"`
	// contains filtered or unexported fields
}

Example data used for training or prediction.

func (*ExamplePayload) Descriptor

func (*ExamplePayload) Descriptor() ([]byte, []int)

Deprecated: Use ExamplePayload.ProtoReflect.Descriptor instead.

func (*ExamplePayload) GetDocument

func (x *ExamplePayload) GetDocument() *Document

func (*ExamplePayload) GetImage

func (x *ExamplePayload) GetImage() *Image

func (*ExamplePayload) GetPayload

func (m *ExamplePayload) GetPayload() isExamplePayload_Payload

func (*ExamplePayload) GetTextSnippet

func (x *ExamplePayload) GetTextSnippet() *TextSnippet

func (*ExamplePayload) ProtoMessage

func (*ExamplePayload) ProtoMessage()

func (*ExamplePayload) ProtoReflect

func (x *ExamplePayload) ProtoReflect() protoreflect.Message

func (*ExamplePayload) Reset

func (x *ExamplePayload) Reset()

func (*ExamplePayload) String

func (x *ExamplePayload) String() string

ExamplePayload_Document

type ExamplePayload_Document struct {
	// Example document.
	Document *Document `protobuf:"bytes,4,opt,name=document,proto3,oneof"`
}

ExamplePayload_Image

type ExamplePayload_Image struct {
	// Example image.
	Image *Image `protobuf:"bytes,1,opt,name=image,proto3,oneof"`
}

ExamplePayload_TextSnippet

type ExamplePayload_TextSnippet struct {
	// Example text.
	TextSnippet *TextSnippet `protobuf:"bytes,2,opt,name=text_snippet,json=textSnippet,proto3,oneof"`
}

ExportDataOperationMetadata

type ExportDataOperationMetadata struct {

	// Output only. Information further describing this export data's output.
	OutputInfo *ExportDataOperationMetadata_ExportDataOutputInfo `protobuf:"bytes,1,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

Details of ExportData operation.

func (*ExportDataOperationMetadata) Descriptor

func (*ExportDataOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ExportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata) GetOutputInfo

func (x *ExportDataOperationMetadata) GetOutputInfo() *ExportDataOperationMetadata_ExportDataOutputInfo

func (*ExportDataOperationMetadata) ProtoMessage

func (*ExportDataOperationMetadata) ProtoMessage()

func (*ExportDataOperationMetadata) ProtoReflect

func (x *ExportDataOperationMetadata) ProtoReflect() protoreflect.Message

func (*ExportDataOperationMetadata) Reset

func (x *ExportDataOperationMetadata) Reset()

func (*ExportDataOperationMetadata) String

func (x *ExportDataOperationMetadata) String() string

ExportDataOperationMetadata_ExportDataOutputInfo

type ExportDataOperationMetadata_ExportDataOutputInfo struct {

	// The output location to which the exported data is written.
	//
	// Types that are assignable to OutputLocation:
	//
	//	*ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
	OutputLocation isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	// contains filtered or unexported fields
}

Further describes this export data's output. Supplements [OutputConfig][google.cloud.automl.v1.OutputConfig].

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor() ([]byte, []int)

Deprecated: Use ExportDataOperationMetadata_ExportDataOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect() protoreflect.Message

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) Reset()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) String

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) String() string

ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory struct {
	// The full path of the Google Cloud Storage directory created, into which
	// the exported data is written.
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}

ExportDataRequest

type ExportDataRequest struct {

	// Required. The resource name of the dataset.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location.
	OutputConfig *OutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].

func (*ExportDataRequest) Descriptor

func (*ExportDataRequest) Descriptor() ([]byte, []int)

Deprecated: Use ExportDataRequest.ProtoReflect.Descriptor instead.

func (*ExportDataRequest) GetName

func (x *ExportDataRequest) GetName() string

func (*ExportDataRequest) GetOutputConfig

func (x *ExportDataRequest) GetOutputConfig() *OutputConfig

func (*ExportDataRequest) ProtoMessage

func (*ExportDataRequest) ProtoMessage()

func (*ExportDataRequest) ProtoReflect

func (x *ExportDataRequest) ProtoReflect() protoreflect.Message

func (*ExportDataRequest) Reset

func (x *ExportDataRequest) Reset()

func (*ExportDataRequest) String

func (x *ExportDataRequest) String() string

ExportModelOperationMetadata

type ExportModelOperationMetadata struct {

	// Output only. Information further describing the output of this model
	// export.
	OutputInfo *ExportModelOperationMetadata_ExportModelOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

Details of ExportModel operation.

func (*ExportModelOperationMetadata) Descriptor

func (*ExportModelOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ExportModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata) GetOutputInfo

func (x *ExportModelOperationMetadata) GetOutputInfo() *ExportModelOperationMetadata_ExportModelOutputInfo

func (*ExportModelOperationMetadata) ProtoMessage

func (*ExportModelOperationMetadata) ProtoMessage()

func (*ExportModelOperationMetadata) ProtoReflect

func (x *ExportModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*ExportModelOperationMetadata) Reset

func (x *ExportModelOperationMetadata) Reset()

func (*ExportModelOperationMetadata) String

func (x *ExportModelOperationMetadata) String() string

ExportModelOperationMetadata_ExportModelOutputInfo

type ExportModelOperationMetadata_ExportModelOutputInfo struct {

	// The full path of the Google Cloud Storage directory created, into which
	// the model will be exported.
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3" json:"gcs_output_directory,omitempty"`
	// contains filtered or unexported fields
}

Further describes the output of model export. Supplements [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor() ([]byte, []int)

Deprecated: Use ExportModelOperationMetadata_ExportModelOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory() string

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect() protoreflect.Message

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) Reset()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) String

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) String() string

ExportModelRequest

type ExportModelRequest struct {

	// Required. The resource name of the model to export.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location and configuration.
	OutputConfig *ModelExportOutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.

func (*ExportModelRequest) Descriptor

func (*ExportModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use ExportModelRequest.ProtoReflect.Descriptor instead.

func (*ExportModelRequest) GetName

func (x *ExportModelRequest) GetName() string

func (*ExportModelRequest) GetOutputConfig

func (x *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfig

func (*ExportModelRequest) ProtoMessage

func (*ExportModelRequest) ProtoMessage()

func (*ExportModelRequest) ProtoReflect

func (x *ExportModelRequest) ProtoReflect() protoreflect.Message

func (*ExportModelRequest) Reset

func (x *ExportModelRequest) Reset()

func (*ExportModelRequest) String

func (x *ExportModelRequest) String() string

GcsDestination

type GcsDestination struct {

	// Required. Google Cloud Storage URI to output directory, up to 2000
	// characters long.
	// Accepted forms:
	// * Prefix path: gs://bucket/directory
	// The requesting user must have write permission to the bucket.
	// The directory is created if it doesn't exist.
	OutputUriPrefix string `protobuf:"bytes,1,opt,name=output_uri_prefix,json=outputUriPrefix,proto3" json:"output_uri_prefix,omitempty"`
	// contains filtered or unexported fields
}

The Google Cloud Storage location where the output is to be written to.

func (*GcsDestination) Descriptor

func (*GcsDestination) Descriptor() ([]byte, []int)

Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.

func (*GcsDestination) GetOutputUriPrefix

func (x *GcsDestination) GetOutputUriPrefix() string

func (*GcsDestination) ProtoMessage

func (*GcsDestination) ProtoMessage()

func (*GcsDestination) ProtoReflect

func (x *GcsDestination) ProtoReflect() protoreflect.Message

func (*GcsDestination) Reset

func (x *GcsDestination) Reset()

func (*GcsDestination) String

func (x *GcsDestination) String() string

GcsSource

type GcsSource struct {

	// Required. Google Cloud Storage URIs to input files, up to 2000
	// characters long. Accepted forms:
	// * Full object path, e.g. gs://bucket/directory/object.csv
	InputUris []string `protobuf:"bytes,1,rep,name=input_uris,json=inputUris,proto3" json:"input_uris,omitempty"`
	// contains filtered or unexported fields
}

The Google Cloud Storage location for the input content.

func (*GcsSource) Descriptor

func (*GcsSource) Descriptor() ([]byte, []int)

Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.

func (*GcsSource) GetInputUris

func (x *GcsSource) GetInputUris() []string

func (*GcsSource) ProtoMessage

func (*GcsSource) ProtoMessage()

func (*GcsSource) ProtoReflect

func (x *GcsSource) ProtoReflect() protoreflect.Message

func (*GcsSource) Reset

func (x *GcsSource) Reset()

func (*GcsSource) String

func (x *GcsSource) String() string

GetAnnotationSpecRequest

type GetAnnotationSpecRequest struct {

	// Required. The resource name of the annotation spec to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].

func (*GetAnnotationSpecRequest) Descriptor

func (*GetAnnotationSpecRequest) Descriptor() ([]byte, []int)

Deprecated: Use GetAnnotationSpecRequest.ProtoReflect.Descriptor instead.

func (*GetAnnotationSpecRequest) GetName

func (x *GetAnnotationSpecRequest) GetName() string

func (*GetAnnotationSpecRequest) ProtoMessage

func (*GetAnnotationSpecRequest) ProtoMessage()

func (*GetAnnotationSpecRequest) ProtoReflect

func (x *GetAnnotationSpecRequest) ProtoReflect() protoreflect.Message

func (*GetAnnotationSpecRequest) Reset

func (x *GetAnnotationSpecRequest) Reset()

func (*GetAnnotationSpecRequest) String

func (x *GetAnnotationSpecRequest) String() string

GetDatasetRequest

type GetDatasetRequest struct {

	// Required. The resource name of the dataset to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].

func (*GetDatasetRequest) Descriptor

func (*GetDatasetRequest) Descriptor() ([]byte, []int)

Deprecated: Use GetDatasetRequest.ProtoReflect.Descriptor instead.

func (*GetDatasetRequest) GetName

func (x *GetDatasetRequest) GetName() string

func (*GetDatasetRequest) ProtoMessage

func (*GetDatasetRequest) ProtoMessage()

func (*GetDatasetRequest) ProtoReflect

func (x *GetDatasetRequest) ProtoReflect() protoreflect.Message

func (*GetDatasetRequest) Reset

func (x *GetDatasetRequest) Reset()

func (*GetDatasetRequest) String

func (x *GetDatasetRequest) String() string

GetModelEvaluationRequest

type GetModelEvaluationRequest struct {

	// Required. Resource name for the model evaluation.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].

func (*GetModelEvaluationRequest) Descriptor

func (*GetModelEvaluationRequest) Descriptor() ([]byte, []int)

Deprecated: Use GetModelEvaluationRequest.ProtoReflect.Descriptor instead.

func (*GetModelEvaluationRequest) GetName

func (x *GetModelEvaluationRequest) GetName() string

func (*GetModelEvaluationRequest) ProtoMessage

func (*GetModelEvaluationRequest) ProtoMessage()

func (*GetModelEvaluationRequest) ProtoReflect

func (x *GetModelEvaluationRequest) ProtoReflect() protoreflect.Message

func (*GetModelEvaluationRequest) Reset

func (x *GetModelEvaluationRequest) Reset()

func (*GetModelEvaluationRequest) String

func (x *GetModelEvaluationRequest) String() string

GetModelRequest

type GetModelRequest struct {

	// Required. Resource name of the model.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].

func (*GetModelRequest) Descriptor

func (*GetModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.

func (*GetModelRequest) GetName

func (x *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) ProtoReflect

func (x *GetModelRequest) ProtoReflect() protoreflect.Message

func (*GetModelRequest) Reset

func (x *GetModelRequest) Reset()

func (*GetModelRequest) String

func (x *GetModelRequest) String() string

Image

type Image struct {

	// Input only. The data representing the image.
	// For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set .
	//
	// Types that are assignable to Data:
	//
	//	*Image_ImageBytes
	Data isImage_Data `protobuf_oneof:"data"`
	// Output only. HTTP URI to the thumbnail image.
	ThumbnailUri string `protobuf:"bytes,4,opt,name=thumbnail_uri,json=thumbnailUri,proto3" json:"thumbnail_uri,omitempty"`
	// contains filtered or unexported fields
}

A representation of an image. Only images up to 30MB in size are supported.

func (*Image) Descriptor

func (*Image) Descriptor() ([]byte, []int)

Deprecated: Use Image.ProtoReflect.Descriptor instead.

func (*Image) GetData

func (m *Image) GetData() isImage_Data

func (*Image) GetImageBytes

func (x *Image) GetImageBytes() []byte

func (*Image) GetThumbnailUri

func (x *Image) GetThumbnailUri() string

func (*Image) ProtoMessage

func (*Image) ProtoMessage()

func (*Image) ProtoReflect

func (x *Image) ProtoReflect() protoreflect.Message

func (*Image) Reset

func (x *Image) Reset()

func (*Image) String

func (x *Image) String() string

ImageClassificationDatasetMetadata

type ImageClassificationDatasetMetadata struct {
	ClassificationType ClassificationType "" /* 163 byte string literal not displayed */

}

Dataset metadata that is specific to image classification.

func (*ImageClassificationDatasetMetadata) Descriptor

func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationDatasetMetadata) GetClassificationType

func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*ImageClassificationDatasetMetadata) ProtoMessage

func (*ImageClassificationDatasetMetadata) ProtoMessage()

func (*ImageClassificationDatasetMetadata) ProtoReflect

func (x *ImageClassificationDatasetMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationDatasetMetadata) Reset

func (x *ImageClassificationDatasetMetadata) Reset()

func (*ImageClassificationDatasetMetadata) String

func (x *ImageClassificationDatasetMetadata) String() string

ImageClassificationModelDeploymentMetadata

type ImageClassificationModelDeploymentMetadata struct {

	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	// [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// contains filtered or unexported fields
}

Model deployment metadata specific to Image Classification.

func (*ImageClassificationModelDeploymentMetadata) Descriptor

func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelDeploymentMetadata) GetNodeCount

func (x *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage()

func (*ImageClassificationModelDeploymentMetadata) ProtoReflect

func (x *ImageClassificationModelDeploymentMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationModelDeploymentMetadata) Reset

func (x *ImageClassificationModelDeploymentMetadata) Reset()

func (*ImageClassificationModelDeploymentMetadata) String

func (x *ImageClassificationModelDeploymentMetadata) String() string

ImageClassificationModelMetadata

type ImageClassificationModelMetadata struct {
	BaseModelId string `protobuf:"bytes,1,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`

	TrainBudgetMilliNodeHours int64 "" /* 144 byte string literal not displayed */

	TrainCostMilliNodeHours int64 "" /* 138 byte string literal not displayed */

	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`

	ModelType string `protobuf:"bytes,7,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`

	NodeQps float64 `protobuf:"fixed64,13,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`

	NodeCount int64 `protobuf:"varint,14,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`

}

Model metadata for image classification.

func (*ImageClassificationModelMetadata) Descriptor

func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelMetadata) GetBaseModelId

func (x *ImageClassificationModelMetadata) GetBaseModelId() string

func (*ImageClassificationModelMetadata) GetModelType

func (x *ImageClassificationModelMetadata) GetModelType() string

func (*ImageClassificationModelMetadata) GetNodeCount

func (x *ImageClassificationModelMetadata) GetNodeCount() int64

func (*ImageClassificationModelMetadata) GetNodeQps

func (x *ImageClassificationModelMetadata) GetNodeQps() float64

func (*ImageClassificationModelMetadata) GetStopReason

func (x *ImageClassificationModelMetadata) GetStopReason() string

func (*ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours

func (x *ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageClassificationModelMetadata) GetTrainCostMilliNodeHours

func (x *ImageClassificationModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageClassificationModelMetadata) ProtoMessage

func (*ImageClassificationModelMetadata) ProtoMessage()

func (*ImageClassificationModelMetadata) ProtoReflect

func (x *ImageClassificationModelMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationModelMetadata) Reset

func (x *ImageClassificationModelMetadata) Reset()

func (*ImageClassificationModelMetadata) String

func (x *ImageClassificationModelMetadata) String() string

ImageObjectDetectionAnnotation

type ImageObjectDetectionAnnotation struct {

	// Output only. The rectangle representing the object location.
	BoundingBox *BoundingPoly `protobuf:"bytes,1,opt,name=bounding_box,json=boundingBox,proto3" json:"bounding_box,omitempty"`
	// Output only. The confidence that this annotation is positive for the parent example,
	// value in [0, 1], higher means higher positivity confidence.
	Score float32 `protobuf:"fixed32,2,opt,name=score,proto3" json:"score,omitempty"`
	// contains filtered or unexported fields
}

Annotation details for image object detection.

func (*ImageObjectDetectionAnnotation) Descriptor

func (*ImageObjectDetectionAnnotation) Descriptor() ([]byte, []int)

Deprecated: Use ImageObjectDetectionAnnotation.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionAnnotation) GetBoundingBox

func (x *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPoly

func (*ImageObjectDetectionAnnotation) GetScore

func (x *ImageObjectDetectionAnnotation) GetScore() float32

func (*ImageObjectDetectionAnnotation) ProtoMessage

func (*ImageObjectDetectionAnnotation) ProtoMessage()

func (*ImageObjectDetectionAnnotation) ProtoReflect

func (x *ImageObjectDetectionAnnotation) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionAnnotation) Reset

func (x *ImageObjectDetectionAnnotation) Reset()

func (*ImageObjectDetectionAnnotation) String

func (x *ImageObjectDetectionAnnotation) String() string

ImageObjectDetectionDatasetMetadata

type ImageObjectDetectionDatasetMetadata struct {
	// contains filtered or unexported fields
}

Dataset metadata specific to image object detection.

func (*ImageObjectDetectionDatasetMetadata) Descriptor

func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()

func (*ImageObjectDetectionDatasetMetadata) ProtoReflect

func (x *ImageObjectDetectionDatasetMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionDatasetMetadata) Reset

func (x *ImageObjectDetectionDatasetMetadata) Reset()

func (*ImageObjectDetectionDatasetMetadata) String

func (x *ImageObjectDetectionDatasetMetadata) String() string

ImageObjectDetectionEvaluationMetrics

type ImageObjectDetectionEvaluationMetrics struct {
	EvaluatedBoundingBoxCount int32 "" /* 141 byte string literal not displayed */

	BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry "" /* 140 byte string literal not displayed */

	BoundingBoxMeanAveragePrecision float32 "" /* 162 byte string literal not displayed */

}

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

func (*ImageObjectDetectionEvaluationMetrics) Descriptor

func (*ImageObjectDetectionEvaluationMetrics) Descriptor() ([]byte, []int)

Deprecated: Use ImageObjectDetectionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntry

func (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount

func (x *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()

func (*ImageObjectDetectionEvaluationMetrics) ProtoReflect

func (x *ImageObjectDetectionEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionEvaluationMetrics) Reset

func (x *ImageObjectDetectionEvaluationMetrics) Reset()

func (*ImageObjectDetectionEvaluationMetrics) String

func (x *ImageObjectDetectionEvaluationMetrics) String() string

ImageObjectDetectionModelDeploymentMetadata

type ImageObjectDetectionModelDeploymentMetadata struct {

	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	// [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// contains filtered or unexported fields
}

Model deployment metadata specific to Image Object Detection.

func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor

func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount

func (x *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage()

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoReflect

func (x *ImageObjectDetectionModelDeploymentMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionModelDeploymentMetadata) Reset

func (x *ImageObjectDetectionModelDeploymentMetadata) Reset()

func (*ImageObjectDetectionModelDeploymentMetadata) String

func (x *ImageObjectDetectionModelDeploymentMetadata) String() string

ImageObjectDetectionModelMetadata

type ImageObjectDetectionModelMetadata struct {
	ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`

	NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`

	NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`

	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`

	TrainBudgetMilliNodeHours int64 "" /* 143 byte string literal not displayed */

	TrainCostMilliNodeHours int64 "" /* 137 byte string literal not displayed */

}

Model metadata specific to image object detection.

func (*ImageObjectDetectionModelMetadata) Descriptor

func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelMetadata) GetModelType

func (x *ImageObjectDetectionModelMetadata) GetModelType() string

func (*ImageObjectDetectionModelMetadata) GetNodeCount

func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelMetadata) GetNodeQps

func (x *ImageObjectDetectionModelMetadata) GetNodeQps() float64

func (*ImageObjectDetectionModelMetadata) GetStopReason

func (x *ImageObjectDetectionModelMetadata) GetStopReason() string

func (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours

func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours

func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) ProtoMessage

func (*ImageObjectDetectionModelMetadata) ProtoMessage()

func (*ImageObjectDetectionModelMetadata) ProtoReflect

func (x *ImageObjectDetectionModelMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionModelMetadata) Reset

func (x *ImageObjectDetectionModelMetadata) Reset()

func (*ImageObjectDetectionModelMetadata) String

func (x *ImageObjectDetectionModelMetadata) String() string

Image_ImageBytes

type Image_ImageBytes struct {
	// Image content represented as a stream of bytes.
	// Note: As with all `bytes` fields, protobuffers use a pure binary
	// representation, whereas JSON representations use base64.
	ImageBytes []byte `protobuf:"bytes,1,opt,name=image_bytes,json=imageBytes,proto3,oneof"`
}

ImportDataOperationMetadata

type ImportDataOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of ImportData operation.

func (*ImportDataOperationMetadata) Descriptor

func (*ImportDataOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use ImportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ImportDataOperationMetadata) ProtoMessage

func (*ImportDataOperationMetadata) ProtoMessage()

func (*ImportDataOperationMetadata) ProtoReflect

func (x *ImportDataOperationMetadata) ProtoReflect() protoreflect.Message

func (*ImportDataOperationMetadata) Reset

func (x *ImportDataOperationMetadata) Reset()

func (*ImportDataOperationMetadata) String

func (x *ImportDataOperationMetadata) String() string

ImportDataRequest

type ImportDataRequest struct {

	// Required. Dataset name. Dataset must already exist. All imported
	// annotations and examples will be added.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired input location and its domain specific semantics,
	// if any.
	InputConfig *InputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].

func (*ImportDataRequest) Descriptor

func (*ImportDataRequest) Descriptor() ([]byte, []int)

Deprecated: Use ImportDataRequest.ProtoReflect.Descriptor instead.

func (*ImportDataRequest) GetInputConfig

func (x *ImportDataRequest) GetInputConfig() *InputConfig

func (*ImportDataRequest) GetName

func (x *ImportDataRequest) GetName() string

func (*ImportDataRequest) ProtoMessage

func (*ImportDataRequest) ProtoMessage()

func (*ImportDataRequest) ProtoReflect

func (x *ImportDataRequest) ProtoReflect() protoreflect.Message

func (*ImportDataRequest) Reset

func (x *ImportDataRequest) Reset()

func (*ImportDataRequest) String

func (x *ImportDataRequest) String() string

InputConfig

type InputConfig struct {
	Source isInputConfig_Source `protobuf_oneof:"source"`

	Params map[string]string "" /* 153 byte string literal not displayed */

}

Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action.

The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification

See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... * ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following: * TRAIN - Rows in this file are used to train the model. * TEST - Rows in this file are used to test the model during training. * UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO. * LABEL - A label that identifies the object in the image. For the MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg

Object Detection

See Preparing your training data for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,) * ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following: * TRAIN - Rows in this file are used to train the model. * TEST - Rows in this file are used to test the model during training. * UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. - LABEL - A label that identifies the object in the image specified by the BOUNDING_BOX. - BOUNDING BOX - The vertices of an object in the example image. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX instances per image are allowed (one BOUNDING_BOX per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. Four sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,,

AutoML Video Intelligence

Classification

See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place of

LABEL,
TIME_SEGMENT_START,TIME_SEGMENT_END

. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,,

Object Tracking

See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it. TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video's frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of

LABEL,
[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX

. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,,

AutoML Natural Language

Entity Extraction

See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATH * ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following: * TRAIN - Rows in this file are used to train the model. * TEST - Rows in this file are used to test the model during training. * UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.. - GCS_FILE_PATH - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from the TRAIN and UNASSIGNED CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl In-line JSONL files In-line .JSONL files contain, per line, a JSON document that wraps a [text_snippet][google.cloud.automl.v1.TextSnippet] field followed by one or more [annotations][google.cloud.automl.v1.AnnotationPayload] fields, which have display_name and text_extraction fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } JSONL files that reference documents .JSONL files contain, per line, a JSON document that wraps a input_config that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } In-line JSONL files with document layout information Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or exportData. In-line .JSONL files for documents contain, per line, a JSON document that wraps a document field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ],

Classification

See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... * ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following: * TRAIN - Rows in this file are used to train the model. * TEST - Rows in this file are used to test the model during training. * UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For the MULTICLASS classification type, at most one LABEL is allowed. The ML_USE and LABEL columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood

Sentiment Analysis

See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT * ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following: * TRAIN - Rows in this file are used to train the model. * TEST - Rows in this file are used to test the model during training. * UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. The ML_USE and SENTIMENT columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP - SENTIMENT - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]

For gcs_source:

CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller.

First three sample rows of a CSV file:

"Id","First Name","Last Name","Dob","Addresses"
"1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
"2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}

For bigquery_source:

An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel.

</section>

Input field definitions:

ML_USE : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED")

Describes how the given example (file) should be used for model
training. "UNASSIGNED" can be used when user has no preference.

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example,

"gs://folder/image1.png".

LABEL : A display name of an object on an image, video etc., e.g. "dog".

Must be up to 32 characters long and can consist only of ASCII
Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
For each label an AnnotationSpec is created which display_name
becomes the label; AnnotationSpecs are given back in predictions.

INSTANCE_ID : A positive integer that identifies a specific instance of a

labeled entity on an example. Used e.g. to track two cars on
a video while being able to tell apart which one is which.

BOUNDING_BOX : (VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,)

A rectangle parallel to the frame of the example (image,
video). If 4 vertices are given they are connected by edges
in the order provided, if 2 are given they are recognized
as diagonally opposite vertices of the rectangle.

VERTEX : (COORDINATE,COORDINATE)

First coordinate is horizontal (x), the second is vertical (y).

COORDINATE : A float in 0 to 1 range, relative to total length of

image or video in given dimension. For fractions the
leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
Point 0,0 is in top left.

TIME_SEGMENT_START : (TIME_OFFSET)

Expresses a beginning, inclusive, of a time segment
within an example that has a time dimension
(e.g. video).

TIME_SEGMENT_END : (TIME_OFFSET)

Expresses an end, exclusive, of a time segment within
n example that has a time dimension (e.g. video).

TIME_OFFSET : A number of seconds as measured from the start of an

example (e.g. video). Fractions are allowed, up to a
microsecond precision. "inf" is allowed, and it means the end
of the example.

TEXT_SNIPPET : The content of a text snippet, UTF-8 encoded, enclosed within

double quotes ("").

DOCUMENT : A field that provides the textual content with document and the layout

 information.

**Errors:**

If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
nothing is imported. Regardless of overall success or failure the per-row
failures, up to a certain count cap, is listed in
Operation.metadata.partial_failures.

func (*InputConfig) Descriptor

func (*InputConfig) Descriptor() ([]byte, []int)

Deprecated: Use InputConfig.ProtoReflect.Descriptor instead.

func (*InputConfig) GetGcsSource

func (x *InputConfig) GetGcsSource() *GcsSource

func (*InputConfig) GetParams

func (x *InputConfig) GetParams() map[string]string

func (*InputConfig) GetSource

func (m *InputConfig) GetSource() isInputConfig_Source

func (*InputConfig) ProtoMessage

func (*InputConfig) ProtoMessage()

func (*InputConfig) ProtoReflect

func (x *InputConfig) ProtoReflect() protoreflect.Message

func (*InputConfig) Reset

func (x *InputConfig) Reset()

func (*InputConfig) String

func (x *InputConfig) String() string

InputConfig_GcsSource

type InputConfig_GcsSource struct {
	// The Google Cloud Storage location for the input content.
	// For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
	// a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

ListDatasetsRequest

type ListDatasetsRequest struct {

	// Required. The resource name of the project from which to list datasets.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// An expression for filtering the results of the request.
	//
	//   - `dataset_metadata` - for existence of the case (e.g.
	//     `image_classification_dataset_metadata:*`). Some examples of using the filter are:
	//
	//   - `translation_dataset_metadata:*` --> The dataset has
	//     `translation_dataset_metadata`.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size. Server may return fewer results than requested.
	// If unspecified, server will pick a default size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return
	// Typically obtained via
	// [ListDatasetsResponse.next_page_token][google.cloud.automl.v1.ListDatasetsResponse.next_page_token] of the previous
	// [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets] call.
	PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

func (*ListDatasetsRequest) Descriptor

func (*ListDatasetsRequest) Descriptor() ([]byte, []int)

Deprecated: Use ListDatasetsRequest.ProtoReflect.Descriptor instead.

func (*ListDatasetsRequest) GetFilter

func (x *ListDatasetsRequest) GetFilter() string

func (*ListDatasetsRequest) GetPageSize

func (x *ListDatasetsRequest) GetPageSize() int32

func (*ListDatasetsRequest) GetPageToken

func (x *ListDatasetsRequest) GetPageToken() string

func (*ListDatasetsRequest) GetParent

func (x *ListDatasetsRequest) GetParent() string

func (*ListDatasetsRequest) ProtoMessage

func (*ListDatasetsRequest) ProtoMessage()

func (*ListDatasetsRequest) ProtoReflect

func (x *ListDatasetsRequest) ProtoReflect() protoreflect.Message

func (*ListDatasetsRequest) Reset

func (x *ListDatasetsRequest) Reset()

func (*ListDatasetsRequest) String

func (x *ListDatasetsRequest) String() string

ListDatasetsResponse

type ListDatasetsResponse struct {

	// The datasets read.
	Datasets []*Dataset `protobuf:"bytes,1,rep,name=datasets,proto3" json:"datasets,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1.ListDatasetsRequest.page_token] to obtain that page.
	NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	// contains filtered or unexported fields
}

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

func (*ListDatasetsResponse) Descriptor

func (*ListDatasetsResponse) Descriptor() ([]byte, []int)

Deprecated: Use ListDatasetsResponse.ProtoReflect.Descriptor instead.

func (*ListDatasetsResponse) GetDatasets

func (x *ListDatasetsResponse) GetDatasets() []*Dataset

func (*ListDatasetsResponse) GetNextPageToken

func (x *ListDatasetsResponse) GetNextPageToken() string

func (*ListDatasetsResponse) ProtoMessage

func (*ListDatasetsResponse) ProtoMessage()

func (*ListDatasetsResponse) ProtoReflect

func (x *ListDatasetsResponse) ProtoReflect() protoreflect.Message

func (*ListDatasetsResponse) Reset

func (x *ListDatasetsResponse) Reset()

func (*ListDatasetsResponse) String

func (x *ListDatasetsResponse) String() string

ListModelEvaluationsRequest

type ListModelEvaluationsRequest struct {

	// Required. Resource name of the model to list the model evaluations for.
	// If modelId is set as "-", this will list model evaluations from across all
	// models of the parent location.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. An expression for filtering the results of the request.
	//
	//   - `annotation_spec_id` - for =, !=  or existence. See example below for
	//     the last.
	//
	// Some examples of using the filter are:
	//
	//   - `annotation_spec_id!=4` --> The model evaluation was done for
	//     annotation spec with ID different than 4.
	//   - `NOT annotation_spec_id:*` --> The model evaluation was done for
	//     aggregate of all annotation specs.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return.
	// Typically obtained via
	// [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1.ListModelEvaluationsResponse.next_page_token] of the previous
	// [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] call.
	PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

func (*ListModelEvaluationsRequest) Descriptor

func (*ListModelEvaluationsRequest) Descriptor() ([]byte, []int)

Deprecated: Use ListModelEvaluationsRequest.ProtoReflect.Descriptor instead.

func (*ListModelEvaluationsRequest) GetFilter

func (x *ListModelEvaluationsRequest) GetFilter() string

func (*ListModelEvaluationsRequest) GetPageSize

func (x *ListModelEvaluationsRequest) GetPageSize() int32

func (*ListModelEvaluationsRequest) GetPageToken

func (x *ListModelEvaluationsRequest) GetPageToken() string

func (*ListModelEvaluationsRequest) GetParent

func (x *ListModelEvaluationsRequest) GetParent() string

func (*ListModelEvaluationsRequest) ProtoMessage

func (*ListModelEvaluationsRequest) ProtoMessage()

func (*ListModelEvaluationsRequest) ProtoReflect

func (x *ListModelEvaluationsRequest) ProtoReflect() protoreflect.Message

func (*ListModelEvaluationsRequest) Reset

func (x *ListModelEvaluationsRequest) Reset()

func (*ListModelEvaluationsRequest) String

func (x *ListModelEvaluationsRequest) String() string

ListModelEvaluationsResponse

type ListModelEvaluationsResponse struct {

	// List of model evaluations in the requested page.
	ModelEvaluation []*ModelEvaluation `protobuf:"bytes,1,rep,name=model_evaluation,json=modelEvaluation,proto3" json:"model_evaluation,omitempty"`
	// A token to retrieve next page of results.
	// Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1.ListModelEvaluationsRequest.page_token] field of a new
	// [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] request to obtain that page.
	NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	// contains filtered or unexported fields
}

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

func (*ListModelEvaluationsResponse) Descriptor

func (*ListModelEvaluationsResponse) Descriptor() ([]byte, []int)

Deprecated: Use ListModelEvaluationsResponse.ProtoReflect.Descriptor instead.

func (*ListModelEvaluationsResponse) GetModelEvaluation

func (x *ListModelEvaluationsResponse) GetModelEvaluation() []*ModelEvaluation

func (*ListModelEvaluationsResponse) GetNextPageToken

func (x *ListModelEvaluationsResponse) GetNextPageToken() string

func (*ListModelEvaluationsResponse) ProtoMessage

func (*ListModelEvaluationsResponse) ProtoMessage()

func (*ListModelEvaluationsResponse) ProtoReflect

func (x *ListModelEvaluationsResponse) ProtoReflect() protoreflect.Message

func (*ListModelEvaluationsResponse) Reset

func (x *ListModelEvaluationsResponse) Reset()

func (*ListModelEvaluationsResponse) String

func (x *ListModelEvaluationsResponse) String() string

ListModelsRequest

type ListModelsRequest struct {

	// Required. Resource name of the project, from which to list the models.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// An expression for filtering the results of the request.
	//
	//   - `model_metadata` - for existence of the case (e.g.
	//     `video_classification_model_metadata:*`).
	//
	//   - `dataset_id` - for = or !=. Some examples of using the filter are:
	//
	//   - `image_classification_model_metadata:*` --> The model has
	//     `image_classification_model_metadata`.
	//
	//   - `dataset_id=5` --> The model was created from a dataset with ID 5.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return
	// Typically obtained via
	// [ListModelsResponse.next_page_token][google.cloud.automl.v1.ListModelsResponse.next_page_token] of the previous
	// [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels] call.
	PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

func (*ListModelsRequest) Descriptor

func (*ListModelsRequest) Descriptor() ([]byte, []int)

Deprecated: Use ListModelsRequest.ProtoReflect.Descriptor instead.

func (*ListModelsRequest) GetFilter

func (x *ListModelsRequest) GetFilter() string

func (*ListModelsRequest) GetPageSize

func (x *ListModelsRequest) GetPageSize() int32

func (*ListModelsRequest) GetPageToken

func (x *ListModelsRequest) GetPageToken() string

func (*ListModelsRequest) GetParent

func (x *ListModelsRequest) GetParent() string

func (*ListModelsRequest) ProtoMessage

func (*ListModelsRequest) ProtoMessage()

func (*ListModelsRequest) ProtoReflect

func (x *ListModelsRequest) ProtoReflect() protoreflect.Message

func (*ListModelsRequest) Reset

func (x *ListModelsRequest) Reset()

func (*ListModelsRequest) String

func (x *ListModelsRequest) String() string

ListModelsResponse

type ListModelsResponse struct {

	// List of models in the requested page.
	Model []*Model `protobuf:"bytes,1,rep,name=model,proto3" json:"model,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListModelsRequest.page_token][google.cloud.automl.v1.ListModelsRequest.page_token] to obtain that page.
	NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	// contains filtered or unexported fields
}

Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

func (*ListModelsResponse) Descriptor

func (*ListModelsResponse) Descriptor() ([]byte, []int)

Deprecated: Use ListModelsResponse.ProtoReflect.Descriptor instead.

func (*ListModelsResponse) GetModel

func (x *ListModelsResponse) GetModel() []*Model

func (*ListModelsResponse) GetNextPageToken

func (x *ListModelsResponse) GetNextPageToken() string

func (*ListModelsResponse) ProtoMessage

func (*ListModelsResponse) ProtoMessage()

func (*ListModelsResponse) ProtoReflect

func (x *ListModelsResponse) ProtoReflect() protoreflect.Message

func (*ListModelsResponse) Reset

func (x *ListModelsResponse) Reset()

func (*ListModelsResponse) String

func (x *ListModelsResponse) String() string

Model

type Model struct {
	ModelMetadata isModel_ModelMetadata `protobuf_oneof:"model_metadata"`

	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`

	DatasetId string `protobuf:"bytes,3,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`

	CreateTime *timestamppb.Timestamp `protobuf:"bytes,7,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`

	UpdateTime *timestamppb.Timestamp `protobuf:"bytes,11,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`

	DeploymentState Model_DeploymentState "" /* 157 byte string literal not displayed */

	Etag string `protobuf:"bytes,10,opt,name=etag,proto3" json:"etag,omitempty"`

	Labels map[string]string "" /* 154 byte string literal not displayed */

}

API proto representing a trained machine learning model.

func (*Model) Descriptor

func (*Model) Descriptor() ([]byte, []int)

Deprecated: Use Model.ProtoReflect.Descriptor instead.

func (*Model) GetCreateTime

func (x *Model) GetCreateTime() *timestamppb.Timestamp

func (*Model) GetDatasetId

func (x *Model) GetDatasetId() string

func (*Model) GetDeploymentState

func (x *Model) GetDeploymentState() Model_DeploymentState

func (*Model) GetDisplayName

func (x *Model) GetDisplayName() string

func (*Model) GetEtag

func (x *Model) GetEtag() string

func (*Model) GetImageClassificationModelMetadata

func (x *Model) GetImageClassificationModelMetadata() *ImageClassificationModelMetadata

func (*Model) GetImageObjectDetectionModelMetadata

func (x *Model) GetImageObjectDetectionModelMetadata() *ImageObjectDetectionModelMetadata

func (*Model) GetLabels

func (x *Model) GetLabels() map[string]string

func (*Model) GetModelMetadata

func (m *Model) GetModelMetadata() isModel_ModelMetadata

func (*Model) GetName

func (x *Model) GetName() string

func (*Model) GetTextClassificationModelMetadata

func (x *Model) GetTextClassificationModelMetadata() *TextClassificationModelMetadata

func (*Model) GetTextExtractionModelMetadata

func (x *Model) GetTextExtractionModelMetadata() *TextExtractionModelMetadata

func (*Model) GetTextSentimentModelMetadata

func (x *Model) GetTextSentimentModelMetadata() *TextSentimentModelMetadata

func (*Model) GetTranslationModelMetadata

func (x *Model) GetTranslationModelMetadata() *TranslationModelMetadata

func (*Model) GetUpdateTime

func (x *Model) GetUpdateTime() *timestamppb.Timestamp

func (*Model) ProtoMessage

func (*Model) ProtoMessage()

func (*Model) ProtoReflect

func (x *Model) ProtoReflect() protoreflect.Message

func (*Model) Reset

func (x *Model) Reset()

func (*Model) String

func (x *Model) String() string

ModelEvaluation

type ModelEvaluation struct {
	Metrics isModelEvaluation_Metrics `protobuf_oneof:"metrics"`

	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	AnnotationSpecId string `protobuf:"bytes,2,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`

	DisplayName string `protobuf:"bytes,15,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`

	CreateTime *timestamppb.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`

	EvaluatedExampleCount int32 "" /* 127 byte string literal not displayed */

}

Evaluation results of a model.

func (*ModelEvaluation) Descriptor

func (*ModelEvaluation) Descriptor() ([]byte, []int)

Deprecated: Use ModelEvaluation.ProtoReflect.Descriptor instead.

func (*ModelEvaluation) GetAnnotationSpecId

func (x *ModelEvaluation) GetAnnotationSpecId() string

func (*ModelEvaluation) GetClassificationEvaluationMetrics

func (x *ModelEvaluation) GetClassificationEvaluationMetrics() *ClassificationEvaluationMetrics

func (*ModelEvaluation) GetCreateTime

func (x *ModelEvaluation) GetCreateTime() *timestamppb.Timestamp

func (*ModelEvaluation) GetDisplayName

func (x *ModelEvaluation) GetDisplayName() string

func (*ModelEvaluation) GetEvaluatedExampleCount

func (x *ModelEvaluation) GetEvaluatedExampleCount() int32

func (*ModelEvaluation) GetImageObjectDetectionEvaluationMetrics

func (x *ModelEvaluation) GetImageObjectDetectionEvaluationMetrics() *ImageObjectDetectionEvaluationMetrics

func (*ModelEvaluation) GetMetrics

func (m *ModelEvaluation) GetMetrics() isModelEvaluation_Metrics

func (*ModelEvaluation) GetName

func (x *ModelEvaluation) GetName() string

func (*ModelEvaluation) GetTextExtractionEvaluationMetrics

func (x *ModelEvaluation) GetTextExtractionEvaluationMetrics() *TextExtractionEvaluationMetrics

func (*ModelEvaluation) GetTextSentimentEvaluationMetrics

func (x *ModelEvaluation) GetTextSentimentEvaluationMetrics() *TextSentimentEvaluationMetrics

func (*ModelEvaluation) GetTranslationEvaluationMetrics

func (x *ModelEvaluation) GetTranslationEvaluationMetrics() *TranslationEvaluationMetrics

func (*ModelEvaluation) ProtoMessage

func (*ModelEvaluation) ProtoMessage()

func (*ModelEvaluation) ProtoReflect

func (x *ModelEvaluation) ProtoReflect() protoreflect.Message

func (*ModelEvaluation) Reset

func (x *ModelEvaluation) Reset()

func (*ModelEvaluation) String

func (x *ModelEvaluation) String() string

ModelEvaluation_ClassificationEvaluationMetrics

type ModelEvaluation_ClassificationEvaluationMetrics struct {
	// Model evaluation metrics for image, text, video and tables
	// classification.
	// Tables problem is considered a classification when the target column
	// is CATEGORY DataType.
	ClassificationEvaluationMetrics *ClassificationEvaluationMetrics `protobuf:"bytes,8,opt,name=classification_evaluation_metrics,json=classificationEvaluationMetrics,proto3,oneof"`
}

ModelEvaluation_ImageObjectDetectionEvaluationMetrics

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics struct {
	// Model evaluation metrics for image object detection.
	ImageObjectDetectionEvaluationMetrics *ImageObjectDetectionEvaluationMetrics `protobuf:"bytes,12,opt,name=image_object_detection_evaluation_metrics,json=imageObjectDetectionEvaluationMetrics,proto3,oneof"`
}

ModelEvaluation_TextExtractionEvaluationMetrics

type ModelEvaluation_TextExtractionEvaluationMetrics struct {
	// Evaluation metrics for text extraction models.
	TextExtractionEvaluationMetrics *TextExtractionEvaluationMetrics `protobuf:"bytes,13,opt,name=text_extraction_evaluation_metrics,json=textExtractionEvaluationMetrics,proto3,oneof"`
}

ModelEvaluation_TextSentimentEvaluationMetrics

type ModelEvaluation_TextSentimentEvaluationMetrics struct {
	// Evaluation metrics for text sentiment models.
	TextSentimentEvaluationMetrics *TextSentimentEvaluationMetrics `protobuf:"bytes,11,opt,name=text_sentiment_evaluation_metrics,json=textSentimentEvaluationMetrics,proto3,oneof"`
}

ModelEvaluation_TranslationEvaluationMetrics

type ModelEvaluation_TranslationEvaluationMetrics struct {
	// Model evaluation metrics for translation.
	TranslationEvaluationMetrics *TranslationEvaluationMetrics `protobuf:"bytes,9,opt,name=translation_evaluation_metrics,json=translationEvaluationMetrics,proto3,oneof"`
}

ModelExportOutputConfig

type ModelExportOutputConfig struct {
	Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`

	ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`

	Params map[string]string "" /* 153 byte string literal not displayed */

}

Output configuration for ModelExport Action.

func (*ModelExportOutputConfig) Descriptor

func (*ModelExportOutputConfig) Descriptor() ([]byte, []int)

Deprecated: Use ModelExportOutputConfig.ProtoReflect.Descriptor instead.

func (*ModelExportOutputConfig) GetDestination

func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination

func (*ModelExportOutputConfig) GetGcsDestination

func (x *ModelExportOutputConfig) GetGcsDestination() *GcsDestination

func (*ModelExportOutputConfig) GetModelFormat

func (x *ModelExportOutputConfig) GetModelFormat() string

func (*ModelExportOutputConfig) GetParams

func (x *ModelExportOutputConfig) GetParams() map[string]string

func (*ModelExportOutputConfig) ProtoMessage

func (*ModelExportOutputConfig) ProtoMessage()

func (*ModelExportOutputConfig) ProtoReflect

func (x *ModelExportOutputConfig) ProtoReflect() protoreflect.Message

func (*ModelExportOutputConfig) Reset

func (x *ModelExportOutputConfig) Reset()

func (*ModelExportOutputConfig) String

func (x *ModelExportOutputConfig) String() string

ModelExportOutputConfig_GcsDestination

type ModelExportOutputConfig_GcsDestination struct {
	// Required. The Google Cloud Storage location where the model is to be written to.
	// This location may only be set for the following model formats:
	//
	//	 "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml".
	//
	//	Under the directory given as the destination a new one with name
	//	"model-export-

Model_DeploymentState

type Model_DeploymentState int32

Deployment state of the model.

Model_DEPLOYMENT_STATE_UNSPECIFIED, Model_DEPLOYED, Model_UNDEPLOYED

const (
	// Should not be used, an un-set enum has this value by default.
	Model_DEPLOYMENT_STATE_UNSPECIFIED Model_DeploymentState = 0
	// Model is deployed.
	Model_DEPLOYED Model_DeploymentState = 1
	// Model is not deployed.
	Model_UNDEPLOYED Model_DeploymentState = 2
)

func (Model_DeploymentState) Descriptor

func (Model_DeploymentState) Descriptor() protoreflect.EnumDescriptor

func (Model_DeploymentState) Enum

func (x Model_DeploymentState) Enum() *Model_DeploymentState

func (Model_DeploymentState) EnumDescriptor

func (Model_DeploymentState) EnumDescriptor() ([]byte, []int)

Deprecated: Use Model_DeploymentState.Descriptor instead.

func (Model_DeploymentState) Number

func (x Model_DeploymentState) Number() protoreflect.EnumNumber

func (Model_DeploymentState) String

func (x Model_DeploymentState) String() string

func (Model_DeploymentState) Type

func (Model_DeploymentState) Type() protoreflect.EnumType

Model_ImageClassificationModelMetadata

type Model_ImageClassificationModelMetadata struct {
	// Metadata for image classification models.
	ImageClassificationModelMetadata *ImageClassificationModelMetadata `protobuf:"bytes,13,opt,name=image_classification_model_metadata,json=imageClassificationModelMetadata,proto3,oneof"`
}

Model_ImageObjectDetectionModelMetadata

type Model_ImageObjectDetectionModelMetadata struct {
	// Metadata for image object detection models.
	ImageObjectDetectionModelMetadata *ImageObjectDetectionModelMetadata `protobuf:"bytes,20,opt,name=image_object_detection_model_metadata,json=imageObjectDetectionModelMetadata,proto3,oneof"`
}

Model_TextClassificationModelMetadata

type Model_TextClassificationModelMetadata struct {
	// Metadata for text classification models.
	TextClassificationModelMetadata *TextClassificationModelMetadata `protobuf:"bytes,14,opt,name=text_classification_model_metadata,json=textClassificationModelMetadata,proto3,oneof"`
}

Model_TextExtractionModelMetadata

type Model_TextExtractionModelMetadata struct {
	// Metadata for text extraction models.
	TextExtractionModelMetadata *TextExtractionModelMetadata `protobuf:"bytes,19,opt,name=text_extraction_model_metadata,json=textExtractionModelMetadata,proto3,oneof"`
}

Model_TextSentimentModelMetadata

type Model_TextSentimentModelMetadata struct {
	// Metadata for text sentiment models.
	TextSentimentModelMetadata *TextSentimentModelMetadata `protobuf:"bytes,22,opt,name=text_sentiment_model_metadata,json=textSentimentModelMetadata,proto3,oneof"`
}

Model_TranslationModelMetadata

type Model_TranslationModelMetadata struct {
	// Metadata for translation models.
	TranslationModelMetadata *TranslationModelMetadata `protobuf:"bytes,15,opt,name=translation_model_metadata,json=translationModelMetadata,proto3,oneof"`
}

NormalizedVertex

type NormalizedVertex struct {

	// Required. Horizontal coordinate.
	X float32 `protobuf:"fixed32,1,opt,name=x,proto3" json:"x,omitempty"`
	// Required. Vertical coordinate.
	Y float32 `protobuf:"fixed32,2,opt,name=y,proto3" json:"y,omitempty"`
	// contains filtered or unexported fields
}

A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.

func (*NormalizedVertex) Descriptor

func (*NormalizedVertex) Descriptor() ([]byte, []int)

Deprecated: Use NormalizedVertex.ProtoReflect.Descriptor instead.

func (*NormalizedVertex) GetX

func (x *NormalizedVertex) GetX() float32

func (*NormalizedVertex) GetY

func (x *NormalizedVertex) GetY() float32

func (*NormalizedVertex) ProtoMessage

func (*NormalizedVertex) ProtoMessage()

func (*NormalizedVertex) ProtoReflect

func (x *NormalizedVertex) ProtoReflect() protoreflect.Message

func (*NormalizedVertex) Reset

func (x *NormalizedVertex) Reset()

func (*NormalizedVertex) String

func (x *NormalizedVertex) String() string

OperationMetadata

type OperationMetadata struct {

	// Ouptut only. Details of specific operation. Even if this field is empty,
	// the presence allows to distinguish different types of operations.
	//
	// Types that are assignable to Details:
	//
	//	*OperationMetadata_DeleteDetails
	//	*OperationMetadata_DeployModelDetails
	//	*OperationMetadata_UndeployModelDetails
	//	*OperationMetadata_CreateModelDetails
	//	*OperationMetadata_CreateDatasetDetails
	//	*OperationMetadata_ImportDataDetails
	//	*OperationMetadata_BatchPredictDetails
	//	*OperationMetadata_ExportDataDetails
	//	*OperationMetadata_ExportModelDetails
	Details isOperationMetadata_Details `protobuf_oneof:"details"`
	// Output only. Progress of operation. Range: [0, 100].
	// Not used currently.
	ProgressPercent int32 `protobuf:"varint,13,opt,name=progress_percent,json=progressPercent,proto3" json:"progress_percent,omitempty"`
	// Output only. Partial failures encountered.
	// E.g. single files that couldn't be read.
	// This field should never exceed 20 entries.
	// Status details field will contain standard GCP error details.
	PartialFailures []*status.Status `protobuf:"bytes,2,rep,name=partial_failures,json=partialFailures,proto3" json:"partial_failures,omitempty"`
	// Output only. Time when the operation was created.
	CreateTime *timestamppb.Timestamp `protobuf:"bytes,3,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Output only. Time when the operation was updated for the last time.
	UpdateTime *timestamppb.Timestamp `protobuf:"bytes,4,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
	// contains filtered or unexported fields
}

Metadata used across all long running operations returned by AutoML API.

func (*OperationMetadata) Descriptor

func (*OperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use OperationMetadata.ProtoReflect.Descriptor instead.

func (*OperationMetadata) GetBatchPredictDetails

func (x *OperationMetadata) GetBatchPredictDetails() *BatchPredictOperationMetadata

func (*OperationMetadata) GetCreateDatasetDetails

func (x *OperationMetadata) GetCreateDatasetDetails() *CreateDatasetOperationMetadata

func (*OperationMetadata) GetCreateModelDetails

func (x *OperationMetadata) GetCreateModelDetails() *CreateModelOperationMetadata

func (*OperationMetadata) GetCreateTime

func (x *OperationMetadata) GetCreateTime() *timestamppb.Timestamp

func (*OperationMetadata) GetDeleteDetails

func (x *OperationMetadata) GetDeleteDetails() *DeleteOperationMetadata

func (*OperationMetadata) GetDeployModelDetails

func (x *OperationMetadata) GetDeployModelDetails() *DeployModelOperationMetadata

func (*OperationMetadata) GetDetails

func (m *OperationMetadata) GetDetails() isOperationMetadata_Details

func (*OperationMetadata) GetExportDataDetails

func (x *OperationMetadata) GetExportDataDetails() *ExportDataOperationMetadata

func (*OperationMetadata) GetExportModelDetails

func (x *OperationMetadata) GetExportModelDetails() *ExportModelOperationMetadata

func (*OperationMetadata) GetImportDataDetails

func (x *OperationMetadata) GetImportDataDetails() *ImportDataOperationMetadata

func (*OperationMetadata) GetPartialFailures

func (x *OperationMetadata) GetPartialFailures() []*status.Status

func (*OperationMetadata) GetProgressPercent

func (x *OperationMetadata) GetProgressPercent() int32

func (*OperationMetadata) GetUndeployModelDetails

func (x *OperationMetadata) GetUndeployModelDetails() *UndeployModelOperationMetadata

func (*OperationMetadata) GetUpdateTime

func (x *OperationMetadata) GetUpdateTime() *timestamppb.Timestamp

func (*OperationMetadata) ProtoMessage

func (*OperationMetadata) ProtoMessage()

func (*OperationMetadata) ProtoReflect

func (x *OperationMetadata) ProtoReflect() protoreflect.Message

func (*OperationMetadata) Reset

func (x *OperationMetadata) Reset()

func (*OperationMetadata) String

func (x *OperationMetadata) String() string

OperationMetadata_BatchPredictDetails

type OperationMetadata_BatchPredictDetails struct {
	// Details of BatchPredict operation.
	BatchPredictDetails *BatchPredictOperationMetadata `protobuf:"bytes,16,opt,name=batch_predict_details,json=batchPredictDetails,proto3,oneof"`
}

OperationMetadata_CreateDatasetDetails

type OperationMetadata_CreateDatasetDetails struct {
	// Details of CreateDataset operation.
	CreateDatasetDetails *CreateDatasetOperationMetadata `protobuf:"bytes,30,opt,name=create_dataset_details,json=createDatasetDetails,proto3,oneof"`
}

OperationMetadata_CreateModelDetails

type OperationMetadata_CreateModelDetails struct {
	// Details of CreateModel operation.
	CreateModelDetails *CreateModelOperationMetadata `protobuf:"bytes,10,opt,name=create_model_details,json=createModelDetails,proto3,oneof"`
}

OperationMetadata_DeleteDetails

type OperationMetadata_DeleteDetails struct {
	// Details of a Delete operation.
	DeleteDetails *DeleteOperationMetadata `protobuf:"bytes,8,opt,name=delete_details,json=deleteDetails,proto3,oneof"`
}

OperationMetadata_DeployModelDetails

type OperationMetadata_DeployModelDetails struct {
	// Details of a DeployModel operation.
	DeployModelDetails *DeployModelOperationMetadata `protobuf:"bytes,24,opt,name=deploy_model_details,json=deployModelDetails,proto3,oneof"`
}

OperationMetadata_ExportDataDetails

type OperationMetadata_ExportDataDetails struct {
	// Details of ExportData operation.
	ExportDataDetails *ExportDataOperationMetadata `protobuf:"bytes,21,opt,name=export_data_details,json=exportDataDetails,proto3,oneof"`
}

OperationMetadata_ExportModelDetails

type OperationMetadata_ExportModelDetails struct {
	// Details of ExportModel operation.
	ExportModelDetails *ExportModelOperationMetadata `protobuf:"bytes,22,opt,name=export_model_details,json=exportModelDetails,proto3,oneof"`
}

OperationMetadata_ImportDataDetails

type OperationMetadata_ImportDataDetails struct {
	// Details of ImportData operation.
	ImportDataDetails *ImportDataOperationMetadata `protobuf:"bytes,15,opt,name=import_data_details,json=importDataDetails,proto3,oneof"`
}

OperationMetadata_UndeployModelDetails

type OperationMetadata_UndeployModelDetails struct {
	// Details of an UndeployModel operation.
	UndeployModelDetails *UndeployModelOperationMetadata `protobuf:"bytes,25,opt,name=undeploy_model_details,json=undeployModelDetails,proto3,oneof"`
}

OutputConfig

type OutputConfig struct {

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//
	//	*OutputConfig_GcsDestination
	Destination isOutputConfig_Destination `protobuf_oneof:"destination"`
	// contains filtered or unexported fields
}

For Translation: CSV file translation.csv, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)
For Tables: Output depends on whether the dataset was imported from Google Cloud Storage or BigQuery. Google Cloud Storage case: [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.gcs_destination] must be set. Exported are CSV file(s) tables_1.csv, tables_2.csv,...,tables_N.csv with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name export_data_<automl-dataset-display-name>_<timestamp-of-export-call> where

func (*OutputConfig) Descriptor

func (*OutputConfig) Descriptor() ([]byte, []int)

Deprecated: Use OutputConfig.ProtoReflect.Descriptor instead.

func (*OutputConfig) GetDestination

func (m *OutputConfig) GetDestination() isOutputConfig_Destination

func (*OutputConfig) GetGcsDestination

func (x *OutputConfig) GetGcsDestination() *GcsDestination

func (*OutputConfig) ProtoMessage

func (*OutputConfig) ProtoMessage()

func (*OutputConfig) ProtoReflect

func (x *OutputConfig) ProtoReflect() protoreflect.Message

func (*OutputConfig) Reset

func (x *OutputConfig) Reset()

func (*OutputConfig) String

func (x *OutputConfig) String() string

OutputConfig_GcsDestination

type OutputConfig_GcsDestination struct {
	// Required. The Google Cloud Storage location where the output is to be written to.
	// For Image Object Detection, Text Extraction, Video Classification and
	// Tables, in the given directory a new directory will be created with name:
	// export_data-

PredictRequest

type PredictRequest struct {
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	Payload *ExamplePayload `protobuf:"bytes,2,opt,name=payload,proto3" json:"payload,omitempty"`

	Params map[string]string "" /* 153 byte string literal not displayed */

}

Request message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].

func (*PredictRequest) Descriptor

func (*PredictRequest) Descriptor() ([]byte, []int)

Deprecated: Use PredictRequest.ProtoReflect.Descriptor instead.

func (*PredictRequest) GetName

func (x *PredictRequest) GetName() string

func (*PredictRequest) GetParams

func (x *PredictRequest) GetParams() map[string]string

func (*PredictRequest) GetPayload

func (x *PredictRequest) GetPayload() *ExamplePayload

func (*PredictRequest) ProtoMessage

func (*PredictRequest) ProtoMessage()

func (*PredictRequest) ProtoReflect

func (x *PredictRequest) ProtoReflect() protoreflect.Message

func (*PredictRequest) Reset

func (x *PredictRequest) Reset()

func (*PredictRequest) String

func (x *PredictRequest) String() string

PredictResponse

type PredictResponse struct {
	Payload []*AnnotationPayload `protobuf:"bytes,1,rep,name=payload,proto3" json:"payload,omitempty"`

	PreprocessedInput *ExamplePayload `protobuf:"bytes,3,opt,name=preprocessed_input,json=preprocessedInput,proto3" json:"preprocessed_input,omitempty"`

	Metadata map[string]string "" /* 157 byte string literal not displayed */

}

Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].

func (*PredictResponse) Descriptor

func (*PredictResponse) Descriptor() ([]byte, []int)

Deprecated: Use PredictResponse.ProtoReflect.Descriptor instead.

func (*PredictResponse) GetMetadata

func (x *PredictResponse) GetMetadata() map[string]string

func (*PredictResponse) GetPayload

func (x *PredictResponse) GetPayload() []*AnnotationPayload

func (*PredictResponse) GetPreprocessedInput

func (x *PredictResponse) GetPreprocessedInput() *ExamplePayload

func (*PredictResponse) ProtoMessage

func (*PredictResponse) ProtoMessage()

func (*PredictResponse) ProtoReflect

func (x *PredictResponse) ProtoReflect() protoreflect.Message

func (*PredictResponse) Reset

func (x *PredictResponse) Reset()

func (*PredictResponse) String

func (x *PredictResponse) String() string

PredictionServiceClient

type PredictionServiceClient interface {
	// Perform an online prediction. The prediction result is directly
	// returned in the response.
	// Available for following ML scenarios, and their expected request payloads:
	//
	// # AutoML Vision Classification
	//
	// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
	//
	// # AutoML Vision Object Detection
	//
	// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
	//
	// # AutoML Natural Language Classification
	//
	// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
	// .PDF, .TIF or .TIFF format with size upto 2MB.
	//
	// # AutoML Natural Language Entity Extraction
	//
	//   - A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document
	//     in .PDF, .TIF or .TIFF format with size upto 20MB.
	//
	// # AutoML Natural Language Sentiment Analysis
	//
	// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
	// .PDF, .TIF or .TIFF format with size upto 2MB.
	//
	// # AutoML Translation
	//
	// * A TextSnippet up to 25,000 characters, UTF-8 encoded.
	//
	// # AutoML Tables
	//
	//   - A row with column values matching
	//     the columns of the model, up to 5MB. Not available for FORECASTING
	//     `prediction_type`.
	Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*PredictResponse, error)
	// Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch
	// prediction result won't be immediately available in the response. Instead,
	// a long running operation object is returned. User can poll the operation
	// result via [GetOperation][google.longrunning.Operations.GetOperation]
	// method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in
	// the [response][google.longrunning.Operation.response] field.
	// Available for following ML scenarios:
	//
	// * AutoML Vision Classification
	// * AutoML Vision Object Detection
	// * AutoML Video Intelligence Classification
	// * AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
	// * AutoML Natural Language Entity Extraction
	// * AutoML Natural Language Sentiment Analysis
	// * AutoML Tables
	BatchPredict(ctx context.Context, in *BatchPredictRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
}

PredictionServiceClient is the client API for PredictionService service.

For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.

func NewPredictionServiceClient

func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient

PredictionServiceServer

type PredictionServiceServer interface {
	// Perform an online prediction. The prediction result is directly
	// returned in the response.
	// Available for following ML scenarios, and their expected request payloads:
	//
	// # AutoML Vision Classification
	//
	// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
	//
	// # AutoML Vision Object Detection
	//
	// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
	//
	// # AutoML Natural Language Classification
	//
	// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
	// .PDF, .TIF or .TIFF format with size upto 2MB.
	//
	// # AutoML Natural Language Entity Extraction
	//
	//   - A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document
	//     in .PDF, .TIF or .TIFF format with size upto 20MB.
	//
	// # AutoML Natural Language Sentiment Analysis
	//
	// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
	// .PDF, .TIF or .TIFF format with size upto 2MB.
	//
	// # AutoML Translation
	//
	// * A TextSnippet up to 25,000 characters, UTF-8 encoded.
	//
	// # AutoML Tables
	//
	//   - A row with column values matching
	//     the columns of the model, up to 5MB. Not available for FORECASTING
	//     `prediction_type`.
	Predict(context.Context, *PredictRequest) (*PredictResponse, error)
	// Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch
	// prediction result won't be immediately available in the response. Instead,
	// a long running operation object is returned. User can poll the operation
	// result via [GetOperation][google.longrunning.Operations.GetOperation]
	// method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in
	// the [response][google.longrunning.Operation.response] field.
	// Available for following ML scenarios:
	//
	// * AutoML Vision Classification
	// * AutoML Vision Object Detection
	// * AutoML Video Intelligence Classification
	// * AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
	// * AutoML Natural Language Entity Extraction
	// * AutoML Natural Language Sentiment Analysis
	// * AutoML Tables
	BatchPredict(context.Context, *BatchPredictRequest) (*longrunning.Operation, error)
}

PredictionServiceServer is the server API for PredictionService service.

TextClassificationDatasetMetadata

type TextClassificationDatasetMetadata struct {
	ClassificationType ClassificationType "" /* 163 byte string literal not displayed */

}

Dataset metadata for classification.

func (*TextClassificationDatasetMetadata) Descriptor

func (*TextClassificationDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TextClassificationDatasetMetadata) GetClassificationType

func (x *TextClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*TextClassificationDatasetMetadata) ProtoMessage

func (*TextClassificationDatasetMetadata) ProtoMessage()

func (*TextClassificationDatasetMetadata) ProtoReflect

func (x *TextClassificationDatasetMetadata) ProtoReflect() protoreflect.Message

func (*TextClassificationDatasetMetadata) Reset

func (x *TextClassificationDatasetMetadata) Reset()

func (*TextClassificationDatasetMetadata) String

func (x *TextClassificationDatasetMetadata) String() string

TextClassificationModelMetadata

type TextClassificationModelMetadata struct {
	ClassificationType ClassificationType "" /* 163 byte string literal not displayed */

}

Model metadata that is specific to text classification.

func (*TextClassificationModelMetadata) Descriptor

func (*TextClassificationModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*TextClassificationModelMetadata) GetClassificationType

func (x *TextClassificationModelMetadata) GetClassificationType() ClassificationType

func (*TextClassificationModelMetadata) ProtoMessage

func (*TextClassificationModelMetadata) ProtoMessage()

func (*TextClassificationModelMetadata) ProtoReflect

func (x *TextClassificationModelMetadata) ProtoReflect() protoreflect.Message

func (*TextClassificationModelMetadata) Reset

func (x *TextClassificationModelMetadata) Reset()

func (*TextClassificationModelMetadata) String

func (x *TextClassificationModelMetadata) String() string

TextExtractionAnnotation

type TextExtractionAnnotation struct {

	// Required. Text extraction annotations can either be a text segment or a
	// text relation.
	//
	// Types that are assignable to Annotation:
	//
	//	*TextExtractionAnnotation_TextSegment
	Annotation isTextExtractionAnnotation_Annotation `protobuf_oneof:"annotation"`
	// Output only. A confidence estimate between 0.0 and 1.0. A higher value
	// means greater confidence in correctness of the annotation.
	Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	// contains filtered or unexported fields
}

Annotation for identifying spans of text.

func (*TextExtractionAnnotation) Descriptor

func (*TextExtractionAnnotation) Descriptor() ([]byte, []int)

Deprecated: Use TextExtractionAnnotation.ProtoReflect.Descriptor instead.

func (*TextExtractionAnnotation) GetAnnotation

func (m *TextExtractionAnnotation) GetAnnotation() isTextExtractionAnnotation_Annotation

func (*TextExtractionAnnotation) GetScore

func (x *TextExtractionAnnotation) GetScore() float32

func (*TextExtractionAnnotation) GetTextSegment

func (x *TextExtractionAnnotation) GetTextSegment() *TextSegment

func (*TextExtractionAnnotation) ProtoMessage

func (*TextExtractionAnnotation) ProtoMessage()

func (*TextExtractionAnnotation) ProtoReflect

func (x *TextExtractionAnnotation) ProtoReflect() protoreflect.Message

func (*TextExtractionAnnotation) Reset

func (x *TextExtractionAnnotation) Reset()

func (*TextExtractionAnnotation) String

func (x *TextExtractionAnnotation) String() string

TextExtractionAnnotation_TextSegment

type TextExtractionAnnotation_TextSegment struct {
	// An entity annotation will set this, which is the part of the original
	// text to which the annotation pertains.
	TextSegment *TextSegment `protobuf:"bytes,3,opt,name=text_segment,json=textSegment,proto3,oneof"`
}

TextExtractionDatasetMetadata

type TextExtractionDatasetMetadata struct {
	// contains filtered or unexported fields
}

Dataset metadata that is specific to text extraction

func (*TextExtractionDatasetMetadata) Descriptor

func (*TextExtractionDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextExtractionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TextExtractionDatasetMetadata) ProtoMessage

func (*TextExtractionDatasetMetadata) ProtoMessage()

func (*TextExtractionDatasetMetadata) ProtoReflect

func (x *TextExtractionDatasetMetadata) ProtoReflect() protoreflect.Message

func (*TextExtractionDatasetMetadata) Reset

func (x *TextExtractionDatasetMetadata) Reset()

func (*TextExtractionDatasetMetadata) String

func (x *TextExtractionDatasetMetadata) String() string

TextExtractionEvaluationMetrics

type TextExtractionEvaluationMetrics struct {
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`

	ConfidenceMetricsEntries []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry "" /* 135 byte string literal not displayed */

}

Model evaluation metrics for text extraction problems.

func (*TextExtractionEvaluationMetrics) Descriptor

func (*TextExtractionEvaluationMetrics) Descriptor() ([]byte, []int)

Deprecated: Use TextExtractionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*TextExtractionEvaluationMetrics) GetAuPrc

func (x *TextExtractionEvaluationMetrics) GetAuPrc() float32

func (*TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries

func (x *TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries() []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry

func (*TextExtractionEvaluationMetrics) ProtoMessage

func (*TextExtractionEvaluationMetrics) ProtoMessage()

func (*TextExtractionEvaluationMetrics) ProtoReflect

func (x *TextExtractionEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*TextExtractionEvaluationMetrics) Reset

func (x *TextExtractionEvaluationMetrics) Reset()

func (*TextExtractionEvaluationMetrics) String

func (x *TextExtractionEvaluationMetrics) String() string

TextExtractionEvaluationMetrics_ConfidenceMetricsEntry

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry struct {

	// Output only. The confidence threshold value used to compute the metrics.
	// Only annotations with score of at least this threshold are considered to
	// be ones the model would return.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Recall under the given confidence threshold.
	Recall float32 `protobuf:"fixed32,3,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision under the given confidence threshold.
	Precision float32 `protobuf:"fixed32,4,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,5,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// contains filtered or unexported fields
}

Metrics for a single confidence threshold.

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

Deprecated: Use TextExtractionEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String

func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String() string

TextExtractionModelMetadata

type TextExtractionModelMetadata struct {
	// contains filtered or unexported fields
}

Model metadata that is specific to text extraction.

func (*TextExtractionModelMetadata) Descriptor

func (*TextExtractionModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextExtractionModelMetadata.ProtoReflect.Descriptor instead.

func (*TextExtractionModelMetadata) ProtoMessage

func (*TextExtractionModelMetadata) ProtoMessage()

func (*TextExtractionModelMetadata) ProtoReflect

func (x *TextExtractionModelMetadata) ProtoReflect() protoreflect.Message

func (*TextExtractionModelMetadata) Reset

func (x *TextExtractionModelMetadata) Reset()

func (*TextExtractionModelMetadata) String

func (x *TextExtractionModelMetadata) String() string

TextSegment

type TextSegment struct {

	// Output only. The content of the TextSegment.
	Content string `protobuf:"bytes,3,opt,name=content,proto3" json:"content,omitempty"`
	// Required. Zero-based character index of the first character of the text
	// segment (counting characters from the beginning of the text).
	StartOffset int64 `protobuf:"varint,1,opt,name=start_offset,json=startOffset,proto3" json:"start_offset,omitempty"`
	// Required. Zero-based character index of the first character past the end of
	// the text segment (counting character from the beginning of the text).
	// The character at the end_offset is NOT included in the text segment.
	EndOffset int64 `protobuf:"varint,2,opt,name=end_offset,json=endOffset,proto3" json:"end_offset,omitempty"`
	// contains filtered or unexported fields
}

A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.

func (*TextSegment) Descriptor

func (*TextSegment) Descriptor() ([]byte, []int)

Deprecated: Use TextSegment.ProtoReflect.Descriptor instead.

func (*TextSegment) GetContent

func (x *TextSegment) GetContent() string

func (*TextSegment) GetEndOffset

func (x *TextSegment) GetEndOffset() int64

func (*TextSegment) GetStartOffset

func (x *TextSegment) GetStartOffset() int64

func (*TextSegment) ProtoMessage

func (*TextSegment) ProtoMessage()

func (*TextSegment) ProtoReflect

func (x *TextSegment) ProtoReflect() protoreflect.Message

func (*TextSegment) Reset

func (x *TextSegment) Reset()

func (*TextSegment) String

func (x *TextSegment) String() string

TextSentimentAnnotation

type TextSentimentAnnotation struct {

	// Output only. The sentiment with the semantic, as given to the
	// [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] when populating the dataset from which the model used
	// for the prediction had been trained.
	// The sentiment values are between 0 and
	// Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
	// with higher value meaning more positive sentiment. They are completely
	// relative, i.e. 0 means least positive sentiment and sentiment_max means
	// the most positive from the sentiments present in the train data. Therefore
	//
	//	e.g. if train data had only negative sentiment, then sentiment_max, would
	//
	// be still negative (although least negative).
	// The sentiment shouldn't be confused with "score" or "magnitude"
	// from the previous Natural Language Sentiment Analysis API.
	Sentiment int32 `protobuf:"varint,1,opt,name=sentiment,proto3" json:"sentiment,omitempty"`
	// contains filtered or unexported fields
}

Contains annotation details specific to text sentiment.

func (*TextSentimentAnnotation) Descriptor

func (*TextSentimentAnnotation) Descriptor() ([]byte, []int)

Deprecated: Use TextSentimentAnnotation.ProtoReflect.Descriptor instead.

func (*TextSentimentAnnotation) GetSentiment

func (x *TextSentimentAnnotation) GetSentiment() int32

func (*TextSentimentAnnotation) ProtoMessage

func (*TextSentimentAnnotation) ProtoMessage()

func (*TextSentimentAnnotation) ProtoReflect

func (x *TextSentimentAnnotation) ProtoReflect() protoreflect.Message

func (*TextSentimentAnnotation) Reset

func (x *TextSentimentAnnotation) Reset()

func (*TextSentimentAnnotation) String

func (x *TextSentimentAnnotation) String() string

TextSentimentDatasetMetadata

type TextSentimentDatasetMetadata struct {

	// Required. A sentiment is expressed as an integer ordinal, where higher value
	// means a more positive sentiment. The range of sentiments that will be used
	// is between 0 and sentiment_max (inclusive on both ends), and all the values
	// in the range must be represented in the dataset before a model can be
	// created.
	// sentiment_max value must be between 1 and 10 (inclusive).
	SentimentMax int32 `protobuf:"varint,1,opt,name=sentiment_max,json=sentimentMax,proto3" json:"sentiment_max,omitempty"`
	// contains filtered or unexported fields
}

Dataset metadata for text sentiment.

func (*TextSentimentDatasetMetadata) Descriptor

func (*TextSentimentDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextSentimentDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TextSentimentDatasetMetadata) GetSentimentMax

func (x *TextSentimentDatasetMetadata) GetSentimentMax() int32

func (*TextSentimentDatasetMetadata) ProtoMessage

func (*TextSentimentDatasetMetadata) ProtoMessage()

func (*TextSentimentDatasetMetadata) ProtoReflect

func (x *TextSentimentDatasetMetadata) ProtoReflect() protoreflect.Message

func (*TextSentimentDatasetMetadata) Reset

func (x *TextSentimentDatasetMetadata) Reset()

func (*TextSentimentDatasetMetadata) String

func (x *TextSentimentDatasetMetadata) String() string

TextSentimentEvaluationMetrics

type TextSentimentEvaluationMetrics struct {

	// Output only. Precision.
	Precision float32 `protobuf:"fixed32,1,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. Recall.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,3,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// Output only. Mean absolute error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanAbsoluteError float32 `protobuf:"fixed32,4,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
	// Output only. Mean squared error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanSquaredError float32 `protobuf:"fixed32,5,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"`
	// Output only. Linear weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	LinearKappa float32 `protobuf:"fixed32,6,opt,name=linear_kappa,json=linearKappa,proto3" json:"linear_kappa,omitempty"`
	// Output only. Quadratic weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	QuadraticKappa float32 `protobuf:"fixed32,7,opt,name=quadratic_kappa,json=quadraticKappa,proto3" json:"quadratic_kappa,omitempty"`
	// Output only. Confusion matrix of the evaluation.
	// Only set for the overall model evaluation, not for evaluation of a single
	// annotation spec.
	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,8,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
	// contains filtered or unexported fields
}

Model evaluation metrics for text sentiment problems.

func (*TextSentimentEvaluationMetrics) Descriptor

func (*TextSentimentEvaluationMetrics) Descriptor() ([]byte, []int)

Deprecated: Use TextSentimentEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*TextSentimentEvaluationMetrics) GetConfusionMatrix

func (x *TextSentimentEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*TextSentimentEvaluationMetrics) GetF1Score

func (x *TextSentimentEvaluationMetrics) GetF1Score() float32

func (*TextSentimentEvaluationMetrics) GetLinearKappa

func (x *TextSentimentEvaluationMetrics) GetLinearKappa() float32

func (*TextSentimentEvaluationMetrics) GetMeanAbsoluteError

func (x *TextSentimentEvaluationMetrics) GetMeanAbsoluteError() float32

func (*TextSentimentEvaluationMetrics) GetMeanSquaredError

func (x *TextSentimentEvaluationMetrics) GetMeanSquaredError() float32

func (*TextSentimentEvaluationMetrics) GetPrecision

func (x *TextSentimentEvaluationMetrics) GetPrecision() float32

func (*TextSentimentEvaluationMetrics) GetQuadraticKappa

func (x *TextSentimentEvaluationMetrics) GetQuadraticKappa() float32

func (*TextSentimentEvaluationMetrics) GetRecall

func (x *TextSentimentEvaluationMetrics) GetRecall() float32

func (*TextSentimentEvaluationMetrics) ProtoMessage

func (*TextSentimentEvaluationMetrics) ProtoMessage()

func (*TextSentimentEvaluationMetrics) ProtoReflect

func (x *TextSentimentEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*TextSentimentEvaluationMetrics) Reset

func (x *TextSentimentEvaluationMetrics) Reset()

func (*TextSentimentEvaluationMetrics) String

func (x *TextSentimentEvaluationMetrics) String() string

TextSentimentModelMetadata

type TextSentimentModelMetadata struct {
	// contains filtered or unexported fields
}

Model metadata that is specific to text sentiment.

func (*TextSentimentModelMetadata) Descriptor

func (*TextSentimentModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TextSentimentModelMetadata.ProtoReflect.Descriptor instead.

func (*TextSentimentModelMetadata) ProtoMessage

func (*TextSentimentModelMetadata) ProtoMessage()

func (*TextSentimentModelMetadata) ProtoReflect

func (x *TextSentimentModelMetadata) ProtoReflect() protoreflect.Message

func (*TextSentimentModelMetadata) Reset

func (x *TextSentimentModelMetadata) Reset()

func (*TextSentimentModelMetadata) String

func (x *TextSentimentModelMetadata) String() string

TextSnippet

type TextSnippet struct {

	// Required. The content of the text snippet as a string. Up to 250000
	// characters long.
	Content string `protobuf:"bytes,1,opt,name=content,proto3" json:"content,omitempty"`
	// Optional. The format of [content][google.cloud.automl.v1.TextSnippet.content]. Currently the only two allowed
	// values are "text/html" and "text/plain". If left blank, the format is
	// automatically determined from the type of the uploaded [content][google.cloud.automl.v1.TextSnippet.content].
	MimeType string `protobuf:"bytes,2,opt,name=mime_type,json=mimeType,proto3" json:"mime_type,omitempty"`
	// Output only. HTTP URI where you can download the content.
	ContentUri string `protobuf:"bytes,4,opt,name=content_uri,json=contentUri,proto3" json:"content_uri,omitempty"`
	// contains filtered or unexported fields
}

A representation of a text snippet.

func (*TextSnippet) Descriptor

func (*TextSnippet) Descriptor() ([]byte, []int)

Deprecated: Use TextSnippet.ProtoReflect.Descriptor instead.

func (*TextSnippet) GetContent

func (x *TextSnippet) GetContent() string

func (*TextSnippet) GetContentUri

func (x *TextSnippet) GetContentUri() string

func (*TextSnippet) GetMimeType

func (x *TextSnippet) GetMimeType() string

func (*TextSnippet) ProtoMessage

func (*TextSnippet) ProtoMessage()

func (*TextSnippet) ProtoReflect

func (x *TextSnippet) ProtoReflect() protoreflect.Message

func (*TextSnippet) Reset

func (x *TextSnippet) Reset()

func (*TextSnippet) String

func (x *TextSnippet) String() string

TranslationAnnotation

type TranslationAnnotation struct {

	// Output only . The translated content.
	TranslatedContent *TextSnippet `protobuf:"bytes,1,opt,name=translated_content,json=translatedContent,proto3" json:"translated_content,omitempty"`
	// contains filtered or unexported fields
}

Annotation details specific to translation.

func (*TranslationAnnotation) Descriptor

func (*TranslationAnnotation) Descriptor() ([]byte, []int)

Deprecated: Use TranslationAnnotation.ProtoReflect.Descriptor instead.

func (*TranslationAnnotation) GetTranslatedContent

func (x *TranslationAnnotation) GetTranslatedContent() *TextSnippet

func (*TranslationAnnotation) ProtoMessage

func (*TranslationAnnotation) ProtoMessage()

func (*TranslationAnnotation) ProtoReflect

func (x *TranslationAnnotation) ProtoReflect() protoreflect.Message

func (*TranslationAnnotation) Reset

func (x *TranslationAnnotation) Reset()

func (*TranslationAnnotation) String

func (x *TranslationAnnotation) String() string

TranslationDatasetMetadata

type TranslationDatasetMetadata struct {

	// Required. The BCP-47 language code of the source language.
	SourceLanguageCode string `protobuf:"bytes,1,opt,name=source_language_code,json=sourceLanguageCode,proto3" json:"source_language_code,omitempty"`
	// Required. The BCP-47 language code of the target language.
	TargetLanguageCode string `protobuf:"bytes,2,opt,name=target_language_code,json=targetLanguageCode,proto3" json:"target_language_code,omitempty"`
	// contains filtered or unexported fields
}

Dataset metadata that is specific to translation.

func (*TranslationDatasetMetadata) Descriptor

func (*TranslationDatasetMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TranslationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TranslationDatasetMetadata) GetSourceLanguageCode

func (x *TranslationDatasetMetadata) GetSourceLanguageCode() string

func (*TranslationDatasetMetadata) GetTargetLanguageCode

func (x *TranslationDatasetMetadata) GetTargetLanguageCode() string

func (*TranslationDatasetMetadata) ProtoMessage

func (*TranslationDatasetMetadata) ProtoMessage()

func (*TranslationDatasetMetadata) ProtoReflect

func (x *TranslationDatasetMetadata) ProtoReflect() protoreflect.Message

func (*TranslationDatasetMetadata) Reset

func (x *TranslationDatasetMetadata) Reset()

func (*TranslationDatasetMetadata) String

func (x *TranslationDatasetMetadata) String() string

TranslationEvaluationMetrics

type TranslationEvaluationMetrics struct {

	// Output only. BLEU score.
	BleuScore float64 `protobuf:"fixed64,1,opt,name=bleu_score,json=bleuScore,proto3" json:"bleu_score,omitempty"`
	// Output only. BLEU score for base model.
	BaseBleuScore float64 `protobuf:"fixed64,2,opt,name=base_bleu_score,json=baseBleuScore,proto3" json:"base_bleu_score,omitempty"`
	// contains filtered or unexported fields
}

Evaluation metrics for the dataset.

func (*TranslationEvaluationMetrics) Descriptor

func (*TranslationEvaluationMetrics) Descriptor() ([]byte, []int)

Deprecated: Use TranslationEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*TranslationEvaluationMetrics) GetBaseBleuScore

func (x *TranslationEvaluationMetrics) GetBaseBleuScore() float64

func (*TranslationEvaluationMetrics) GetBleuScore

func (x *TranslationEvaluationMetrics) GetBleuScore() float64

func (*TranslationEvaluationMetrics) ProtoMessage

func (*TranslationEvaluationMetrics) ProtoMessage()

func (*TranslationEvaluationMetrics) ProtoReflect

func (x *TranslationEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*TranslationEvaluationMetrics) Reset

func (x *TranslationEvaluationMetrics) Reset()

func (*TranslationEvaluationMetrics) String

func (x *TranslationEvaluationMetrics) String() string

TranslationModelMetadata

type TranslationModelMetadata struct {

	// The resource name of the model to use as a baseline to train the custom
	// model. If unset, we use the default base model provided by Google
	// Translate. Format:
	// `projects/{project_id}/locations/{location_id}/models/{model_id}`
	BaseModel string `protobuf:"bytes,1,opt,name=base_model,json=baseModel,proto3" json:"base_model,omitempty"`
	// Output only. Inferred from the dataset.
	// The source language (The BCP-47 language code) that is used for training.
	SourceLanguageCode string `protobuf:"bytes,2,opt,name=source_language_code,json=sourceLanguageCode,proto3" json:"source_language_code,omitempty"`
	// Output only. The target language (The BCP-47 language code) that is used
	// for training.
	TargetLanguageCode string `protobuf:"bytes,3,opt,name=target_language_code,json=targetLanguageCode,proto3" json:"target_language_code,omitempty"`
	// contains filtered or unexported fields
}

Model metadata that is specific to translation.

func (*TranslationModelMetadata) Descriptor

func (*TranslationModelMetadata) Descriptor() ([]byte, []int)

Deprecated: Use TranslationModelMetadata.ProtoReflect.Descriptor instead.

func (*TranslationModelMetadata) GetBaseModel

func (x *TranslationModelMetadata) GetBaseModel() string

func (*TranslationModelMetadata) GetSourceLanguageCode

func (x *TranslationModelMetadata) GetSourceLanguageCode() string

func (*TranslationModelMetadata) GetTargetLanguageCode

func (x *TranslationModelMetadata) GetTargetLanguageCode() string

func (*TranslationModelMetadata) ProtoMessage

func (*TranslationModelMetadata) ProtoMessage()

func (*TranslationModelMetadata) ProtoReflect

func (x *TranslationModelMetadata) ProtoReflect() protoreflect.Message

func (*TranslationModelMetadata) Reset

func (x *TranslationModelMetadata) Reset()

func (*TranslationModelMetadata) String

func (x *TranslationModelMetadata) String() string

UndeployModelOperationMetadata

type UndeployModelOperationMetadata struct {
	// contains filtered or unexported fields
}

Details of UndeployModel operation.

func (*UndeployModelOperationMetadata) Descriptor

func (*UndeployModelOperationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use UndeployModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*UndeployModelOperationMetadata) ProtoMessage

func (*UndeployModelOperationMetadata) ProtoMessage()

func (*UndeployModelOperationMetadata) ProtoReflect

func (x *UndeployModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*UndeployModelOperationMetadata) Reset

func (x *UndeployModelOperationMetadata) Reset()

func (*UndeployModelOperationMetadata) String

func (x *UndeployModelOperationMetadata) String() string

UndeployModelRequest

type UndeployModelRequest struct {

	// Required. Resource name of the model to undeploy.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].

func (*UndeployModelRequest) Descriptor

func (*UndeployModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use UndeployModelRequest.ProtoReflect.Descriptor instead.

func (*UndeployModelRequest) GetName

func (x *UndeployModelRequest) GetName() string

func (*UndeployModelRequest) ProtoMessage

func (*UndeployModelRequest) ProtoMessage()

func (*UndeployModelRequest) ProtoReflect

func (x *UndeployModelRequest) ProtoReflect() protoreflect.Message

func (*UndeployModelRequest) Reset

func (x *UndeployModelRequest) Reset()

func (*UndeployModelRequest) String

func (x *UndeployModelRequest) String() string

UnimplementedAutoMlServer

type UnimplementedAutoMlServer struct {
}

UnimplementedAutoMlServer can be embedded to have forward compatible implementations.

func (*UnimplementedAutoMlServer) CreateDataset

func (*UnimplementedAutoMlServer) CreateDataset(context.Context, *CreateDatasetRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) CreateModel

func (*UnimplementedAutoMlServer) CreateModel(context.Context, *CreateModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeleteDataset

func (*UnimplementedAutoMlServer) DeleteDataset(context.Context, *DeleteDatasetRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeleteModel

func (*UnimplementedAutoMlServer) DeleteModel(context.Context, *DeleteModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeployModel

func (*UnimplementedAutoMlServer) DeployModel(context.Context, *DeployModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ExportData

func (*UnimplementedAutoMlServer) ExportData(context.Context, *ExportDataRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ExportModel

func (*UnimplementedAutoMlServer) ExportModel(context.Context, *ExportModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) GetAnnotationSpec

func (*UnimplementedAutoMlServer) GetAnnotationSpec(context.Context, *GetAnnotationSpecRequest) (*AnnotationSpec, error)

func (*UnimplementedAutoMlServer) GetDataset

func (*UnimplementedAutoMlServer) GetDataset(context.Context, *GetDatasetRequest) (*Dataset, error)

func (*UnimplementedAutoMlServer) GetModel

func (*UnimplementedAutoMlServer) GetModel(context.Context, *GetModelRequest) (*Model, error)

func (*UnimplementedAutoMlServer) GetModelEvaluation

func (*UnimplementedAutoMlServer) GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)

func (*UnimplementedAutoMlServer) ImportData

func (*UnimplementedAutoMlServer) ImportData(context.Context, *ImportDataRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ListDatasets

func (*UnimplementedAutoMlServer) ListDatasets(context.Context, *ListDatasetsRequest) (*ListDatasetsResponse, error)

func (*UnimplementedAutoMlServer) ListModelEvaluations

func (*UnimplementedAutoMlServer) ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)

func (*UnimplementedAutoMlServer) ListModels

func (*UnimplementedAutoMlServer) ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)

func (*UnimplementedAutoMlServer) UndeployModel

func (*UnimplementedAutoMlServer) UndeployModel(context.Context, *UndeployModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) UpdateDataset

func (*UnimplementedAutoMlServer) UpdateDataset(context.Context, *UpdateDatasetRequest) (*Dataset, error)

func (*UnimplementedAutoMlServer) UpdateModel

func (*UnimplementedAutoMlServer) UpdateModel(context.Context, *UpdateModelRequest) (*Model, error)

UnimplementedPredictionServiceServer

type UnimplementedPredictionServiceServer struct {
}

UnimplementedPredictionServiceServer can be embedded to have forward compatible implementations.

func (*UnimplementedPredictionServiceServer) BatchPredict

func (*UnimplementedPredictionServiceServer) BatchPredict(context.Context, *BatchPredictRequest) (*longrunning.Operation, error)

func (*UnimplementedPredictionServiceServer) Predict

func (*UnimplementedPredictionServiceServer) Predict(context.Context, *PredictRequest) (*PredictResponse, error)

UpdateDatasetRequest

type UpdateDatasetRequest struct {

	// Required. The dataset which replaces the resource on the server.
	Dataset *Dataset `protobuf:"bytes,1,opt,name=dataset,proto3" json:"dataset,omitempty"`
	// Required. The update mask applies to the resource.
	UpdateMask *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]

func (*UpdateDatasetRequest) Descriptor

func (*UpdateDatasetRequest) Descriptor() ([]byte, []int)

Deprecated: Use UpdateDatasetRequest.ProtoReflect.Descriptor instead.

func (*UpdateDatasetRequest) GetDataset

func (x *UpdateDatasetRequest) GetDataset() *Dataset

func (*UpdateDatasetRequest) GetUpdateMask

func (x *UpdateDatasetRequest) GetUpdateMask() *fieldmaskpb.FieldMask

func (*UpdateDatasetRequest) ProtoMessage

func (*UpdateDatasetRequest) ProtoMessage()

func (*UpdateDatasetRequest) ProtoReflect

func (x *UpdateDatasetRequest) ProtoReflect() protoreflect.Message

func (*UpdateDatasetRequest) Reset

func (x *UpdateDatasetRequest) Reset()

func (*UpdateDatasetRequest) String

func (x *UpdateDatasetRequest) String() string

UpdateModelRequest

type UpdateModelRequest struct {

	// Required. The model which replaces the resource on the server.
	Model *Model `protobuf:"bytes,1,opt,name=model,proto3" json:"model,omitempty"`
	// Required. The update mask applies to the resource.
	UpdateMask *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]

func (*UpdateModelRequest) Descriptor

func (*UpdateModelRequest) Descriptor() ([]byte, []int)

Deprecated: Use UpdateModelRequest.ProtoReflect.Descriptor instead.

func (*UpdateModelRequest) GetModel

func (x *UpdateModelRequest) GetModel() *Model

func (*UpdateModelRequest) GetUpdateMask

func (x *UpdateModelRequest) GetUpdateMask() *fieldmaskpb.FieldMask

func (*UpdateModelRequest) ProtoMessage

func (*UpdateModelRequest) ProtoMessage()

func (*UpdateModelRequest) ProtoReflect

func (x *UpdateModelRequest) ProtoReflect() protoreflect.Message

func (*UpdateModelRequest) Reset

func (x *UpdateModelRequest) Reset()

func (*UpdateModelRequest) String

func (x *UpdateModelRequest) String() string