package automl

import "google.golang.org/genproto/googleapis/cloud/automl/v1beta1"

Package automl aliases all exported identifiers in package "cloud.google.com/go/automl/apiv1beta1/automlpb".

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb. Please read https://github.com/googleapis/google-cloud-go/blob/main/migration.md for more details.

Index

Constants 

const (
	ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED     = src.ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED
	ClassificationType_MULTICLASS                          = src.ClassificationType_MULTICLASS
	ClassificationType_MULTILABEL                          = src.ClassificationType_MULTILABEL
	DocumentDimensions_CENTIMETER                          = src.DocumentDimensions_CENTIMETER
	DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED = src.DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED
	DocumentDimensions_INCH                                = src.DocumentDimensions_INCH
	DocumentDimensions_POINT                               = src.DocumentDimensions_POINT
	Document_Layout_FORM_FIELD                             = src.Document_Layout_FORM_FIELD
	Document_Layout_FORM_FIELD_CONTENTS                    = src.Document_Layout_FORM_FIELD_CONTENTS
	Document_Layout_FORM_FIELD_NAME                        = src.Document_Layout_FORM_FIELD_NAME
	Document_Layout_PARAGRAPH                              = src.Document_Layout_PARAGRAPH
	Document_Layout_TABLE                                  = src.Document_Layout_TABLE
	Document_Layout_TABLE_CELL                             = src.Document_Layout_TABLE_CELL
	Document_Layout_TABLE_HEADER                           = src.Document_Layout_TABLE_HEADER
	Document_Layout_TABLE_ROW                              = src.Document_Layout_TABLE_ROW
	Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED          = src.Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED
	Document_Layout_TOKEN                                  = src.Document_Layout_TOKEN
	Model_DEPLOYED                                         = src.Model_DEPLOYED
	Model_DEPLOYMENT_STATE_UNSPECIFIED                     = src.Model_DEPLOYMENT_STATE_UNSPECIFIED
	Model_UNDEPLOYED                                       = src.Model_UNDEPLOYED
	TypeCode_ARRAY                                         = src.TypeCode_ARRAY
	TypeCode_CATEGORY                                      = src.TypeCode_CATEGORY
	TypeCode_FLOAT64                                       = src.TypeCode_FLOAT64
	TypeCode_STRING                                        = src.TypeCode_STRING
	TypeCode_STRUCT                                        = src.TypeCode_STRUCT
	TypeCode_TIMESTAMP                                     = src.TypeCode_TIMESTAMP
	TypeCode_TYPE_CODE_UNSPECIFIED                         = src.TypeCode_TYPE_CODE_UNSPECIFIED
)

Deprecated: Please use consts in: cloud.google.com/go/automl/apiv1beta1/automlpb

Variables 

var (
	ClassificationType_name                                   = src.ClassificationType_name
	ClassificationType_value                                  = src.ClassificationType_value
	DocumentDimensions_DocumentDimensionUnit_name             = src.DocumentDimensions_DocumentDimensionUnit_name
	DocumentDimensions_DocumentDimensionUnit_value            = src.DocumentDimensions_DocumentDimensionUnit_value
	Document_Layout_TextSegmentType_name                      = src.Document_Layout_TextSegmentType_name
	Document_Layout_TextSegmentType_value                     = src.Document_Layout_TextSegmentType_value
	File_google_cloud_automl_v1beta1_annotation_payload_proto = src.File_google_cloud_automl_v1beta1_annotation_payload_proto
	File_google_cloud_automl_v1beta1_annotation_spec_proto    = src.File_google_cloud_automl_v1beta1_annotation_spec_proto
	File_google_cloud_automl_v1beta1_classification_proto     = src.File_google_cloud_automl_v1beta1_classification_proto
	File_google_cloud_automl_v1beta1_column_spec_proto        = src.File_google_cloud_automl_v1beta1_column_spec_proto
	File_google_cloud_automl_v1beta1_data_items_proto         = src.File_google_cloud_automl_v1beta1_data_items_proto
	File_google_cloud_automl_v1beta1_data_stats_proto         = src.File_google_cloud_automl_v1beta1_data_stats_proto
	File_google_cloud_automl_v1beta1_data_types_proto         = src.File_google_cloud_automl_v1beta1_data_types_proto
	File_google_cloud_automl_v1beta1_dataset_proto            = src.File_google_cloud_automl_v1beta1_dataset_proto
	File_google_cloud_automl_v1beta1_detection_proto          = src.File_google_cloud_automl_v1beta1_detection_proto
	File_google_cloud_automl_v1beta1_geometry_proto           = src.File_google_cloud_automl_v1beta1_geometry_proto
	File_google_cloud_automl_v1beta1_image_proto              = src.File_google_cloud_automl_v1beta1_image_proto
	File_google_cloud_automl_v1beta1_io_proto                 = src.File_google_cloud_automl_v1beta1_io_proto
	File_google_cloud_automl_v1beta1_model_evaluation_proto   = src.File_google_cloud_automl_v1beta1_model_evaluation_proto
	File_google_cloud_automl_v1beta1_model_proto              = src.File_google_cloud_automl_v1beta1_model_proto
	File_google_cloud_automl_v1beta1_operations_proto         = src.File_google_cloud_automl_v1beta1_operations_proto
	File_google_cloud_automl_v1beta1_prediction_service_proto = src.File_google_cloud_automl_v1beta1_prediction_service_proto
	File_google_cloud_automl_v1beta1_ranges_proto             = src.File_google_cloud_automl_v1beta1_ranges_proto
	File_google_cloud_automl_v1beta1_regression_proto         = src.File_google_cloud_automl_v1beta1_regression_proto
	File_google_cloud_automl_v1beta1_service_proto            = src.File_google_cloud_automl_v1beta1_service_proto
	File_google_cloud_automl_v1beta1_table_spec_proto         = src.File_google_cloud_automl_v1beta1_table_spec_proto
	File_google_cloud_automl_v1beta1_tables_proto             = src.File_google_cloud_automl_v1beta1_tables_proto
	File_google_cloud_automl_v1beta1_temporal_proto           = src.File_google_cloud_automl_v1beta1_temporal_proto
	File_google_cloud_automl_v1beta1_text_extraction_proto    = src.File_google_cloud_automl_v1beta1_text_extraction_proto
	File_google_cloud_automl_v1beta1_text_proto               = src.File_google_cloud_automl_v1beta1_text_proto
	File_google_cloud_automl_v1beta1_text_segment_proto       = src.File_google_cloud_automl_v1beta1_text_segment_proto
	File_google_cloud_automl_v1beta1_text_sentiment_proto     = src.File_google_cloud_automl_v1beta1_text_sentiment_proto
	File_google_cloud_automl_v1beta1_translation_proto        = src.File_google_cloud_automl_v1beta1_translation_proto
	File_google_cloud_automl_v1beta1_video_proto              = src.File_google_cloud_automl_v1beta1_video_proto
	Model_DeploymentState_name                                = src.Model_DeploymentState_name
	Model_DeploymentState_value                               = src.Model_DeploymentState_value
	TypeCode_name                                             = src.TypeCode_name
	TypeCode_value                                            = src.TypeCode_value
)

Deprecated: Please use vars in: cloud.google.com/go/automl/apiv1beta1/automlpb

Functions

func RegisterAutoMlServer 

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

Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb

func RegisterPredictionServiceServer 

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

Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb

Types

type AnnotationPayload 

type AnnotationPayload = src.AnnotationPayload

Contains annotation information that is relevant to AutoML.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type AnnotationPayload_Classification 

type AnnotationPayload_Classification = src.AnnotationPayload_Classification

type AnnotationPayload_ImageObjectDetection 

type AnnotationPayload_ImageObjectDetection = src.AnnotationPayload_ImageObjectDetection

type AnnotationPayload_Tables 

type AnnotationPayload_Tables = src.AnnotationPayload_Tables

type AnnotationPayload_TextExtraction 

type AnnotationPayload_TextExtraction = src.AnnotationPayload_TextExtraction

type AnnotationPayload_TextSentiment 

type AnnotationPayload_TextSentiment = src.AnnotationPayload_TextSentiment

type AnnotationPayload_Translation 

type AnnotationPayload_Translation = src.AnnotationPayload_Translation

type AnnotationPayload_VideoClassification 

type AnnotationPayload_VideoClassification = src.AnnotationPayload_VideoClassification

type AnnotationPayload_VideoObjectTracking 

type AnnotationPayload_VideoObjectTracking = src.AnnotationPayload_VideoObjectTracking

type AnnotationSpec 

type AnnotationSpec = src.AnnotationSpec

A definition of an annotation spec.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ArrayStats 

type ArrayStats = src.ArrayStats

The data statistics of a series of ARRAY values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type AutoMlClient 

type AutoMlClient = src.AutoMlClient

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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

func NewAutoMlClient 

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb

type AutoMlServer 

type AutoMlServer = src.AutoMlServer

AutoMlServer is the server API for AutoMl service.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictInputConfig 

type BatchPredictInputConfig = src.BatchPredictInputConfig

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.v1beta1.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: - For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png - For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png - For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf - For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf - For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf - For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf - For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain 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. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } } - For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]. GCS case: 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.v1beta1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1beta1.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. For FORECASTING [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored. First three sample rows of 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"}]} BigQuery case: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1beta1.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 of the table will be attempted. For FORECASTING [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored. Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") 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 an 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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictInputConfig_BigquerySource 

type BatchPredictInputConfig_BigquerySource = src.BatchPredictInputConfig_BigquerySource

type BatchPredictInputConfig_GcsSource 

type BatchPredictInputConfig_GcsSource = src.BatchPredictInputConfig_GcsSource

type BatchPredictOperationMetadata 

type BatchPredictOperationMetadata = src.BatchPredictOperationMetadata

Details of BatchPredict operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictOperationMetadata_BatchPredictOutputInfo 

type BatchPredictOperationMetadata_BatchPredictOutputInfo = src.BatchPredictOperationMetadata_BatchPredictOutputInfo

Further describes this batch predict's output. Supplements BatchPredictOutputConfig[google.cloud.automl.v1beta1.BatchPredictOutputConfig].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset 

type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory 

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory

type BatchPredictOutputConfig 

type BatchPredictOutputConfig = src.BatchPredictOutputConfig

Output configuration for BatchPredict Action. # As destination the [gcs_destination][google.cloud.automl.v1beta1.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-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for. - 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`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`fields. * 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`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`fields. * 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 snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file 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 text snippet or input text file followed by exactly one [`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 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 snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file 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 text snippet or input text file followed by exactly one [`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 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`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`. * For Tables: Output depends on whether [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] or [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). GCS 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.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] given on input followed by M target column names in the format of "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>_<target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction [scores][google.cloud.automl.v1beta1.TablesAnnotation.score]. For REGRESSION and FORECASTING [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1beta1.display_name] given on input followed by the predicted target column with name in the format of "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1beta1.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`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a JSON string, and containing only `code` and `message`. BigQuery case: [bigquery_destination][google.cloud.automl.v1beta1.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 <model-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, `predictions`, and `errors`. The `predictions` table's column names will be the input columns' [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] followed by the target column with name in the format of "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], represented as STRUCT-s, containing TablesAnnotation[google.cloud.automl.v1beta1.TablesAnnotation]. The `errors` table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>", and as a value has [`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing only `code` and `message`.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictOutputConfig_BigqueryDestination 

type BatchPredictOutputConfig_BigqueryDestination = src.BatchPredictOutputConfig_BigqueryDestination

type BatchPredictOutputConfig_GcsDestination 

type BatchPredictOutputConfig_GcsDestination = src.BatchPredictOutputConfig_GcsDestination

type BatchPredictRequest 

type BatchPredictRequest = src.BatchPredictRequest

Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BatchPredictResult 

type BatchPredictResult = src.BatchPredictResult

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.v1beta1.PredictionService.BatchPredict].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BigQueryDestination 

type BigQueryDestination = src.BigQueryDestination

The BigQuery location for the output content.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BigQuerySource 

type BigQuerySource = src.BigQuerySource

The BigQuery location for the input content.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BoundingBoxMetricsEntry 

type BoundingBoxMetricsEntry = src.BoundingBoxMetricsEntry

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry 

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry = src.BoundingBoxMetricsEntry_ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type BoundingPoly 

type BoundingPoly = src.BoundingPoly

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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CategoryStats 

type CategoryStats = src.CategoryStats

The data statistics of a series of CATEGORY values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CategoryStats_SingleCategoryStats 

type CategoryStats_SingleCategoryStats = src.CategoryStats_SingleCategoryStats

The statistics of a single CATEGORY value.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationAnnotation 

type ClassificationAnnotation = src.ClassificationAnnotation

Contains annotation details specific to classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationEvaluationMetrics 

type ClassificationEvaluationMetrics = src.ClassificationEvaluationMetrics

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry 

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry = src.ClassificationEvaluationMetrics_ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationEvaluationMetrics_ConfusionMatrix 

type ClassificationEvaluationMetrics_ConfusionMatrix = src.ClassificationEvaluationMetrics_ConfusionMatrix

Confusion matrix of the model running the classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationEvaluationMetrics_ConfusionMatrix_Row 

type ClassificationEvaluationMetrics_ConfusionMatrix_Row = src.ClassificationEvaluationMetrics_ConfusionMatrix_Row

Output only. A row in the confusion matrix.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ClassificationType 

type ClassificationType = src.ClassificationType

Type of the classification problem.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ColumnSpec 

type ColumnSpec = src.ColumnSpec

A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: - Tables

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ColumnSpec_CorrelatedColumn 

type ColumnSpec_CorrelatedColumn = src.ColumnSpec_CorrelatedColumn

Identifies the table's column, and its correlation with the column this ColumnSpec describes.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CorrelationStats 

type CorrelationStats = src.CorrelationStats

A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CreateDatasetRequest 

type CreateDatasetRequest = src.CreateDatasetRequest

Request message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CreateModelOperationMetadata 

type CreateModelOperationMetadata = src.CreateModelOperationMetadata

Details of CreateModel operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type CreateModelRequest 

type CreateModelRequest = src.CreateModelRequest

Request message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DataStats 

type DataStats = src.DataStats

The data statistics of a series of values that share the same DataType.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DataStats_ArrayStats 

type DataStats_ArrayStats = src.DataStats_ArrayStats

type DataStats_CategoryStats 

type DataStats_CategoryStats = src.DataStats_CategoryStats

type DataStats_Float64Stats 

type DataStats_Float64Stats = src.DataStats_Float64Stats

type DataStats_StringStats 

type DataStats_StringStats = src.DataStats_StringStats

type DataStats_StructStats 

type DataStats_StructStats = src.DataStats_StructStats

type DataStats_TimestampStats 

type DataStats_TimestampStats = src.DataStats_TimestampStats

type DataType 

type DataType = src.DataType

Indicated the type of data that can be stored in a structured data entity (e.g. a table).

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DataType_ListElementType 

type DataType_ListElementType = src.DataType_ListElementType

type DataType_StructType 

type DataType_StructType = src.DataType_StructType

type DataType_TimeFormat 

type DataType_TimeFormat = src.DataType_TimeFormat

type Dataset 

type Dataset = src.Dataset

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Dataset_ImageClassificationDatasetMetadata 

type Dataset_ImageClassificationDatasetMetadata = src.Dataset_ImageClassificationDatasetMetadata

type Dataset_ImageObjectDetectionDatasetMetadata 

type Dataset_ImageObjectDetectionDatasetMetadata = src.Dataset_ImageObjectDetectionDatasetMetadata

type Dataset_TablesDatasetMetadata 

type Dataset_TablesDatasetMetadata = src.Dataset_TablesDatasetMetadata

type Dataset_TextClassificationDatasetMetadata 

type Dataset_TextClassificationDatasetMetadata = src.Dataset_TextClassificationDatasetMetadata

type Dataset_TextExtractionDatasetMetadata 

type Dataset_TextExtractionDatasetMetadata = src.Dataset_TextExtractionDatasetMetadata

type Dataset_TextSentimentDatasetMetadata 

type Dataset_TextSentimentDatasetMetadata = src.Dataset_TextSentimentDatasetMetadata

type Dataset_TranslationDatasetMetadata 

type Dataset_TranslationDatasetMetadata = src.Dataset_TranslationDatasetMetadata

type Dataset_VideoClassificationDatasetMetadata 

type Dataset_VideoClassificationDatasetMetadata = src.Dataset_VideoClassificationDatasetMetadata

type Dataset_VideoObjectTrackingDatasetMetadata 

type Dataset_VideoObjectTrackingDatasetMetadata = src.Dataset_VideoObjectTrackingDatasetMetadata

type DeleteDatasetRequest 

type DeleteDatasetRequest = src.DeleteDatasetRequest

Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DeleteModelRequest 

type DeleteModelRequest = src.DeleteModelRequest

Request message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DeleteOperationMetadata 

type DeleteOperationMetadata = src.DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DeployModelOperationMetadata 

type DeployModelOperationMetadata = src.DeployModelOperationMetadata

Details of DeployModel operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DeployModelRequest 

type DeployModelRequest = src.DeployModelRequest

Request message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DeployModelRequest_ImageClassificationModelDeploymentMetadata 

type DeployModelRequest_ImageClassificationModelDeploymentMetadata = src.DeployModelRequest_ImageClassificationModelDeploymentMetadata

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata 

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata = src.DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata

type Document 

type Document = src.Document

A structured text document e.g. a PDF.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DocumentDimensions 

type DocumentDimensions = src.DocumentDimensions

Message that describes dimension of a document.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DocumentDimensions_DocumentDimensionUnit 

type DocumentDimensions_DocumentDimensionUnit = src.DocumentDimensions_DocumentDimensionUnit

Unit of the document dimension.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DocumentInputConfig 

type DocumentInputConfig = src.DocumentInputConfig

Input configuration of a Document[google.cloud.automl.v1beta1.Document].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Document_Layout 

type Document_Layout = src.Document_Layout

Describes the layout information of a [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the document.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Document_Layout_TextSegmentType 

type Document_Layout_TextSegmentType = src.Document_Layout_TextSegmentType

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type DoubleRange 

type DoubleRange = src.DoubleRange

A range between two double numbers.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExamplePayload 

type ExamplePayload = src.ExamplePayload

Example data used for training or prediction.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExamplePayload_Document 

type ExamplePayload_Document = src.ExamplePayload_Document

type ExamplePayload_Image 

type ExamplePayload_Image = src.ExamplePayload_Image

type ExamplePayload_Row 

type ExamplePayload_Row = src.ExamplePayload_Row

type ExamplePayload_TextSnippet 

type ExamplePayload_TextSnippet = src.ExamplePayload_TextSnippet

type ExportDataOperationMetadata 

type ExportDataOperationMetadata = src.ExportDataOperationMetadata

Details of ExportData operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportDataOperationMetadata_ExportDataOutputInfo 

type ExportDataOperationMetadata_ExportDataOutputInfo = src.ExportDataOperationMetadata_ExportDataOutputInfo

Further describes this export data's output. Supplements OutputConfig[google.cloud.automl.v1beta1.OutputConfig].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset 

type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset = src.ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory 

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory = src.ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory

type ExportDataRequest 

type ExportDataRequest = src.ExportDataRequest

Request message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportEvaluatedExamplesOperationMetadata 

type ExportEvaluatedExamplesOperationMetadata = src.ExportEvaluatedExamplesOperationMetadata

Details of EvaluatedExamples operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo 

type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo = src.ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo

Further describes the output of the evaluated examples export. Supplements ExportEvaluatedExamplesOutputConfig[google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportEvaluatedExamplesOutputConfig 

type ExportEvaluatedExamplesOutputConfig = src.ExportEvaluatedExamplesOutputConfig

Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported): - For Tables: [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name `export_evaluated_examples_<model-display-name>_<timestamp-of-export-call>` where <model-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset an `evaluated_examples` table will be created. It will have all the same columns as the [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id] of the [dataset][google.cloud.automl.v1beta1.Model.dataset_id] from which the model was created, as they were at the moment of model's evaluation (this includes the target column with its ground truth), followed by a column called "predicted_<target_column>". That last column will contain the model's prediction result for each respective row, given as ARRAY of [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], represented as STRUCT-s, containing TablesAnnotation[google.cloud.automl.v1beta1.TablesAnnotation].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportEvaluatedExamplesOutputConfig_BigqueryDestination 

type ExportEvaluatedExamplesOutputConfig_BigqueryDestination = src.ExportEvaluatedExamplesOutputConfig_BigqueryDestination

type ExportEvaluatedExamplesRequest 

type ExportEvaluatedExamplesRequest = src.ExportEvaluatedExamplesRequest

Request message for [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportModelOperationMetadata 

type ExportModelOperationMetadata = src.ExportModelOperationMetadata

Details of ExportModel operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportModelOperationMetadata_ExportModelOutputInfo 

type ExportModelOperationMetadata_ExportModelOutputInfo = src.ExportModelOperationMetadata_ExportModelOutputInfo

Further describes the output of model export. Supplements ModelExportOutputConfig[google.cloud.automl.v1beta1.ModelExportOutputConfig].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ExportModelRequest 

type ExportModelRequest = src.ExportModelRequest

Request message for [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Float64Stats 

type Float64Stats = src.Float64Stats

The data statistics of a series of FLOAT64 values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Float64Stats_HistogramBucket 

type Float64Stats_HistogramBucket = src.Float64Stats_HistogramBucket

A bucket of a histogram.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GcrDestination 

type GcrDestination = src.GcrDestination

The GCR location where the image must be pushed to.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GcsDestination 

type GcsDestination = src.GcsDestination

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GcsSource 

type GcsSource = src.GcsSource

The Google Cloud Storage location for the input content.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetAnnotationSpecRequest 

type GetAnnotationSpecRequest = src.GetAnnotationSpecRequest

Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetColumnSpecRequest 

type GetColumnSpecRequest = src.GetColumnSpecRequest

Request message for [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetDatasetRequest 

type GetDatasetRequest = src.GetDatasetRequest

Request message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetModelEvaluationRequest 

type GetModelEvaluationRequest = src.GetModelEvaluationRequest

Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetModelRequest 

type GetModelRequest = src.GetModelRequest

Request message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type GetTableSpecRequest 

type GetTableSpecRequest = src.GetTableSpecRequest

Request message for [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Image 

type Image = src.Image

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageClassificationDatasetMetadata 

type ImageClassificationDatasetMetadata = src.ImageClassificationDatasetMetadata

Dataset metadata that is specific to image classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageClassificationModelDeploymentMetadata 

type ImageClassificationModelDeploymentMetadata = src.ImageClassificationModelDeploymentMetadata

Model deployment metadata specific to Image Classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageClassificationModelMetadata 

type ImageClassificationModelMetadata = src.ImageClassificationModelMetadata

Model metadata for image classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageObjectDetectionAnnotation 

type ImageObjectDetectionAnnotation = src.ImageObjectDetectionAnnotation

Annotation details for image object detection.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageObjectDetectionDatasetMetadata 

type ImageObjectDetectionDatasetMetadata = src.ImageObjectDetectionDatasetMetadata

Dataset metadata specific to image object detection.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageObjectDetectionEvaluationMetrics 

type ImageObjectDetectionEvaluationMetrics = src.ImageObjectDetectionEvaluationMetrics

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageObjectDetectionModelDeploymentMetadata 

type ImageObjectDetectionModelDeploymentMetadata = src.ImageObjectDetectionModelDeploymentMetadata

Model deployment metadata specific to Image Object Detection.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImageObjectDetectionModelMetadata 

type ImageObjectDetectionModelMetadata = src.ImageObjectDetectionModelMetadata

Model metadata specific to image object detection.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Image_ImageBytes 

type Image_ImageBytes = src.Image_ImageBytes

type Image_InputConfig 

type Image_InputConfig = src.Image_InputConfig

type ImportDataOperationMetadata 

type ImportDataOperationMetadata = src.ImportDataOperationMetadata

Details of ImportData operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ImportDataRequest 

type ImportDataRequest = src.ImportDataRequest

Request message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type InputConfig 

type InputConfig = src.InputConfig

Input configuration for 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.v1beta1.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: - For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For 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 - For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. 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,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,, - For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have 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 end has to be after the start. 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 shuold 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,,, - For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of 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,,,,,,,,,,, - For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "document_text": {"content": "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 3, }, "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, }, { "text_segment": { "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } } - For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood - For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2 - For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source] can be used. All inputs is concatenated into a single [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] 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. 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 = A path to file on GCS, e.g. "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 an 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 = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). 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 huge. 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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type InputConfig_BigquerySource 

type InputConfig_BigquerySource = src.InputConfig_BigquerySource

type InputConfig_GcsSource 

type InputConfig_GcsSource = src.InputConfig_GcsSource

type ListColumnSpecsRequest 

type ListColumnSpecsRequest = src.ListColumnSpecsRequest

Request message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListColumnSpecsResponse 

type ListColumnSpecsResponse = src.ListColumnSpecsResponse

Response message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListDatasetsRequest 

type ListDatasetsRequest = src.ListDatasetsRequest

Request message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListDatasetsResponse 

type ListDatasetsResponse = src.ListDatasetsResponse

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListModelEvaluationsRequest 

type ListModelEvaluationsRequest = src.ListModelEvaluationsRequest

Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListModelEvaluationsResponse 

type ListModelEvaluationsResponse = src.ListModelEvaluationsResponse

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListModelsRequest 

type ListModelsRequest = src.ListModelsRequest

Request message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListModelsResponse 

type ListModelsResponse = src.ListModelsResponse

Response message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListTableSpecsRequest 

type ListTableSpecsRequest = src.ListTableSpecsRequest

Request message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ListTableSpecsResponse 

type ListTableSpecsResponse = src.ListTableSpecsResponse

Response message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Model 

type Model = src.Model

API proto representing a trained machine learning model.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ModelEvaluation 

type ModelEvaluation = src.ModelEvaluation

Evaluation results of a model.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ModelEvaluation_ClassificationEvaluationMetrics 

type ModelEvaluation_ClassificationEvaluationMetrics = src.ModelEvaluation_ClassificationEvaluationMetrics

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics 

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics = src.ModelEvaluation_ImageObjectDetectionEvaluationMetrics

type ModelEvaluation_RegressionEvaluationMetrics 

type ModelEvaluation_RegressionEvaluationMetrics = src.ModelEvaluation_RegressionEvaluationMetrics

type ModelEvaluation_TextExtractionEvaluationMetrics 

type ModelEvaluation_TextExtractionEvaluationMetrics = src.ModelEvaluation_TextExtractionEvaluationMetrics

type ModelEvaluation_TextSentimentEvaluationMetrics 

type ModelEvaluation_TextSentimentEvaluationMetrics = src.ModelEvaluation_TextSentimentEvaluationMetrics

type ModelEvaluation_TranslationEvaluationMetrics 

type ModelEvaluation_TranslationEvaluationMetrics = src.ModelEvaluation_TranslationEvaluationMetrics

type ModelEvaluation_VideoObjectTrackingEvaluationMetrics 

type ModelEvaluation_VideoObjectTrackingEvaluationMetrics = src.ModelEvaluation_VideoObjectTrackingEvaluationMetrics

type ModelExportOutputConfig 

type ModelExportOutputConfig = src.ModelExportOutputConfig

Output configuration for ModelExport Action.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type ModelExportOutputConfig_GcrDestination 

type ModelExportOutputConfig_GcrDestination = src.ModelExportOutputConfig_GcrDestination

type ModelExportOutputConfig_GcsDestination 

type ModelExportOutputConfig_GcsDestination = src.ModelExportOutputConfig_GcsDestination

type Model_DeploymentState 

type Model_DeploymentState = src.Model_DeploymentState

Deployment state of the model.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Model_ImageClassificationModelMetadata 

type Model_ImageClassificationModelMetadata = src.Model_ImageClassificationModelMetadata

type Model_ImageObjectDetectionModelMetadata 

type Model_ImageObjectDetectionModelMetadata = src.Model_ImageObjectDetectionModelMetadata

type Model_TablesModelMetadata 

type Model_TablesModelMetadata = src.Model_TablesModelMetadata

type Model_TextClassificationModelMetadata 

type Model_TextClassificationModelMetadata = src.Model_TextClassificationModelMetadata

type Model_TextExtractionModelMetadata 

type Model_TextExtractionModelMetadata = src.Model_TextExtractionModelMetadata

type Model_TextSentimentModelMetadata 

type Model_TextSentimentModelMetadata = src.Model_TextSentimentModelMetadata

type Model_TranslationModelMetadata 

type Model_TranslationModelMetadata = src.Model_TranslationModelMetadata

type Model_VideoClassificationModelMetadata 

type Model_VideoClassificationModelMetadata = src.Model_VideoClassificationModelMetadata

type Model_VideoObjectTrackingModelMetadata 

type Model_VideoObjectTrackingModelMetadata = src.Model_VideoObjectTrackingModelMetadata

type NormalizedVertex 

type NormalizedVertex = src.NormalizedVertex

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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type OperationMetadata 

type OperationMetadata = src.OperationMetadata

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type OperationMetadata_BatchPredictDetails 

type OperationMetadata_BatchPredictDetails = src.OperationMetadata_BatchPredictDetails

type OperationMetadata_CreateModelDetails 

type OperationMetadata_CreateModelDetails = src.OperationMetadata_CreateModelDetails

type OperationMetadata_DeleteDetails 

type OperationMetadata_DeleteDetails = src.OperationMetadata_DeleteDetails

type OperationMetadata_DeployModelDetails 

type OperationMetadata_DeployModelDetails = src.OperationMetadata_DeployModelDetails

type OperationMetadata_ExportDataDetails 

type OperationMetadata_ExportDataDetails = src.OperationMetadata_ExportDataDetails

type OperationMetadata_ExportEvaluatedExamplesDetails 

type OperationMetadata_ExportEvaluatedExamplesDetails = src.OperationMetadata_ExportEvaluatedExamplesDetails

type OperationMetadata_ExportModelDetails 

type OperationMetadata_ExportModelDetails = src.OperationMetadata_ExportModelDetails

type OperationMetadata_ImportDataDetails 

type OperationMetadata_ImportDataDetails = src.OperationMetadata_ImportDataDetails

type OperationMetadata_UndeployModelDetails 

type OperationMetadata_UndeployModelDetails = src.OperationMetadata_UndeployModelDetails

type OutputConfig 

type OutputConfig = src.OutputConfig

- 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 GCS or BigQuery. GCS case: [gcs_destination][google.cloud.automl.v1beta1.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.v1beta1.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 <automl-dataset-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table called `primary_table` will be created, and filled with precisely the same data as this obtained on import.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type OutputConfig_BigqueryDestination 

type OutputConfig_BigqueryDestination = src.OutputConfig_BigqueryDestination

type OutputConfig_GcsDestination 

type OutputConfig_GcsDestination = src.OutputConfig_GcsDestination

type PredictRequest 

type PredictRequest = src.PredictRequest

Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type PredictResponse 

type PredictResponse = src.PredictResponse

Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type PredictionServiceClient 

type PredictionServiceClient = src.PredictionServiceClient

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.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

func NewPredictionServiceClient 

func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient

Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb

type PredictionServiceServer 

type PredictionServiceServer = src.PredictionServiceServer

PredictionServiceServer is the server API for PredictionService service.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type RegressionEvaluationMetrics 

type RegressionEvaluationMetrics = src.RegressionEvaluationMetrics

Metrics for regression problems.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type Row 

type Row = src.Row

A representation of a row in a relational table.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type StringStats 

type StringStats = src.StringStats

The data statistics of a series of STRING values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type StringStats_UnigramStats 

type StringStats_UnigramStats = src.StringStats_UnigramStats

The statistics of a unigram.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type StructStats 

type StructStats = src.StructStats

The data statistics of a series of STRUCT values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type StructType 

type StructType = src.StructType

`StructType` defines the DataType-s of a [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT] type.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TableSpec 

type TableSpec = src.TableSpec

A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: - Tables

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TablesAnnotation 

type TablesAnnotation = src.TablesAnnotation

Contains annotation details specific to Tables.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TablesDatasetMetadata 

type TablesDatasetMetadata = src.TablesDatasetMetadata

Metadata for a dataset used for AutoML Tables.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TablesModelColumnInfo 

type TablesModelColumnInfo = src.TablesModelColumnInfo

An information specific to given column and Tables Model, in context of the Model and the predictions created by it.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TablesModelMetadata 

type TablesModelMetadata = src.TablesModelMetadata

Model metadata specific to AutoML Tables.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TablesModelMetadata_OptimizationObjectivePrecisionValue 

type TablesModelMetadata_OptimizationObjectivePrecisionValue = src.TablesModelMetadata_OptimizationObjectivePrecisionValue

type TablesModelMetadata_OptimizationObjectiveRecallValue 

type TablesModelMetadata_OptimizationObjectiveRecallValue = src.TablesModelMetadata_OptimizationObjectiveRecallValue

type TextClassificationDatasetMetadata 

type TextClassificationDatasetMetadata = src.TextClassificationDatasetMetadata

Dataset metadata for classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextClassificationModelMetadata 

type TextClassificationModelMetadata = src.TextClassificationModelMetadata

Model metadata that is specific to text classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextExtractionAnnotation 

type TextExtractionAnnotation = src.TextExtractionAnnotation

Annotation for identifying spans of text.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextExtractionAnnotation_TextSegment 

type TextExtractionAnnotation_TextSegment = src.TextExtractionAnnotation_TextSegment

type TextExtractionDatasetMetadata 

type TextExtractionDatasetMetadata = src.TextExtractionDatasetMetadata

Dataset metadata that is specific to text extraction

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextExtractionEvaluationMetrics 

type TextExtractionEvaluationMetrics = src.TextExtractionEvaluationMetrics

Model evaluation metrics for text extraction problems.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry 

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry = src.TextExtractionEvaluationMetrics_ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextExtractionModelMetadata 

type TextExtractionModelMetadata = src.TextExtractionModelMetadata

Model metadata that is specific to text extraction.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSegment 

type TextSegment = src.TextSegment

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

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSentimentAnnotation 

type TextSentimentAnnotation = src.TextSentimentAnnotation

Contains annotation details specific to text sentiment.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSentimentDatasetMetadata 

type TextSentimentDatasetMetadata = src.TextSentimentDatasetMetadata

Dataset metadata for text sentiment.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSentimentEvaluationMetrics 

type TextSentimentEvaluationMetrics = src.TextSentimentEvaluationMetrics

Model evaluation metrics for text sentiment problems.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSentimentModelMetadata 

type TextSentimentModelMetadata = src.TextSentimentModelMetadata

Model metadata that is specific to text sentiment.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TextSnippet 

type TextSnippet = src.TextSnippet

A representation of a text snippet.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TimeSegment 

type TimeSegment = src.TimeSegment

A time period inside of an example that has a time dimension (e.g. video).

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TimestampStats 

type TimestampStats = src.TimestampStats

The data statistics of a series of TIMESTAMP values.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TimestampStats_GranularStats 

type TimestampStats_GranularStats = src.TimestampStats_GranularStats

Stats split by a defined in context granularity.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TranslationAnnotation 

type TranslationAnnotation = src.TranslationAnnotation

Annotation details specific to translation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TranslationDatasetMetadata 

type TranslationDatasetMetadata = src.TranslationDatasetMetadata

Dataset metadata that is specific to translation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TranslationEvaluationMetrics 

type TranslationEvaluationMetrics = src.TranslationEvaluationMetrics

Evaluation metrics for the dataset.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TranslationModelMetadata 

type TranslationModelMetadata = src.TranslationModelMetadata

Model metadata that is specific to translation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type TypeCode 

type TypeCode = src.TypeCode

`TypeCode` is used as a part of DataType[google.cloud.automl.v1beta1.DataType].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UndeployModelOperationMetadata 

type UndeployModelOperationMetadata = src.UndeployModelOperationMetadata

Details of UndeployModel operation.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UndeployModelRequest 

type UndeployModelRequest = src.UndeployModelRequest

Request message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel].

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UnimplementedAutoMlServer 

type UnimplementedAutoMlServer = src.UnimplementedAutoMlServer

UnimplementedAutoMlServer can be embedded to have forward compatible implementations.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UnimplementedPredictionServiceServer 

type UnimplementedPredictionServiceServer = src.UnimplementedPredictionServiceServer

UnimplementedPredictionServiceServer can be embedded to have forward compatible implementations.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UpdateColumnSpecRequest 

type UpdateColumnSpecRequest = src.UpdateColumnSpecRequest

Request message for [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec]

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UpdateDatasetRequest 

type UpdateDatasetRequest = src.UpdateDatasetRequest

Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset]

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type UpdateTableSpecRequest 

type UpdateTableSpecRequest = src.UpdateTableSpecRequest

Request message for [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec]

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoClassificationAnnotation 

type VideoClassificationAnnotation = src.VideoClassificationAnnotation

Contains annotation details specific to video classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoClassificationDatasetMetadata 

type VideoClassificationDatasetMetadata = src.VideoClassificationDatasetMetadata

Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoClassificationModelMetadata 

type VideoClassificationModelMetadata = src.VideoClassificationModelMetadata

Model metadata specific to video classification.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoObjectTrackingAnnotation 

type VideoObjectTrackingAnnotation = src.VideoObjectTrackingAnnotation

Annotation details for video object tracking.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoObjectTrackingDatasetMetadata 

type VideoObjectTrackingDatasetMetadata = src.VideoObjectTrackingDatasetMetadata

Dataset metadata specific to video object tracking.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoObjectTrackingEvaluationMetrics 

type VideoObjectTrackingEvaluationMetrics = src.VideoObjectTrackingEvaluationMetrics

Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

type VideoObjectTrackingModelMetadata 

type VideoObjectTrackingModelMetadata = src.VideoObjectTrackingModelMetadata

Model metadata specific to video object tracking.

Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb

Source Files

alias.go

Version
v0.0.0-20250219182151-9fdb1cabc7b2 (latest)
Published
Feb 19, 2025
Platform
linux/amd64
Imports
2 packages
Last checked
1 hour ago –

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