// Copyright 2022 Google LLC // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // Code generated by aliasgen. DO NOT EDIT. // Package automl aliases all exported identifiers in package // "cloud.google.com/go/automl/apiv1/automlpb". // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb. // Please read https://github.com/googleapis/google-cloud-go/blob/main/migration.md // for more details. package automl import ( src "cloud.google.com/go/automl/apiv1/automlpb" grpc "google.golang.org/grpc" ) // Deprecated: Please use consts in: cloud.google.com/go/automl/apiv1/automlpb 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 ) // Deprecated: Please use vars in: cloud.google.com/go/automl/apiv1/automlpb 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_v1_annotation_payload_proto = src.File_google_cloud_automl_v1_annotation_payload_proto File_google_cloud_automl_v1_annotation_spec_proto = src.File_google_cloud_automl_v1_annotation_spec_proto File_google_cloud_automl_v1_classification_proto = src.File_google_cloud_automl_v1_classification_proto File_google_cloud_automl_v1_data_items_proto = src.File_google_cloud_automl_v1_data_items_proto File_google_cloud_automl_v1_dataset_proto = src.File_google_cloud_automl_v1_dataset_proto File_google_cloud_automl_v1_detection_proto = src.File_google_cloud_automl_v1_detection_proto File_google_cloud_automl_v1_geometry_proto = src.File_google_cloud_automl_v1_geometry_proto File_google_cloud_automl_v1_image_proto = src.File_google_cloud_automl_v1_image_proto File_google_cloud_automl_v1_io_proto = src.File_google_cloud_automl_v1_io_proto File_google_cloud_automl_v1_model_evaluation_proto = src.File_google_cloud_automl_v1_model_evaluation_proto File_google_cloud_automl_v1_model_proto = src.File_google_cloud_automl_v1_model_proto File_google_cloud_automl_v1_operations_proto = src.File_google_cloud_automl_v1_operations_proto File_google_cloud_automl_v1_prediction_service_proto = src.File_google_cloud_automl_v1_prediction_service_proto File_google_cloud_automl_v1_service_proto = src.File_google_cloud_automl_v1_service_proto File_google_cloud_automl_v1_text_extraction_proto = src.File_google_cloud_automl_v1_text_extraction_proto File_google_cloud_automl_v1_text_proto = src.File_google_cloud_automl_v1_text_proto File_google_cloud_automl_v1_text_segment_proto = src.File_google_cloud_automl_v1_text_segment_proto File_google_cloud_automl_v1_text_sentiment_proto = src.File_google_cloud_automl_v1_text_sentiment_proto File_google_cloud_automl_v1_translation_proto = src.File_google_cloud_automl_v1_translation_proto Model_DeploymentState_name = src.Model_DeploymentState_name Model_DeploymentState_value = src.Model_DeploymentState_value ) // Contains annotation information that is relevant to AutoML. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type AnnotationPayload = src.AnnotationPayload type AnnotationPayload_Classification = src.AnnotationPayload_Classification type AnnotationPayload_ImageObjectDetection = src.AnnotationPayload_ImageObjectDetection type AnnotationPayload_TextExtraction = src.AnnotationPayload_TextExtraction type AnnotationPayload_TextSentiment = src.AnnotationPayload_TextSentiment type AnnotationPayload_Translation = src.AnnotationPayload_Translation // A definition of an annotation spec. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type AnnotationSpec = src.AnnotationSpec // 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/apiv1/automlpb type AutoMlClient = src.AutoMlClient // AutoMlServer is the server API for AutoMl service. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type AutoMlServer = src.AutoMlServer // Input configuration for BatchPredict Action. The format of input depends on // the ML problem of the model used for prediction. As input source the // [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, // unless specified otherwise. The formats are represented in EBNF with commas // being literal and with non-terminal symbols defined near the end of this // comment. The formats are:

AutoML Vision

Classification
One or more CSV // files where each line is a single column: GCS_FILE_PATH The Google Cloud // Storage location of an image of up to 30MB in size. Supported extensions: // .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict // output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif // gs://folder/image3.png
Object Detection
One or // more CSV files where each line is a single column: GCS_FILE_PATH The Google // Cloud Storage location of an image of up to 30MB in size. Supported // extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch // predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif // gs://folder/image3.png

AutoML Video Intelligence

//
Classification
One or more // CSV files where each line is a single column: // GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END `GCS_FILE_PATH` is the // Google Cloud Storage location of video up to 50GB in size and up to 3h in // duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. // `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the // video, and the end time must be after the start time. Sample rows: // gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 // gs://folder/vid2.mov,0,inf
Object Tracking
One // or more CSV files where each line is a single column: // GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END `GCS_FILE_PATH` is the // Google Cloud Storage location of video up to 50GB in size and up to 3h in // duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. // `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the // video, and the end time must be after the start time. Sample rows: // gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 // gs://folder/vid2.mov,0,inf

AutoML Natural // Language

Classification
// One or more CSV files where each line is a single column: GCS_FILE_PATH // `GCS_FILE_PATH` is the Google Cloud Storage location of a text file. // Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no // larger than 10MB in size. Sample rows: gs://folder/text1.txt // gs://folder/text2.pdf gs://folder/text3.tif
Sentiment // Analysis
One or more CSV files where each line is a single column: // GCS_FILE_PATH `GCS_FILE_PATH` is the Google Cloud Storage location of a text // file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be // no larger than 128kB in size. Sample rows: gs://folder/text1.txt // gs://folder/text2.pdf gs://folder/text3.tif
Entity // Extraction
One or more JSONL (JSON Lines) files that either provide // inline text or documents. You can only use one format, either inline text or // documents, for a single call to [AutoMl.BatchPredict]. Each JSONL file // contains a per line a proto that wraps a temporary user-assigned TextSnippet // ID (string up to 2000 characters long) called "id", a TextSnippet proto (in // JSON representation) and zero or more TextFeature protos. Any given text // snippet content must have 30,000 characters or less, and also be UTF-8 NFC // encoded (ASCII already is). The IDs provided should be unique. Each document // JSONL file contains, per line, a proto that wraps a Document proto with // `input_config` set. Each document cannot exceed 2MB in size. Supported // document extensions: .PDF, .TIF, .TIFF Each JSONL file must not exceed 100MB // in size, and no more than 20 JSONL files may be passed. Sample inline JSONL // file (Shown with artificial line breaks. Actual line breaks are denoted by // "\n".): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, // "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, // "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ // {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, // "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "Extended // sample content", "mime_type": "text/plain" } } Sample document JSONL file // (Shown with artificial line breaks. Actual line breaks are denoted by // "\n".): { "document": { "input_config": { "gcs_source": { "input_uris": [ // "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { // "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } //

AutoML Tables

See [Preparing your // training data](https://cloud.google.com/automl-tables/docs/predict-batch) // for more information. You can use either // [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or // [bigquery_source][BatchPredictInputConfig.bigquery_source]. **For // gcs_source:** CSV file(s), each by itself 10GB or smaller and total size // must be 100GB or smaller, where first file must have a header containing // column names. If the first row of a subsequent file is the same as the // header, then it is also treated as a header. All other rows contain values // for the corresponding columns. The column names must contain the model's // [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] // [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order // doesn't matter). The columns corresponding to the model's input feature // column specs must contain values compatible with the column spec's data // types. Prediction on all the rows, i.e. the CSV lines, will be attempted. // Sample rows from a CSV file: 
 "First Name","Last
// Name","Dob","Addresses"
// "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
// "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
//
**For bigquery_source:** The URI of a BigQuery table. The user data // size of the BigQuery table must be 100GB or smaller. The column names must // contain the model's // [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] // [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order // doesn't matter). The columns corresponding to the model's input feature // column specs must contain values compatible with the column spec's data // types. Prediction on all the rows of the table will be attempted.
//
**Input field definitions:** `GCS_FILE_PATH` : The path to a file on // Google Cloud Storage. For example, "gs://folder/video.avi". // `TIME_SEGMENT_START` : (`TIME_OFFSET`) Expresses a beginning, inclusive, of // a time segment within an example that has a time dimension (e.g. video). // `TIME_SEGMENT_END` : (`TIME_OFFSET`) Expresses an end, exclusive, of a time // segment within n example that has a time dimension (e.g. video). // `TIME_OFFSET` : A number of seconds as measured from the start of an example // (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is // allowed, and it means the end of the example. **Errors:** If any of the // provided CSV files can't be parsed or if more than certain percent of CSV // rows cannot be processed then the operation fails and prediction does not // happen. Regardless of overall success or failure the per-row failures, up to // a certain count cap, will be listed in Operation.metadata.partial_failures. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BatchPredictInputConfig = src.BatchPredictInputConfig type BatchPredictInputConfig_GcsSource = src.BatchPredictInputConfig_GcsSource // Details of BatchPredict operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BatchPredictOperationMetadata = src.BatchPredictOperationMetadata // Further describes this batch predict's output. Supplements // [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BatchPredictOperationMetadata_BatchPredictOutputInfo = src.BatchPredictOperationMetadata_BatchPredictOutputInfo type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory // Output configuration for BatchPredict Action. As destination the // [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] // must be set unless specified otherwise for a domain. If gcs_destination is // set then in the given directory a new directory is created. Its name will be // "prediction--", 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" : "" 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" : "" 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" // : "" 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" : "" 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 file (or document) in the // text snippet (or document) proto and a list of zero or more // AnnotationPayload protos (called annotations), which have classification // detail populated. A single text file (or document) will be listed only once // with all its annotations, and its annotations will never be split across // files. If prediction for any input file (or document) failed (partially or // completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., // `errors_N.jsonl` files will be created (N depends on total number of failed // predictions). These files will have a JSON representation of a proto that // wraps input file followed by exactly one // [`google.rpc.Status`](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 file (or document) in the text snippet (or document) // proto and a list of zero or more AnnotationPayload protos (called // annotations), which have text_sentiment detail populated. A single text file // (or document) will be listed only once with all its annotations, and its // annotations will never be split across files. If prediction for any input // file (or document) failed (partially or completely), then additional // `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be // created (N depends on total number of failed predictions). These files will // have a JSON representation of a proto that wraps input file followed by // exactly one // [`google.rpc.Status`](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" : "" (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.v1p1beta.BatchPredictOutputConfig.gcs_destination] // or // [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] // is set (either is allowed). Google Cloud Storage case: In the created // directory files `tables_1.csv`, `tables_2.csv`,..., `tables_N.csv` will be // created, where N may be 1, and depends on the total number of the // successfully predicted rows. For all CLASSIFICATION // [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: // Each .csv file will contain a header, listing all columns' // [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] given // on input followed by M target column names in the format of // "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>__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.v1p1beta.TablesAnnotation.score]. For // REGRESSION and FORECASTING // [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: // Each .csv file will contain a header, listing all columns' // [display_name-s][google.cloud.automl.v1p1beta.display_name] given on input // followed by the predicted target column with name in the format of // "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" // Subsequent lines will contain the respective values of successfully // predicted rows, with the last, i.e. the target, column having the predicted // target value. If prediction for any rows failed, then an additional // `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be created (N // depends on total number of failed rows). These files will have analogous // format as `tables_*.csv`, but always with a single target column having // [`google.rpc.Status`](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.v1p1beta.OutputConfig.bigquery_destination] // pointing to a BigQuery project must be set. In the given project a new // dataset will be created with name // `prediction__` where // 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.v1p1beta.ColumnSpec.display_name] // followed by the target column with name in the format of // "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1p1beta.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.v1p1beta.AnnotationPayload], // represented as STRUCT-s, containing // [TablesAnnotation][google.cloud.automl.v1p1beta.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.v1p1beta.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1p1beta.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/apiv1/automlpb type BatchPredictOutputConfig = src.BatchPredictOutputConfig type BatchPredictOutputConfig_GcsDestination = src.BatchPredictOutputConfig_GcsDestination // Request message for // [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BatchPredictRequest = src.BatchPredictRequest // Result of the Batch Predict. This message is returned in // [response][google.longrunning.Operation.response] of the operation returned // by the // [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BatchPredictResult = src.BatchPredictResult // 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/apiv1/automlpb type BoundingBoxMetricsEntry = src.BoundingBoxMetricsEntry // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type BoundingBoxMetricsEntry_ConfidenceMetricsEntry = src.BoundingBoxMetricsEntry_ConfidenceMetricsEntry // 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/apiv1/automlpb type BoundingPoly = src.BoundingPoly // Contains annotation details specific to classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ClassificationAnnotation = src.ClassificationAnnotation // 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/apiv1/automlpb type ClassificationEvaluationMetrics = src.ClassificationEvaluationMetrics // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ClassificationEvaluationMetrics_ConfidenceMetricsEntry = src.ClassificationEvaluationMetrics_ConfidenceMetricsEntry // Confusion matrix of the model running the classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ClassificationEvaluationMetrics_ConfusionMatrix = src.ClassificationEvaluationMetrics_ConfusionMatrix // Output only. A row in the confusion matrix. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ClassificationEvaluationMetrics_ConfusionMatrix_Row = src.ClassificationEvaluationMetrics_ConfusionMatrix_Row // Type of the classification problem. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ClassificationType = src.ClassificationType // Details of CreateDataset operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type CreateDatasetOperationMetadata = src.CreateDatasetOperationMetadata // Request message for // [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type CreateDatasetRequest = src.CreateDatasetRequest // Details of CreateModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type CreateModelOperationMetadata = src.CreateModelOperationMetadata // Request message for // [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type CreateModelRequest = src.CreateModelRequest // 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/apiv1/automlpb type Dataset = src.Dataset type Dataset_ImageClassificationDatasetMetadata = src.Dataset_ImageClassificationDatasetMetadata type Dataset_ImageObjectDetectionDatasetMetadata = src.Dataset_ImageObjectDetectionDatasetMetadata type Dataset_TextClassificationDatasetMetadata = src.Dataset_TextClassificationDatasetMetadata type Dataset_TextExtractionDatasetMetadata = src.Dataset_TextExtractionDatasetMetadata type Dataset_TextSentimentDatasetMetadata = src.Dataset_TextSentimentDatasetMetadata type Dataset_TranslationDatasetMetadata = src.Dataset_TranslationDatasetMetadata // Request message for // [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DeleteDatasetRequest = src.DeleteDatasetRequest // Request message for // [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DeleteModelRequest = src.DeleteModelRequest // Details of operations that perform deletes of any entities. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DeleteOperationMetadata = src.DeleteOperationMetadata // Details of DeployModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DeployModelOperationMetadata = src.DeployModelOperationMetadata // Request message for // [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DeployModelRequest = src.DeployModelRequest type DeployModelRequest_ImageClassificationModelDeploymentMetadata = src.DeployModelRequest_ImageClassificationModelDeploymentMetadata type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata = src.DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata // A structured text document e.g. a PDF. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Document = src.Document // Message that describes dimension of a document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DocumentDimensions = src.DocumentDimensions // Unit of the document dimension. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DocumentDimensions_DocumentDimensionUnit = src.DocumentDimensions_DocumentDimensionUnit // Input configuration of a [Document][google.cloud.automl.v1.Document]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type DocumentInputConfig = src.DocumentInputConfig // Describes the layout information of a // [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the // document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Document_Layout = src.Document_Layout // The type of TextSegment in the context of the original document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Document_Layout_TextSegmentType = src.Document_Layout_TextSegmentType // Example data used for training or prediction. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExamplePayload = src.ExamplePayload type ExamplePayload_Document = src.ExamplePayload_Document type ExamplePayload_Image = src.ExamplePayload_Image type ExamplePayload_TextSnippet = src.ExamplePayload_TextSnippet // Details of ExportData operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportDataOperationMetadata = src.ExportDataOperationMetadata // Further describes this export data's output. Supplements // [OutputConfig][google.cloud.automl.v1.OutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportDataOperationMetadata_ExportDataOutputInfo = src.ExportDataOperationMetadata_ExportDataOutputInfo type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory = src.ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory // Request message for // [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportDataRequest = src.ExportDataRequest // Details of ExportModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportModelOperationMetadata = src.ExportModelOperationMetadata // Further describes the output of model export. Supplements // [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportModelOperationMetadata_ExportModelOutputInfo = src.ExportModelOperationMetadata_ExportModelOutputInfo // Request message for // [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need // to be enabled for exporting, otherwise an error code will be returned. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ExportModelRequest = src.ExportModelRequest // The Google Cloud Storage location where the output is to be written to. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GcsDestination = src.GcsDestination // The Google Cloud Storage location for the input content. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GcsSource = src.GcsSource // Request message for // [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GetAnnotationSpecRequest = src.GetAnnotationSpecRequest // Request message for // [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GetDatasetRequest = src.GetDatasetRequest // Request message for // [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GetModelEvaluationRequest = src.GetModelEvaluationRequest // Request message for // [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type GetModelRequest = src.GetModelRequest // A representation of an image. Only images up to 30MB in size are supported. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Image = src.Image // Dataset metadata that is specific to image classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageClassificationDatasetMetadata = src.ImageClassificationDatasetMetadata // Model deployment metadata specific to Image Classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageClassificationModelDeploymentMetadata = src.ImageClassificationModelDeploymentMetadata // Model metadata for image classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageClassificationModelMetadata = src.ImageClassificationModelMetadata // Annotation details for image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageObjectDetectionAnnotation = src.ImageObjectDetectionAnnotation // Dataset metadata specific to image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageObjectDetectionDatasetMetadata = src.ImageObjectDetectionDatasetMetadata // 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/apiv1/automlpb type ImageObjectDetectionEvaluationMetrics = src.ImageObjectDetectionEvaluationMetrics // Model deployment metadata specific to Image Object Detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageObjectDetectionModelDeploymentMetadata = src.ImageObjectDetectionModelDeploymentMetadata // Model metadata specific to image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImageObjectDetectionModelMetadata = src.ImageObjectDetectionModelMetadata type Image_ImageBytes = src.Image_ImageBytes // Details of ImportData operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImportDataOperationMetadata = src.ImportDataOperationMetadata // Request message for // [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ImportDataRequest = src.ImportDataRequest // Input configuration for // [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action. The // format of input depends on dataset_metadata the Dataset into which the // import is happening has. As input source the // [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, // unless specified otherwise. Additionally any input .CSV file by itself must // be 100MB or smaller, unless specified otherwise. If an "example" file (that // is, image, video etc.) with identical content (even if it had different // `GCS_FILE_PATH`) is mentioned multiple times, then its label, bounding boxes // etc. are appended. The same file should be always provided with the same // `ML_USE` and `GCS_FILE_PATH`, if it is not, then these values are // nondeterministically selected from the given ones. The formats are // represented in EBNF with commas being literal and with non-terminal symbols // defined near the end of this comment. The formats are:

AutoML // Vision

Classification
// See [Preparing your training // data](https://cloud.google.com/vision/automl/docs/prepare) for more // information. CSV file(s) with each line in format: // ML_USE,GCS_FILE_PATH,LABEL,LABEL,... * `ML_USE` - Identifies the data set // that the current row (file) applies to. This value can be one of the // following: * `TRAIN` - Rows in this file are used to train the model. * // `TEST` - Rows in this file are used to test the model during training. * // `UNASSIGNED` - Rows in this file are not categorized. They are Automatically // divided into train and test data. 80% for training and 20% for testing. - // `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to // 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, // .ICO. * `LABEL` - A label that identifies the object in the image. For the // `MULTICLASS` classification type, at most one `LABEL` is allowed per image. // If an image has not yet been labeled, then it should be mentioned just once // with no `LABEL`. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy // TEST,gs://folder/image2.jpg,dandelion,tulip,rose // UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg //
Object Detection
See [Preparing your training // data](https://cloud.google.com/vision/automl/object-detection/docs/prepare) // for more information. A CSV file(s) with each line in format: // ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,) * `ML_USE` - // Identifies the data set that the current row (file) applies to. This value // can be one of the following: * `TRAIN` - Rows in this file are used to train // the model. * `TEST` - Rows in this file are used to test the model during // training. * `UNASSIGNED` - Rows in this file are not categorized. They are // Automatically divided into train and test data. 80% for training and 20% for // testing. - `GCS_FILE_PATH` - The Google Cloud Storage location of an image // of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image // is assumed to be exhaustively labeled. - `LABEL` - A label that identifies // the object in the image specified by the `BOUNDING_BOX`. - `BOUNDING BOX` - // The vertices of an object in the example image. The minimum allowed // `BOUNDING_BOX` edge length is 0.01, and no more than 500 `BOUNDING_BOX` // instances per image are allowed (one `BOUNDING_BOX` per line). If an image // has no looked for objects then it should be mentioned just once with no // LABEL and the ",,,,,,," in place of the `BOUNDING_BOX`. **Four sample // rows:** TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, // TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, // UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 // TEST,gs://folder/im3.png,,,,,,,,,

AutoML Video // Intelligence

Classification
See [Preparing // your training // data](https://cloud.google.com/video-intelligence/automl/docs/prepare) for // more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH // For `ML_USE`, do not use `VALIDATE`. `GCS_FILE_PATH` is the path to another // .csv file that describes training example for a given `ML_USE`, using the // following row format: // GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here // `GCS_FILE_PATH` leads to a video of up to 50GB in size and up to 3h // duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. // `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the // video, and the end time must be after the start time. Any segment of a video // which has one or more labels on it, is considered a hard negative for all // other labels. Any segment with no labels on it is considered to be unknown. // If a whole video is unknown, then it should be mentioned just once with ",," // in place of `LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END`. Sample top level // CSV file: TRAIN,gs://folder/train_videos.csv // TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv // Sample rows of a CSV file for a particular ML_USE: // gs://folder/video1.avi,car,120,180.000021 // gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 // gs://folder/vid3.avi,,,
Object Tracking
See // [Preparing your training // data](/video-intelligence/automl/object-tracking/docs/prepare) for more // information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For // `ML_USE`, do not use `VALIDATE`. `GCS_FILE_PATH` is the path to another .csv // file that describes training example for a given `ML_USE`, using the // following row format: // GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or // GCS_FILE_PATH,,,,,,,,,, Here `GCS_FILE_PATH` leads to a video of up to 50GB // in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, // .AVI. Providing `INSTANCE_ID`s can help to obtain a better model. When a // specific labeled entity leaves the video frame, and shows up afterwards it // is not required, albeit preferable, that the same `INSTANCE_ID` is given to // it. `TIMESTAMP` must be within the length of the video, the `BOUNDING_BOX` // is assumed to be drawn on the closest video's frame to the `TIMESTAMP`. Any // mentioned by the `TIMESTAMP` frame is expected to be exhaustively labeled // and no more than 500 `BOUNDING_BOX`-es per frame are allowed. If a whole // video is unknown, then it should be mentioned just once with ",,,,,,,,,," in // place of `LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX`. Sample top level CSV // file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv // UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for // a particular ML_USE: // gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 // gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 // gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 // gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, // gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, // gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, // gs://folder/video2.avi,,,,,,,,,,,

AutoML Natural // Language

Entity // Extraction
See [Preparing your training // data](/natural-language/automl/entity-analysis/docs/prepare) for more // information. One or more CSV file(s) with each line in the following format: // ML_USE,GCS_FILE_PATH * `ML_USE` - Identifies the data set that the current // row (file) applies to. This value can be one of the following: * `TRAIN` - // Rows in this file are used to train the model. * `TEST` - Rows in this file // are used to test the model during training. * `UNASSIGNED` - Rows in this // file are not categorized. They are Automatically divided into train and test // data. 80% for training and 20% for testing.. - `GCS_FILE_PATH` - a // Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that // contains in-line text in-line as documents for model training. After the // training data set has been determined from the `TRAIN` and `UNASSIGNED` CSV // files, the training data is divided into train and validation data sets. 70% // for training and 30% for validation. For example: // TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl // TEST,gs://folder/file3.jsonl **In-line JSONL files** In-line .JSONL files // contain, per line, a JSON document that wraps a // [`text_snippet`][google.cloud.automl.v1.TextSnippet] field followed by one // or more [`annotations`][google.cloud.automl.v1.AnnotationPayload] fields, // which have `display_name` and `text_extraction` fields to describe the // entity from the text snippet. Multiple JSON documents can be separated using // line breaks (\n). The supplied text must be annotated exhaustively. For // example, if you include the text "horse", but do not label it as "animal", // then "horse" is assumed to not be an "animal". Any given text snippet // content must have 30,000 characters or less, and also be UTF-8 NFC encoded. // ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": // { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", // "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } // }, { "display_name": "vehicle", "text_extraction": { "text_segment": // {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", // "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } // } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": // [ { "display_name": "animal", "text_extraction": { "text_segment": // {"start_offset": 5, "end_offset": 7} } } ] } **JSONL files that reference // documents** .JSONL files contain, per line, a JSON document that wraps a // `input_config` that contains the path to a source document. Multiple JSON // documents can be separated using line breaks (\n). Supported document // extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { // "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { // "document": { "input_config": { "gcs_source": { "input_uris": [ // "gs://folder/document2.tif" ] } } } } **In-line JSONL files with document // layout information** **Note:** You can only annotate documents using the UI. // The format described below applies to annotated documents exported using the // UI or `exportData`. In-line .JSONL files for documents contain, per line, a // JSON document that wraps a `document` field that provides the textual // content of the document and the layout information. For example: { // "document": { "document_text": { "content": "dog car cat" } "layout": [ { // "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, // "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, // "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, // "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, // "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { // "display_name": "animal", "text_extraction": { "text_segment": // {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", // "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } // }, { "display_name": "animal", "text_extraction": { "text_segment": // {"start_offset": 8, "end_offset": 11} } }, ], //
Classification
See [Preparing your training // data](https://cloud.google.com/natural-language/automl/docs/prepare) for // more information. One or more CSV file(s) with each line in the following // format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... * `ML_USE` - // Identifies the data set that the current row (file) applies to. This value // can be one of the following: * `TRAIN` - Rows in this file are used to train // the model. * `TEST` - Rows in this file are used to test the model during // training. * `UNASSIGNED` - Rows in this file are not categorized. They are // Automatically divided into train and test data. 80% for training and 20% for // testing. - `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a // pattern. If the column content is a valid Google Cloud Storage file path, // that is, prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, // if the content is enclosed in double quotes (""), it is treated as a // `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a file with // supported extension and UTF-8 encoding, for example, // "gs://folder/content.txt" AutoML imports the file content as a text snippet. // For `TEXT_SNIPPET`, AutoML imports the column content excluding quotes. In // both cases, size of the content must be 10MB or less in size. For zip files, // the size of each file inside the zip must be 10MB or less in size. For the // `MULTICLASS` classification type, at most one `LABEL` is allowed. The // `ML_USE` and `LABEL` columns are optional. Supported file extensions: .TXT, // .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV // row. Sample rows: TRAIN,"They have bad food and very // rude",RudeService,BadFood gs://folder/content.txt,SlowService // TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood //
Sentiment Analysis
See [Preparing your training // data](https://cloud.google.com/natural-language/automl/docs/prepare) for // more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET // | GCS_FILE_PATH),SENTIMENT * `ML_USE` - Identifies the data set that the // current row (file) applies to. This value can be one of the following: * // `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in // this file are used to test the model during training. * `UNASSIGNED` - Rows // in this file are not categorized. They are Automatically divided into train // and test data. 80% for training and 20% for testing. - `TEXT_SNIPPET` and // `GCS_FILE_PATH` are distinguished by a pattern. If the column content is a // valid Google Cloud Storage file path, that is, prefixed by "gs://", it is // treated as a `GCS_FILE_PATH`. Otherwise, if the content is enclosed in // double quotes (""), it is treated as a `TEXT_SNIPPET`. For `GCS_FILE_PATH`, // the path must lead to a file with supported extension and UTF-8 encoding, // for example, "gs://folder/content.txt" AutoML imports the file content as a // text snippet. For `TEXT_SNIPPET`, AutoML imports the column content // excluding quotes. In both cases, size of the content must be 128kB or less // in size. For zip files, the size of each file inside the zip must be 128kB // or less in size. The `ML_USE` and `SENTIMENT` columns are optional. // Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP - `SENTIMENT` - An // integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max // (inclusive). Describes the ordinal of the sentiment - higher value means a // more positive sentiment. All the values are completely relative, i.e. // neither 0 needs to mean a negative or neutral sentiment nor sentiment_max // needs to mean a positive one - it is just required that 0 is the least // positive sentiment in the data, and sentiment_max is the most positive one. // The SENTIMENT shouldn't be confused with "score" or "magnitude" from the // previous Natural Language Sentiment Analysis API. All SENTIMENT values // between 0 and sentiment_max must be represented in the imported data. On // prediction the same 0 to sentiment_max range will be used. The difference // between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 // may be similar whereas the difference between 2 and 3 may be large. Sample // rows: TRAIN,"@freewrytin this is way too good for your product",2 // gs://folder/content.txt,3 TEST,gs://folder/document.pdf // VALIDATE,gs://folder/text_files.zip,2

AutoML // Tables

See // [Preparing your training // data](https://cloud.google.com/automl-tables/docs/prepare) for more // information. You can use either // [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or // [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All // input is concatenated into a single // [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id] // **For gcs_source:** CSV file(s), where the first row of the first file is // the header, containing unique column names. If the first row of a subsequent // file is the same as the header, then it is also treated as a header. All // other rows contain values for the corresponding columns. Each .CSV file by // itself must be 10GB or smaller, and their total size must be 100GB or // smaller. First three sample rows of a CSV file: 
 "Id","First
// Name","Last Name","Dob","Addresses"
// "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
// "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
//
**For bigquery_source:** An URI of a BigQuery table. The user data // size of the BigQuery table must be 100GB or smaller. An imported table must // have between 2 and 1,000 columns, inclusive, and between 1000 and // 100,000,000 rows, inclusive. There are at most 5 import data running in // parallel.
**Input field definitions:** `ML_USE` : ("TRAIN" // | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) // should be used for model training. "UNASSIGNED" can be used when user has no // preference. `GCS_FILE_PATH` : The path to a file on Google Cloud Storage. // For example, "gs://folder/image1.png". `LABEL` : A display name of an object // on an image, video etc., e.g. "dog". Must be up to 32 characters long and // can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and // ASCII digits 0-9. For each label an AnnotationSpec is created which // display_name becomes the label; AnnotationSpecs are given back in // predictions. `INSTANCE_ID` : A positive integer that identifies a specific // instance of a labeled entity on an example. Used e.g. to track two cars on a // video while being able to tell apart which one is which. `BOUNDING_BOX` : // (`VERTEX,VERTEX,VERTEX,VERTEX` | `VERTEX,,,VERTEX,,`) A rectangle parallel // to the frame of the example (image, video). If 4 vertices are given they are // connected by edges in the order provided, if 2 are given they are recognized // as diagonally opposite vertices of the rectangle. `VERTEX` : // (`COORDINATE,COORDINATE`) First coordinate is horizontal (x), the second is // vertical (y). `COORDINATE` : A float in 0 to 1 range, relative to total // length of image or video in given dimension. For fractions the leading // non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left. // `TIME_SEGMENT_START` : (`TIME_OFFSET`) Expresses a beginning, inclusive, of // a time segment within an example that has a time dimension (e.g. video). // `TIME_SEGMENT_END` : (`TIME_OFFSET`) Expresses an end, exclusive, of a time // segment within n example that has a time dimension (e.g. video). // `TIME_OFFSET` : A number of seconds as measured from the start of an example // (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is // allowed, and it means the end of the example. `TEXT_SNIPPET` : The content // of a text snippet, UTF-8 encoded, enclosed within double quotes (""). // `DOCUMENT` : A field that provides the textual content with document and the // layout information. **Errors:** If any of the provided CSV files can't be // parsed or if more than certain percent of CSV rows cannot be processed then // the operation fails and nothing is imported. Regardless of overall success // or failure the per-row failures, up to a certain count cap, is listed in // Operation.metadata.partial_failures. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type InputConfig = src.InputConfig type InputConfig_GcsSource = src.InputConfig_GcsSource // Request message for // [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListDatasetsRequest = src.ListDatasetsRequest // Response message for // [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListDatasetsResponse = src.ListDatasetsResponse // Request message for // [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListModelEvaluationsRequest = src.ListModelEvaluationsRequest // Response message for // [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListModelEvaluationsResponse = src.ListModelEvaluationsResponse // Request message for // [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListModelsRequest = src.ListModelsRequest // Response message for // [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ListModelsResponse = src.ListModelsResponse // API proto representing a trained machine learning model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Model = src.Model // Evaluation results of a model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ModelEvaluation = src.ModelEvaluation type ModelEvaluation_ClassificationEvaluationMetrics = src.ModelEvaluation_ClassificationEvaluationMetrics type ModelEvaluation_ImageObjectDetectionEvaluationMetrics = src.ModelEvaluation_ImageObjectDetectionEvaluationMetrics type ModelEvaluation_TextExtractionEvaluationMetrics = src.ModelEvaluation_TextExtractionEvaluationMetrics type ModelEvaluation_TextSentimentEvaluationMetrics = src.ModelEvaluation_TextSentimentEvaluationMetrics type ModelEvaluation_TranslationEvaluationMetrics = src.ModelEvaluation_TranslationEvaluationMetrics // Output configuration for ModelExport Action. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type ModelExportOutputConfig = src.ModelExportOutputConfig type ModelExportOutputConfig_GcsDestination = src.ModelExportOutputConfig_GcsDestination // Deployment state of the model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type Model_DeploymentState = src.Model_DeploymentState type Model_ImageClassificationModelMetadata = src.Model_ImageClassificationModelMetadata type Model_ImageObjectDetectionModelMetadata = src.Model_ImageObjectDetectionModelMetadata type Model_TextClassificationModelMetadata = src.Model_TextClassificationModelMetadata type Model_TextExtractionModelMetadata = src.Model_TextExtractionModelMetadata type Model_TextSentimentModelMetadata = src.Model_TextSentimentModelMetadata type Model_TranslationModelMetadata = src.Model_TranslationModelMetadata // 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/apiv1/automlpb type NormalizedVertex = src.NormalizedVertex // Metadata used across all long running operations returned by AutoML API. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type OperationMetadata = src.OperationMetadata type OperationMetadata_BatchPredictDetails = src.OperationMetadata_BatchPredictDetails type OperationMetadata_CreateDatasetDetails = src.OperationMetadata_CreateDatasetDetails type OperationMetadata_CreateModelDetails = src.OperationMetadata_CreateModelDetails type OperationMetadata_DeleteDetails = src.OperationMetadata_DeleteDetails type OperationMetadata_DeployModelDetails = src.OperationMetadata_DeployModelDetails type OperationMetadata_ExportDataDetails = src.OperationMetadata_ExportDataDetails type OperationMetadata_ExportModelDetails = src.OperationMetadata_ExportModelDetails type OperationMetadata_ImportDataDetails = src.OperationMetadata_ImportDataDetails type OperationMetadata_UndeployModelDetails = src.OperationMetadata_UndeployModelDetails // - For Translation: CSV file `translation.csv`, with each line in format: // ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes // examples that have given ML_USE, using the following row format per line: // TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language) - For // Tables: Output depends on whether the dataset was imported from Google Cloud // Storage or BigQuery. Google Cloud Storage case: // [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.gcs_destination] // must be set. Exported are CSV file(s) `tables_1.csv`, // `tables_2.csv`,...,`tables_N.csv` with each having as header line the // table's column names, and all other lines contain values for the header // columns. BigQuery case: // [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] // pointing to a BigQuery project must be set. In the given project a new // dataset will be created with name // `export_data__` where // 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/apiv1/automlpb type OutputConfig = src.OutputConfig type OutputConfig_GcsDestination = src.OutputConfig_GcsDestination // Request message for // [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type PredictRequest = src.PredictRequest // Response message for // [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type PredictResponse = src.PredictResponse // 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/apiv1/automlpb type PredictionServiceClient = src.PredictionServiceClient // PredictionServiceServer is the server API for PredictionService service. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type PredictionServiceServer = src.PredictionServiceServer // Dataset metadata for classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextClassificationDatasetMetadata = src.TextClassificationDatasetMetadata // Model metadata that is specific to text classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextClassificationModelMetadata = src.TextClassificationModelMetadata // Annotation for identifying spans of text. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextExtractionAnnotation = src.TextExtractionAnnotation type TextExtractionAnnotation_TextSegment = src.TextExtractionAnnotation_TextSegment // Dataset metadata that is specific to text extraction // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextExtractionDatasetMetadata = src.TextExtractionDatasetMetadata // Model evaluation metrics for text extraction problems. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextExtractionEvaluationMetrics = src.TextExtractionEvaluationMetrics // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry = src.TextExtractionEvaluationMetrics_ConfidenceMetricsEntry // Model metadata that is specific to text extraction. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextExtractionModelMetadata = src.TextExtractionModelMetadata // 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/apiv1/automlpb type TextSegment = src.TextSegment // Contains annotation details specific to text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextSentimentAnnotation = src.TextSentimentAnnotation // Dataset metadata for text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextSentimentDatasetMetadata = src.TextSentimentDatasetMetadata // Model evaluation metrics for text sentiment problems. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextSentimentEvaluationMetrics = src.TextSentimentEvaluationMetrics // Model metadata that is specific to text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextSentimentModelMetadata = src.TextSentimentModelMetadata // A representation of a text snippet. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TextSnippet = src.TextSnippet // Annotation details specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TranslationAnnotation = src.TranslationAnnotation // Dataset metadata that is specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TranslationDatasetMetadata = src.TranslationDatasetMetadata // Evaluation metrics for the dataset. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TranslationEvaluationMetrics = src.TranslationEvaluationMetrics // Model metadata that is specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type TranslationModelMetadata = src.TranslationModelMetadata // Details of UndeployModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UndeployModelOperationMetadata = src.UndeployModelOperationMetadata // Request message for // [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UndeployModelRequest = src.UndeployModelRequest // UnimplementedAutoMlServer can be embedded to have forward compatible // implementations. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UnimplementedAutoMlServer = src.UnimplementedAutoMlServer // UnimplementedPredictionServiceServer can be embedded to have forward // compatible implementations. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UnimplementedPredictionServiceServer = src.UnimplementedPredictionServiceServer // Request message for // [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset] // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UpdateDatasetRequest = src.UpdateDatasetRequest // Request message for // [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel] // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb type UpdateModelRequest = src.UpdateModelRequest // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient { return src.NewAutoMlClient(cc) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient { return src.NewPredictionServiceClient(cc) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer) { src.RegisterAutoMlServer(s, srv) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer) { src.RegisterPredictionServiceServer(s, srv) }