package google.cloud.automl.v1
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service AutoMl
service.proto:55AutoML Server API. The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted. An ID of a resource is the last element of the item's resource name. For `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`, then the id for the item is `{dataset_id}`. Currently the only supported `location_id` is "us-central1". On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
rpc CreateDataset (, )
service.proto:61Creates a dataset.
message
service.proto:240Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].
string parent = 1
The resource name of the project to create the dataset for.
optional dataset = 2
The dataset to create.
rpc GetDataset (, )
service.proto:70Gets a dataset.
message
service.proto:250Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].
string name = 1
The resource name of the dataset to retrieve.
rpc ListDatasets (, )
service.proto:77Lists datasets in a project.
message
service.proto:257Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
string parent = 1
The resource name of the project from which to list datasets.
string filter = 3
An expression for filtering the results of the request. * `dataset_metadata` - for existence of the case (e.g. image_classification_dataset_metadata:*). Some examples of using the filter are: * `translation_dataset_metadata:*` --> The dataset has translation_dataset_metadata.
int32 page_size = 4
Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
string page_token = 6
A token identifying a page of results for the server to return Typically obtained via [ListDatasetsResponse.next_page_token][google.cloud.automl.v1.ListDatasetsResponse.next_page_token] of the previous [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets] call.
message
service.proto:285Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
repeated datasets = 1
The datasets read.
string next_page_token = 2
A token to retrieve next page of results. Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1.ListDatasetsRequest.page_token] to obtain that page.
rpc UpdateDataset (, )
service.proto:84Updates a dataset.
message
service.proto:298Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]
optional dataset = 1
The dataset which replaces the resource on the server.
optional update_mask = 2
Required. The update mask applies to the resource.
rpc DeleteDataset (, )
service.proto:96Deletes a dataset and all of its contents. Returns empty response in the [response][google.longrunning.Operation.response] field when it completes, and `delete_details` in the [metadata][google.longrunning.Operation.metadata] field.
message
service.proto:308Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].
string name = 1
The resource name of the dataset to delete.
rpc ImportData (, )
service.proto:104Imports data into a dataset.
message
service.proto:315Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].
string name = 1
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
optional input_config = 3
Required. The desired input location and its domain specific semantics, if any.
rpc ExportData (, )
service.proto:114Exports dataset's data to the provided output location. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:327Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].
string name = 1
Required. The resource name of the dataset.
optional output_config = 3
Required. The desired output location.
rpc GetAnnotationSpec (, )
service.proto:122Gets an annotation spec.
message
service.proto:337Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
string name = 1
The resource name of the annotation spec to retrieve.
message
annotation_spec.proto:30A definition of an annotation spec.
string name = 1
Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
string display_name = 2
Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and must match the regexp `[a-zA-Z0-9_]+`. (_), and ASCII digits 0-9.
int32 example_count = 9
Output only. The number of examples in the parent dataset labeled by the annotation spec.
rpc CreateModel (, )
service.proto:134Creates a model. Returns a Model in the [response][google.longrunning.Operation.response] field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.
message
service.proto:344Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
string parent = 1
Resource name of the parent project where the model is being created.
optional model = 4
The model to create.
rpc GetModel (, )
service.proto:142Gets a model.
message
service.proto:354Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].
string name = 1
Resource name of the model.
rpc ListModels (, )
service.proto:149Lists models.
message
service.proto:361Request message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
string parent = 1
Resource name of the project, from which to list the models.
string filter = 3
An expression for filtering the results of the request. * `model_metadata` - for existence of the case (e.g. image_classification_model_metadata:*). * `dataset_id` - for = or !=. Some examples of using the filter are: * `image_classification_model_metadata:*` --> The model has image_classification_model_metadata. * `dataset_id=5` --> The model was created from a dataset with ID 5.
int32 page_size = 4
Requested page size.
string page_token = 6
A token identifying a page of results for the server to return Typically obtained via [ListModelsResponse.next_page_token][google.cloud.automl.v1.ListModelsResponse.next_page_token] of the previous [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels] call.
message
service.proto:389Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
repeated model = 1
List of models in the requested page.
string next_page_token = 2
A token to retrieve next page of results. Pass to [ListModelsRequest.page_token][google.cloud.automl.v1.ListModelsRequest.page_token] to obtain that page.
rpc DeleteModel (, )
service.proto:160Deletes a model. Returns `google.protobuf.Empty` in the [response][google.longrunning.Operation.response] field when it completes, and `delete_details` in the [metadata][google.longrunning.Operation.metadata] field.
message
service.proto:402Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].
string name = 1
Resource name of the model being deleted.
rpc UpdateModel (, )
service.proto:167Updates a model.
message
service.proto:409Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]
optional model = 1
The model which replaces the resource on the server.
optional update_mask = 2
Required. The update mask applies to the resource.
rpc DeployModel (, )
service.proto:186Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. Only applicable for Text Classification, Image Object Detection; all other domains manage deployment automatically. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:419Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
oneof model_deployment_metadata
The per-domain specific deployment parameters.
image_object_detection_model_deployment_metadata = 2
Model deployment metadata specific to Image Object Detection.
image_classification_model_deployment_metadata = 4
Model deployment metadata specific to Image Classification.
string name = 1
Resource name of the model to deploy.
rpc UndeployModel (, )
service.proto:200Undeploys a model. If the model is not deployed this method has no effect. Only applicable for Text Classification, Image Object Detection; all other domains manage deployment automatically. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:437Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
string name = 1
Resource name of the model to undeploy.
rpc ExportModel (, )
service.proto:215Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig]. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:445Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.
string name = 1
Required. The resource name of the model to export.
optional output_config = 3
Required. The desired output location and configuration.
rpc GetModelEvaluation (, )
service.proto:223Gets a model evaluation.
message
service.proto:455Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
string name = 1
Resource name for the model evaluation.
Lists model evaluations.
message
service.proto:462Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
string parent = 1
Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
string filter = 3
An expression for filtering the results of the request. * `annotation_spec_id` - for =, != or existence. See example below for the last. Some examples of using the filter are: * `annotation_spec_id!=4` --> The model evaluation was done for annotation spec with ID different than 4. * `NOT annotation_spec_id:*` --> The model evaluation was done for aggregate of all annotation specs.
int32 page_size = 4
Requested page size.
string page_token = 6
A token identifying a page of results for the server to return. Typically obtained via [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1.ListModelEvaluationsResponse.next_page_token] of the previous [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] call.
message
service.proto:495Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
repeated model_evaluation = 1
List of model evaluations in the requested page.
string next_page_token = 2
A token to retrieve next page of results. Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1.ListModelEvaluationsRequest.page_token] field of a new [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] request to obtain that page.
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
rpc Predict (, )
prediction_service.proto:60Perform an online prediction. The prediction result will be directly returned in the response. Available for following ML problems, and their expected request payloads: * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded. * Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded. * Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded. * Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.
message
prediction_service.proto:89Request message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
string name = 1
Name of the model requested to serve the prediction.
optional payload = 2
Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
map<string, string> params = 3
Additional domain-specific parameters, any string must be up to 25000 characters long. * For Image Classification: `score_threshold` - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Object Detection: `score_threshold` - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` - (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server.
message
prediction_service.proto:118Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
repeated payload = 1
Prediction result. Translation and Text Sentiment will return precisely one payload.
optional preprocessed_input = 3
The preprocessed example that AutoML actually makes prediction on. Empty if AutoML does not preprocess the input example. * For Text Extraction: If the input is a .pdf file, the OCR'ed text will be provided in [document_text][google.cloud.automl.v1p1beta.Document.document_text]. * For Text Classification: If the input is a .pdf file, the OCR'ed trucated text will be provided in [document_text][google.cloud.automl.v1p1beta.Document.document_text]. * For Text Sentiment: If the input is a .pdf file, the OCR'ed trucated text will be provided in [document_text][google.cloud.automl.v1p1beta.Document.document_text].
map<string, string> metadata = 2
Additional domain-specific prediction response metadata. * For Image Object Detection: `max_bounding_box_count` - (int64) At most that many bounding boxes per image could have been returned. * For Text Sentiment: `sentiment_score` - (float, deprecated) A value between -1 and 1, -1 maps to least positive sentiment, while 1 maps to the most positive one and the higher the score, the more positive the sentiment in the document is. Yet these values are relative to the training data, so e.g. if all data was positive then -1 will be also positive (though the least). The sentiment_score shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API.
Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via [GetOperation][google.longrunning.Operations.GetOperation] method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in the [response][google.longrunning.Operation.response] field. Available for following ML problems: * Image Classification * Image Object Detection * Text Extraction
message
prediction_service.proto:158Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
string name = 1
Name of the model requested to serve the batch prediction.
optional input_config = 3
Required. The input configuration for batch prediction.
optional output_config = 4
Required. The Configuration specifying where output predictions should be written.
map<string, string> params = 5
Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. * For Text Classification: `score_threshold` - (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Classification: `score_threshold` - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Object Detection: `score_threshold` - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` - (int64) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server.
message
annotation_payload.proto:36Contains annotation information that is relevant to AutoML.
Used in:
oneof detail
Output only . Additional information about the annotation specific to the AutoML domain.
translation = 2
Annotation details for translation.
classification = 3
Annotation details for content or image classification.
image_object_detection = 4
Annotation details for image object detection.
text_extraction = 6
Annotation details for text extraction.
text_sentiment = 7
Annotation details for text sentiment.
string annotation_spec_id = 1
Output only . The resource ID of the annotation spec that this annotation pertains to. The annotation spec comes from either an ancestor dataset, or the dataset that was used to train the model in use.
string display_name = 5
Output only. The value of [display_name][google.cloud.automl.v1p1beta.AnnotationSpec.display_name] when the model was trained. Because this field returns a value at model training time, for different models trained using the same dataset, the returned value could be different as model owner could update the `display_name` between any two model training.
message
io.proto:614Input 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: <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> 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 Text files can be no larger than 10MB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf </section><section><h5>Sentiment Analysis</h5> 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 Text files can be no larger than 128kB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf </section><section><h5>Entity Extraction</h5> 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. Only PDF documents are currently supported, and each PDF document cannot exceed 2MB in size. 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.pdf" ] } } } } </section> </div> **Input field definitions:** `GCS_FILE_PATH` : The path to a file on Google Cloud Storage. For example, "gs://folder/video.avi". **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.
Used in: ,
oneof source
The source of the input.
gcs_source = 1
Required. The Google Cloud Storage location for the input content.
message
operations.proto:137Details of BatchPredict operation.
Used in:
optional input_config = 1
Output only. The input config that was given upon starting this batch predict operation.
optional output_info = 2
Output only. Information further describing this batch predict's output.
message
operations.proto:142Further describes this batch predict's output. Supplements [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].
Used in:
oneof output_location
The output location into which prediction output is written.
string gcs_output_directory = 1
The full path of the Google Cloud Storage directory created, into which the prediction output is written.
message
io.proto:816Output 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-<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 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 (or pdf) file 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 (or pdf) file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text (or pdf) 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 (or pdf) 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 (or pdf) file 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 (or pdf) file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text (or pdf) 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 (or pdf) 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`.
Used in:
oneof destination
The destination of the output.
gcs_destination = 1
Required. The Google Cloud Storage location of the directory where the output is to be written to.
message
prediction_service.proto:199Result 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].
map<string, string> metadata = 1
Additional domain-specific prediction response metadata. * For Image Object Detection: `max_bounding_box_count` - (int64) At most that many bounding boxes per image could have been returned.
message
detection.proto:43Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Used in:
float iou_threshold = 1
Output only. The intersection-over-union threshold value used to compute this metrics entry.
float mean_average_precision = 2
Output only. The mean average precision, most often close to au_prc.
repeated confidence_metrics_entries = 3
Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them.
message
detection.proto:45Metrics for a single confidence threshold.
Used in:
float confidence_threshold = 1
Output only. The confidence threshold value used to compute the metrics.
float recall = 2
Output only. Recall under the given confidence threshold.
float precision = 3
Output only. Precision under the given confidence threshold.
float f1_score = 4
Output only. The harmonic mean of recall and precision.
message
geometry.proto:45A 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.
Used in: ,
repeated normalized_vertices = 2
Output only . The bounding polygon normalized vertices.
message
classification.proto:43Contains annotation details specific to classification.
Used in:
float score = 1
Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence that the annotation is positive. If a user approves an annotation as negative or positive, the score value remains unchanged. If a user creates an annotation, the score is 0 for negative or 1 for positive.
message
classification.proto:53Model evaluation metrics for classification problems.
Used in:
float au_prc = 1
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
float au_roc = 6
Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
float log_loss = 7
Output only. The Log Loss metric.
repeated confidence_metrics_entry = 3
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.
optional confusion_matrix = 4
Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.
repeated string annotation_spec_id = 5
Output only. The annotation spec ids used for this evaluation.
message
classification.proto:55Metrics for a single confidence threshold.
Used in:
float confidence_threshold = 1
Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
int32 position_threshold = 14
Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.
float recall = 2
Output only. Recall (True Positive Rate) for the given confidence threshold.
float precision = 3
Output only. Precision for the given confidence threshold.
float false_positive_rate = 8
Output only. False Positive Rate for the given confidence threshold.
float f1_score = 4
Output only. The harmonic mean of recall and precision.
float recall_at1 = 5
Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
float precision_at1 = 6
Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
float false_positive_rate_at1 = 9
Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
float f1_score_at1 = 7
Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
int64 true_positive_count = 10
Output only. The number of model created labels that match a ground truth label.
int64 false_positive_count = 11
Output only. The number of model created labels that do not match a ground truth label.
int64 false_negative_count = 12
Output only. The number of ground truth labels that are not matched by a model created label.
int64 true_negative_count = 13
Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.
message
classification.proto:117Confusion matrix of the model running the classification.
Used in: ,
repeated string annotation_spec_id = 1
Output only. IDs of the annotation specs used in the confusion matrix.
repeated string display_name = 3
Output only. Display name of the annotation specs used in the confusion matrix, as they were at the moment of the evaluation.
repeated row = 2
Output only. Rows in the confusion matrix. The number of rows is equal to the size of `annotation_spec_id`. `row[i].example_count[j]` is the number of examples that have ground truth of the `annotation_spec_id[i]` and are predicted as `annotation_spec_id[j]` by the model being evaluated.
message
classification.proto:119Output only. A row in the confusion matrix.
Used in:
repeated int32 example_count = 1
Output only. Value of the specific cell in the confusion matrix. The number of values each row has (i.e. the length of the row) is equal to the length of the `annotation_spec_id` field or, if that one is not populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
Type of the classification problem.
Used in: , ,
CLASSIFICATION_TYPE_UNSPECIFIED = 0
An un-set value of this enum.
MULTICLASS = 1
At most one label is allowed per example.
MULTILABEL = 2
Multiple labels are allowed for one example.
message
operations.proto:104Details of CreateDataset operation.
Used in:
(message has no fields)
message
operations.proto:109Details of CreateModel operation.
Used in:
(message has no fields)
message
dataset.proto:36A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Used as response type in: AutoMl.GetDataset, AutoMl.UpdateDataset
Used as field type in: , ,
oneof dataset_metadata
Required. The dataset metadata that is specific to the problem type.
translation_dataset_metadata = 23
Metadata for a dataset used for translation.
image_classification_dataset_metadata = 24
Metadata for a dataset used for image classification.
text_classification_dataset_metadata = 25
Metadata for a dataset used for text classification.
image_object_detection_dataset_metadata = 26
Metadata for a dataset used for image object detection.
text_extraction_dataset_metadata = 28
Metadata for a dataset used for text extraction.
text_sentiment_dataset_metadata = 30
Metadata for a dataset used for text sentiment.
string name = 1
Output only. The resource name of the dataset. Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
string display_name = 2
Required. The name of the dataset to show in the interface. The name can 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.
string description = 3
User-provided description of the dataset. The description can be up to 25000 characters long.
int32 example_count = 21
Output only. The number of examples in the dataset.
optional create_time = 14
Output only. Timestamp when this dataset was created.
string etag = 17
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
map<string, string> labels = 39
Optional. The labels with user-defined metadata to organize your dataset. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. See https://goo.gl/xmQnxf for more information on and examples of labels.
message
operations.proto:89Details of operations that perform deletes of any entities.
Used in:
(message has no fields)
message
operations.proto:94Details of DeployModel operation.
Used in:
(message has no fields)
message
data_items.proto:99A structured text document e.g. a PDF.
Used in:
optional input_config = 1
An input config specifying the content of the document.
optional document_text = 2
The plain text version of this document.
repeated layout = 3
Describes the layout of the document. Sorted by [page_number][].
optional document_dimensions = 4
The dimensions of the page in the document.
int32 page_count = 5
Number of pages in the document.
message
data_items.proto:101Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.
Used in:
optional text_segment = 1
Text Segment that represents a segment in [document_text][google.cloud.automl.v1p1beta.Document.document_text].
int32 page_number = 2
Page number of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the original document, starts from 1.
optional bounding_poly = 3
The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page. Contains exactly 4 [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices] and they are connected by edges in the order provided, which will represent a rectangle parallel to the frame. The [NormalizedVertex-s][google.cloud.automl.v1p1beta.NormalizedVertex] are relative to the page. Coordinates are based on top-left as point (0,0).
Layout.TextSegmentType text_segment_type = 4
The type of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in document.
The type of TextSegment in the context of the original document.
Used in:
TEXT_SEGMENT_TYPE_UNSPECIFIED = 0
Should not be used.
TOKEN = 1
The text segment is a token. e.g. word.
PARAGRAPH = 2
The text segment is a paragraph.
FORM_FIELD = 3
The text segment is a form field.
FORM_FIELD_NAME = 4
The text segment is the name part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.
FORM_FIELD_CONTENTS = 5
The text segment is the text content part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.
TABLE = 6
The text segment is a whole table, including headers, and all rows.
TABLE_HEADER = 7
The text segment is a table's headers. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.
TABLE_ROW = 8
The text segment is a row in table. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.
TABLE_CELL = 9
The text segment is a cell in table. It will be treated as child of another TABLE_ROW TextSegment if its span is subspan of another TextSegment with type TABLE_ROW.
message
data_items.proto:72Message that describes dimension of a document.
Used in:
DocumentDimensions.DocumentDimensionUnit unit = 1
Unit of the dimension.
float width = 2
Width value of the document, works together with the unit.
float height = 3
Height value of the document, works together with the unit.
Unit of the document dimension.
Used in:
DOCUMENT_DIMENSION_UNIT_UNSPECIFIED = 0
Should not be used.
INCH = 1
Document dimension is measured in inches.
CENTIMETER = 2
Document dimension is measured in centimeters.
POINT = 3
Document dimension is measured in points. 72 points = 1 inch.
message
io.proto:630Input configuration of a [Document][google.cloud.automl.v1.Document].
Used in:
optional gcs_source = 1
The Google Cloud Storage location of the document file. Only a single path should be given. Max supported size: 512MB. Supported extensions: .PDF.
message
data_items.proto:186Example data used for training or prediction.
Used in: ,
oneof payload
Required. Input only. The example data.
image = 1
Example image.
text_snippet = 2
Example text.
document = 4
Example document.
message
operations.proto:119Details of ExportData operation.
Used in:
optional output_info = 1
Output only. Information further describing this export data's output.
message
operations.proto:123Further describes this export data's output. Supplements [OutputConfig][google.cloud.automl.v1.OutputConfig].
Used in:
oneof output_location
The output location to which the exported data is written.
string gcs_output_directory = 1
The full path of the Google Cloud Storage directory created, into which the exported data is written.
message
operations.proto:160Details of ExportModel operation.
Used in:
optional output_info = 2
Output only. Information further describing the output of this model export.
message
operations.proto:164Further describes the output of model export. Supplements [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
Used in:
string gcs_output_directory = 1
The full path of the Google Cloud Storage directory created, into which the model will be exported.
message
io.proto:884The Google Cloud Storage location where the output is to be written to.
Used in: , ,
string output_uri_prefix = 1
Required. Google Cloud Storage URI to output directory, up to 2000 characters long. Accepted forms: * Prefix path: gs://bucket/directory The requesting user must have write permission to the bucket. The directory is created if it doesn't exist.
message
io.proto:876The Google Cloud Storage location for the input content.
Used in: , , ,
repeated string input_uris = 1
Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms: * Full object path, e.g. gs://bucket/directory/object.csv
message
data_items.proto:37A representation of an image. Only images up to 30MB in size are supported.
Used in:
oneof data
Input only. The data representing the image. For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set, as other options are not currently supported by prediction API. You can read the contents of an uploaded image by using the [content_uri][google.cloud.automl.v1.Image.content_uri] field.
bytes image_bytes = 1
Image content represented as a stream of bytes. Note: As with all `bytes` fields, protobuffers use a pure binary representation, whereas JSON representations use base64.
image_input_config = 7
An input config specifying the content of the image.
string thumbnail_uri = 4
Output only. HTTP URI to the thumbnail image.
message
image.proto:35Dataset metadata that is specific to image classification.
Used in:
ClassificationType classification_type = 1
Required. Type of the classification problem.
message
image.proto:175Model deployment metadata specific to Image Classification.
Used in:
int64 node_count = 1
Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps]. Must be between 1 and 100, inclusive on both ends.
message
image.proto:44Model metadata for image classification.
Used in:
string base_model_id = 1
Optional. The ID of the `base` model. If it is specified, the new model will be created based on the `base` model. Otherwise, the new model will be created from scratch. The `base` model must be in the same `project` and `location` as the new model to create, and have the same `model_type`.
int64 train_budget_milli_node_hours = 16
The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual `train_cost` will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be `MODEL_CONVERGED`. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type `cloud`(default), the train budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192, 000 which represents one day in wall time. For model type `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.
int64 train_cost_milli_node_hours = 17
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.
string stop_reason = 5
Output only. The reason that this create model operation stopped, e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string model_type = 7
Optional. Type of the model. The available values are: * `cloud` - Model to be used via prediction calls to AutoML API. This is the default value. * `mobile-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. * `mobile-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. * `mobile-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. * `mobile-core-ml-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models. * `mobile-core-ml-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. * `mobile-core-ml-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
double node_qps = 13
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
int64 node_count = 14
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.
message
io.proto:623Input configuration of an [Document][google.cloud.automl.v1.Image].
Used in:
optional gcs_source = 1
The Google Cloud Storage location of the document file. Only a single path should be given.
message
detection.proto:32Annotation details for image object detection.
Used in:
optional bounding_box = 1
Output only. The rectangle representing the object location.
float score = 2
Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence.
message
image.proto:41Dataset metadata specific to image object detection.
Used in:
(message has no fields)
message
detection.proto:74Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Used in:
int32 evaluated_bounding_box_count = 1
Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had.
repeated bounding_box_metrics_entries = 2
Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.
float bounding_box_mean_average_precision = 3
Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.
message
image.proto:186Model deployment metadata specific to Image Object Detection.
Used in:
int64 node_count = 1
Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends.
message
image.proto:127Model metadata specific to image object detection.
Used in:
string model_type = 1
Optional. Type of the model. The available values are: * `cloud-high-accuracy-1` - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models. * `cloud-low-latency-1` - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
int64 node_count = 3
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.
double node_qps = 4
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
string stop_reason = 5
Output only. The reason that this create model operation stopped, e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
int64 train_budget_milli_node_hours = 6
The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual `train_cost` will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be `MODEL_CONVERGED`. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.
int64 train_cost_milli_node_hours = 7
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.
message
operations.proto:114Details of ImportData operation.
Used in:
(message has no fields)
message
io.proto:472Input 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: <h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> 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 </section><section><h5>Object Detection</h5> 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,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> 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 PDF document. Multiple JSON documents can be separated using line breaks (\n). For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } } **In-line JSONL files with PDF layout information** **Note:** You can only annotate PDF files using the UI. The format described below applies to annotated PDF files exported using the UI or `exportData`. In-line .JSONL files for PDF documents contain, per line, a JSON document that wraps a `document` field that provides the textual content of the PDF 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} } }, ], </section><section><h5>Classification</h5> 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, .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 </section><section><h5>Sentiment Analysis</h5> 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, .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 </section> </div> **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. `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. `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.
Used in:
oneof source
The source of the input.
gcs_source = 1
The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
map<string, string> params = 2
Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long.
message
model.proto:35API proto representing a trained machine learning model.
Used as response type in: AutoMl.GetModel, AutoMl.UpdateModel
Used as field type in: , ,
oneof model_metadata
Required. The model metadata that is specific to the problem type. Must match the metadata type of the dataset used to train the model.
translation_model_metadata = 15
Metadata for translation models.
image_classification_model_metadata = 13
Metadata for image classification models.
text_classification_model_metadata = 14
Metadata for text classification models.
image_object_detection_model_metadata = 20
Metadata for image object detection models.
text_extraction_model_metadata = 19
Metadata for text extraction models.
text_sentiment_model_metadata = 22
Metadata for text sentiment models.
string name = 1
Output only. Resource name of the model. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
string display_name = 2
Required. The name of the model to show in the interface. The name can 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. It must start with a letter.
string dataset_id = 3
Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.
optional create_time = 7
Output only. Timestamp when the model training finished and can be used for prediction.
optional update_time = 11
Output only. Timestamp when this model was last updated.
Model.DeploymentState deployment_state = 8
Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.
string etag = 10
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
map<string, string> labels = 34
Optional. The labels with user-defined metadata to organize your model. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. See https://goo.gl/xmQnxf for more information on and examples of labels.
Deployment state of the model.
Used in:
DEPLOYMENT_STATE_UNSPECIFIED = 0
Should not be used, an un-set enum has this value by default.
DEPLOYED = 1
Model is deployed.
UNDEPLOYED = 2
Model is not deployed.
message
model_evaluation.proto:37Evaluation results of a model.
Used as response type in: AutoMl.GetModelEvaluation
Used as field type in:
oneof metrics
Output only. Problem type specific evaluation metrics.
classification_evaluation_metrics = 8
Model evaluation metrics for image, text classification.
translation_evaluation_metrics = 9
Model evaluation metrics for translation.
image_object_detection_evaluation_metrics = 12
Model evaluation metrics for image object detection.
text_sentiment_evaluation_metrics = 11
Evaluation metrics for text sentiment models.
text_extraction_evaluation_metrics = 13
Evaluation metrics for text extraction models.
string name = 1
Output only. Resource name of the model evaluation. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
string annotation_spec_id = 2
Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation.
string display_name = 15
Output only. The value of [display_name][google.cloud.automl.v1.AnnotationSpec.display_name] at the moment when the model was trained. Because this field returns a value at model training time, for different models trained from the same dataset, the values may differ, since display names could had been changed between the two model's trainings.
optional create_time = 5
Output only. Timestamp when this model evaluation was created.
int32 evaluated_example_count = 6
Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model. For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id].
message
io.proto:826Output configuration for ModelExport Action.
Used in:
oneof destination
The destination of the output.
gcs_destination = 1
Required. The Google Cloud Storage location where the model is to be written to. This location may only be set for the following model formats: "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml". Under the directory given as the destination a new one with name "model-export-<model-display-name>-<timestamp-of-export-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside the model and any of its supporting files will be written.
string model_format = 4
The format in which the model must be exported. The available, and default, formats depend on the problem and model type (if given problem and type combination doesn't have a format listed, it means its models are not exportable): * For Image Classification mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js". * For Image Classification mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1: "core_ml" (default). * For Image Object Detection mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: "tflite", "tf_saved_model", "tf_js". Formats description: * tflite - Used for Android mobile devices. * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/) devices. * tf_saved_model - A tensorflow model in SavedModel format. * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can be used in the browser and in Node.js using JavaScript.x` * core_ml - Used for iOS mobile devices.
map<string, string> params = 2
Additional model-type and format specific parameters describing the requirements for the to be exported model files, any string must be up to 25000 characters long.
message
geometry.proto:34A 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.
Used in:
float x = 1
Required. Horizontal coordinate.
float y = 2
Required. Vertical coordinate.
message
operations.proto:39Metadata used across all long running operations returned by AutoML API.
oneof details
Ouptut only. Details of specific operation. Even if this field is empty, the presence allows to distinguish different types of operations.
delete_details = 8
Details of a Delete operation.
deploy_model_details = 24
Details of a DeployModel operation.
undeploy_model_details = 25
Details of an UndeployModel operation.
create_model_details = 10
Details of CreateModel operation.
create_dataset_details = 30
Details of CreateDataset operation.
import_data_details = 15
Details of ImportData operation.
batch_predict_details = 16
Details of BatchPredict operation.
export_data_details = 21
Details of ExportData operation.
export_model_details = 22
Details of ExportModel operation.
int32 progress_percent = 13
Output only. Progress of operation. Range: [0, 100]. Not used currently.
repeated partial_failures = 2
Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details.
optional create_time = 3
Output only. Time when the operation was created.
optional update_time = 4
Output only. Time when the operation was updated for the last time.
message
io.proto:710Output configuration for ExportData. As destination the [gcs_destination][google.cloud.automl.v1.OutputConfig.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 "export_data-<dataset-display-name>-<timestamp-of-export-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Only ground truth annotations are exported (not approved annotations are not exported). The outputs correspond to how the data was imported, and may be used as input to import data. The output formats are represented as EBNF with literal commas and same non-terminal symbols definitions are these in import data's [InputConfig][google.cloud.automl.v1.InputConfig]: * For Image Classification: CSV file(s) `image_classification_1.csv`, `image_classification_2.csv`,...,`image_classification_N.csv`with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... where GCS_FILE_PATHs point at the original, source locations of the imported images. For MULTICLASS classification type, there can be at most one LABEL per example. * For Image Object Detection: CSV file(s) `image_object_detection_1.csv`, `image_object_detection_2.csv`,...,`image_object_detection_N.csv` with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,) where GCS_FILE_PATHs point at the original, source locations of the imported images. * For Text Classification: In the created directory CSV file(s) `text_classification_1.csv`, `text_classification_2.csv`, ...,`text_classification_N.csv` will be created where N depends on the total number of examples exported. Each line in the CSV is of the format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... where GCS_FILE_PATHs point at the exported .txt files containing the text content of the imported example. For MULTICLASS classification type, there will be at most one LABEL per example. * For Text Sentiment: In the created directory CSV file(s) `text_sentiment_1.csv`, `text_sentiment_2.csv`, ...,`text_sentiment_N.csv` will be created where N depends on the total number of examples exported. Each line in the CSV is of the format: ML_USE,GCS_FILE_PATH,SENTIMENT where GCS_FILE_PATHs point at the exported .txt files containing the text content of the imported example. * For Text Extraction: CSV file `text_extraction.csv`, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (i.e. JSON Lines) file which contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by AnnotationPayload protos (called annotations). If initially documents had been imported, the JSONL will point at the original, source locations of the imported documents. * 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)
Used in:
oneof destination
The destination of the output.
gcs_destination = 1
Required. The Google Cloud Storage location where the output is to be written to. For Image Object Detection, Text Extraction in the given directory a new directory will be created with name: export_data-<dataset-display-name>-<timestamp-of-export-call> where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory.
message
text.proto:32Dataset metadata for classification.
Used in:
ClassificationType classification_type = 1
Required. Type of the classification problem.
message
text.proto:38Model metadata that is specific to text classification.
Used in:
ClassificationType classification_type = 3
Output only. Classification type of the dataset used to train this model.
message
text_extraction.proto:31Annotation for identifying spans of text.
Used in:
oneof annotation
Required. Text extraction annotations can either be a text segment or a text relation.
text_segment = 3
An entity annotation will set this, which is the part of the original text to which the annotation pertains.
float score = 1
Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence in correctness of the annotation.
message
text.proto:44Dataset metadata that is specific to text extraction
Used in:
(message has no fields)
message
text_extraction.proto:46Model evaluation metrics for text extraction problems.
Used in:
float au_prc = 1
Output only. The Area under precision recall curve metric.
repeated confidence_metrics_entries = 2
Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.
message
text_extraction.proto:48Metrics for a single confidence threshold.
Used in:
float confidence_threshold = 1
Output only. The confidence threshold value used to compute the metrics. Only annotations with score of at least this threshold are considered to be ones the model would return.
float recall = 3
Output only. Recall under the given confidence threshold.
float precision = 4
Output only. Precision under the given confidence threshold.
float f1_score = 5
Output only. The harmonic mean of recall and precision.
message
text.proto:47Model metadata that is specific to text extraction.
Used in:
(message has no fields)
message
text_segment.proto:31A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Used in: ,
string content = 3
Output only. The content of the TextSegment.
int64 start_offset = 1
Required. Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).
int64 end_offset = 2
Required. Zero-based character index of the first character past the end of the text segment (counting character from the beginning of the text). The character at the end_offset is NOT included in the text segment.
message
text_sentiment.proto:32Contains annotation details specific to text sentiment.
Used in:
int32 sentiment = 1
Output only. The sentiment with the semantic, as given to the [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] when populating the dataset from which the model used for the prediction had been trained. The sentiment values are between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive), with higher value meaning more positive sentiment. They are completely relative, i.e. 0 means least positive sentiment and sentiment_max means the most positive from the sentiments present in the train data. Therefore e.g. if train data had only negative sentiment, then sentiment_max, would be still negative (although least negative). The sentiment shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API.
message
text.proto:50Dataset metadata for text sentiment.
Used in:
int32 sentiment_max = 1
Required. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentiment_max (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. sentiment_max value must be between 1 and 10 (inclusive).
message
text_sentiment.proto:49Model evaluation metrics for text sentiment problems.
Used in:
float precision = 1
Output only. Precision.
float recall = 2
Output only. Recall.
float f1_score = 3
Output only. The harmonic mean of recall and precision.
float mean_absolute_error = 4
Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
float mean_squared_error = 5
Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
float linear_kappa = 6
Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
float quadratic_kappa = 7
Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
optional confusion_matrix = 8
Output only. Confusion matrix of the evaluation. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
message
text.proto:60Model metadata that is specific to text sentiment.
Used in:
(message has no fields)
message
data_items.proto:57A representation of a text snippet.
Used in: , ,
string content = 1
Required. The content of the text snippet as a string. Up to 250000 characters long.
string mime_type = 2
Optional. The format of [content][google.cloud.automl.v1.TextSnippet.content]. Currently the only two allowed values are "text/html" and "text/plain". If left blank, the format is automatically determined from the type of the uploaded [content][google.cloud.automl.v1.TextSnippet.content].
string content_uri = 4
Output only. HTTP URI where you can download the content.
message
translation.proto:68Annotation details specific to translation.
Used in:
optional translated_content = 1
Output only . The translated content.
message
translation.proto:33Dataset metadata that is specific to translation.
Used in:
string source_language_code = 1
Required. The BCP-47 language code of the source language.
string target_language_code = 2
Required. The BCP-47 language code of the target language.
message
translation.proto:42Evaluation metrics for the dataset.
Used in:
double bleu_score = 1
Output only. BLEU score.
double base_bleu_score = 2
Output only. BLEU score for base model.
message
translation.proto:51Model metadata that is specific to translation.
Used in:
string base_model = 1
The resource name of the model to use as a baseline to train the custom model. If unset, we use the default base model provided by Google Translate. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
string source_language_code = 2
Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.
string target_language_code = 3
Output only. The target languge (The BCP-47 language code) that is used for training.
message
operations.proto:99Details of UndeployModel operation.
Used in:
(message has no fields)