v1

Should not be used, an un-set enum has this value by default.

Model is deployed.

Model is not deployed.

Annotation details for translation.

This object should have the same structure as TranslationAnnotation

The resource name of the project to create the dataset for.

The dataset to create.

This object should have the same structure as Dataset

Resource name of the parent project where the model is being created.

The model to create.

This object should have the same structure as Model

Metadata for a dataset used for translation.

This object should have the same structure as TranslationDatasetMetadata

Output only. The resource name of the dataset. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}

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.

User-provided description of the dataset. The description can be up to 25000 characters long.

Output only. The number of examples in the dataset.

Output only. Timestamp when this dataset was created.

This object should have the same structure as Timestamp

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

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.

The resource name of the dataset to delete.

Resource name of the model being deleted.

Example text.

This object should have the same structure as TextSnippet

Required. The resource name of the dataset.

Required. The desired output location.

This object should have the same structure as OutputConfig

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.

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

The resource name of the dataset to retrieve.

Resource name for the model evaluation.

Resource name of the model.

Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.

Required. The desired input location and its domain specific semantics, if any.

This object should have the same structure as InputConfig

The Google Cloud Storage location for the input content. In ImportData, the gcs_source points to a csv with structure described in the comment.

This object should have the same structure as GcsSource

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long.

The resource name of the project from which to list datasets.

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.

Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.

A token identifying a page of results for the server to return Typically obtained via ListDatasetsResponse.next_page_token of the previous AutoMl.ListDatasets call.

The datasets read.

This object should have the same structure as Dataset

A token to retrieve next page of results. Pass to ListDatasetsRequest.page_token to obtain that page.

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.

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.

Requested page size.

A token identifying a page of results for the server to return. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous AutoMl.ListModelEvaluations call.

List of model evaluations in the requested page.

This object should have the same structure as ModelEvaluation

A token to retrieve next page of results. Pass to the ListModelEvaluationsRequest.page_token field of a new AutoMl.ListModelEvaluations request to obtain that page.

Resource name of the project, from which to list the models.

An expression for filtering the results of the request.

* `model_metadata` - for existence of the case (e.g.
          video_classification_model_metadata:*).
* `dataset_id` - for = or !=. Some examples of using the filter are:

* `image_classification_model_metadata:*` --> The model has
                                     image_classification_model_metadata.
* `dataset_id=5` --> The model was created from a dataset with ID 5.

Requested page size.

A token identifying a page of results for the server to return Typically obtained via ListModelsResponse.next_page_token of the previous AutoMl.ListModels call.

List of models in the requested page.

This object should have the same structure as Model

A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page.

Metadata for translation models.

This object should have the same structure as TranslationModelMetadata

Output only. Resource name of the model. Format: projects/{project_id}/locations/{location_id}/models/{model_id}

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.

Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.

Output only. Timestamp when the model training finished and can be used for prediction.

This object should have the same structure as Timestamp

Output only. Timestamp when this model was last updated.

This object should have the same structure as Timestamp

Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.

The number should be among the values of DeploymentState

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.

Model evaluation metrics for translation.

This object should have the same structure as TranslationEvaluationMetrics

Output only. Resource name of the model evaluation. Format:

projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}

Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation.

Output only. Timestamp when this model evaluation was created.

This object should have the same structure as Timestamp

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.

The Google Cloud Storage location where the output is to be written to. For Image Object Detection, Text Extraction, Video Classification and Tables, in the given directory a new directory will be created with name: export_data-- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory.

This object should have the same structure as GcsDestination

Name of the model requested to serve the prediction.

Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.

This object should have the same structure as ExamplePayload

Additional domain-specific parameters, any string must be up to 25000 characters long.

Prediction result. Translation and Text Sentiment will return precisely one payload.

This object should have the same structure as AnnotationPayload

Additional domain-specific prediction response metadata.

Required. The content of the text snippet as a string. Up to 250000 characters long.

Optional. The format of 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.

Output only. HTTP URI where you can download the content.

Output only . The translated content.

This object should have the same structure as TextSnippet

Required. The BCP-47 language code of the source language.

Required. The BCP-47 language code of the target language.

Output only. BLEU score.

Output only. BLEU score for base model.

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}

Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.

Output only. The target languge (The BCP-47 language code) that is used for training.

The dataset which replaces the resource on the server.

This object should have the same structure as Dataset

Required. The update mask applies to the resource.

This object should have the same structure as FieldMask

The model which replaces the resource on the server.

This object should have the same structure as Model

Required. The update mask applies to the resource.

This object should have the same structure as FieldMask