// Copyright 2020 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

syntax = "proto3";

package google.cloud.automl.v1beta1;

import "google/cloud/automl/v1beta1/classification.proto";
import "google/api/annotations.proto";

option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
option java_multiple_files = true;
option java_outer_classname = "TextProto";
option java_package = "com.google.cloud.automl.v1beta1";
option php_namespace = "Google\\Cloud\\AutoMl\\V1beta1";
option ruby_package = "Google::Cloud::AutoML::V1beta1";

// Dataset metadata for classification.
message TextClassificationDatasetMetadata {
  // Required. Type of the classification problem.
  ClassificationType classification_type = 1;
}

// Model metadata that is specific to text classification.
message TextClassificationModelMetadata {
  // Output only. Classification type of the dataset used to train this model.
  ClassificationType classification_type = 3;
}

// Dataset metadata that is specific to text extraction
message TextExtractionDatasetMetadata {

}

// Model metadata that is specific to text extraction.
message TextExtractionModelMetadata {
  // Indicates the scope of model use case.
  //
  // * `default`: Use to train a general text extraction model. Default value.
  //
  // * `health_care`: Use to train a text extraction model that is tuned for
  //   healthcare applications.
  string model_hint = 3;
}

// Dataset metadata for text sentiment.
message TextSentimentDatasetMetadata {
  // 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).
  int32 sentiment_max = 1;
}

// Model metadata that is specific to text sentiment.
message TextSentimentModelMetadata {

}