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84 lines
3.4 KiB
84 lines
3.4 KiB
// Copyright 2018 Google LLC.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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syntax = "proto3";
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package google.cloud.automl.v1beta1;
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import "google/api/annotations.proto";
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import "google/cloud/automl/v1beta1/classification.proto";
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option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
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option java_outer_classname = "TextSentimentProto";
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option java_package = "com.google.cloud.automl.v1beta1";
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option php_namespace = "Google\\Cloud\\AutoMl\\V1beta1";
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// Contains annotation details specific to text sentiment.
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message TextSentimentAnnotation {
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// Output only. The sentiment with the semantic, as given to the
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// [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData] when populating the dataset from which the model used
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// for the prediction had been trained.
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// The sentiment values are between 0 and
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// Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
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// with higher value meaning more positive sentiment. They are completely
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// relative, i.e. 0 means least positive sentiment and sentiment_max means
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// the most positive from the sentiments present in the train data. Therefore
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// e.g. if train data had only negative sentiment, then sentiment_max, would
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// be still negative (although least negative).
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// The sentiment shouldn't be confused with "score" or "magnitude"
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// from the previous Natural Language Sentiment Analysis API.
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int32 sentiment = 1;
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}
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// Model evaluation metrics for text sentiment problems.
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message TextSentimentEvaluationMetrics {
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// Output only. Precision.
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float precision = 1;
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// Output only. Recall.
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float recall = 2;
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// Output only. The harmonic mean of recall and precision.
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float f1_score = 3;
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// Output only. Mean absolute error. Only set for the overall model
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// evaluation, not for evaluation of a single annotation spec.
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float mean_absolute_error = 4;
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// Output only. Mean squared error. Only set for the overall model
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// evaluation, not for evaluation of a single annotation spec.
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float mean_squared_error = 5;
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// Output only. Linear weighted kappa. Only set for the overall model
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// evaluation, not for evaluation of a single annotation spec.
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float linear_kappa = 6;
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// Output only. Quadratic weighted kappa. Only set for the overall model
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// evaluation, not for evaluation of a single annotation spec.
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float quadratic_kappa = 7;
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// Output only. Confusion matrix of the evaluation.
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// Only set for the overall model evaluation, not for evaluation of a single
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// annotation spec.
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ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 8;
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// Output only. The annotation spec ids used for this evaluation.
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// Deprecated, remove after Boq Migration and use then
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// TextSentimentModelMetadata.annotation_spec_count for count, and list
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// all model evaluations to see the exact annotation_spec_ids that were
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// used.
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repeated string annotation_spec_id = 9;
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}
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