# Constants
Apply machine learning models to reveal the structure and meaning of text.
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# Structs
AnalyzeEntitiesRequest: The entity analysis request message.
AnalyzeEntitiesResponse: The entity analysis response message.
AnalyzeEntitySentimentRequest: The entity-level sentiment analysis request message.
AnalyzeEntitySentimentResponse: The entity-level sentiment analysis response message.
AnalyzeSentimentRequest: The sentiment analysis request message.
AnalyzeSentimentResponse: The sentiment analysis response message.
AnalyzeSyntaxRequest: The syntax analysis request message.
AnalyzeSyntaxResponse: The syntax analysis response message.
AnnotateTextRequest: The request message for the text annotation API, which can perform multiple analysis types (sentiment, entities, and syntax) in one call.
AnnotateTextRequestFeatures: All available features for sentiment, syntax, and semantic analysis.
AnnotateTextResponse: The text annotations response message.
ClassificationCategory: Represents a category returned from the text classifier.
ClassificationModelOptions: Model options available for classification requests.
ClassificationModelOptionsV1Model: Options for the V1 model.
ClassificationModelOptionsV2Model: Options for the V2 model.
ClassifyTextRequest: The document classification request message.
ClassifyTextResponse: The document classification response message.
Color: Represents a color in the RGBA color space.
CpuMetric: Metric for billing reports.
DependencyEdge: Represents dependency parse tree information for a token.
Document: Represents the input to API methods.
Entity: Represents a phrase in the text that is a known entity, such as a person, an organization, or location.
EntityMention: Represents a mention for an entity in the text.
InfraUsage: Infra Usage of billing metrics.
ModerateTextRequest: The document moderation request message.
ModerateTextResponse: The document moderation response message.
PartOfSpeech: Represents part of speech information for a token.
Sentence: Represents a sentence in the input document.
Sentiment: Represents the feeling associated with the entire text or entities in the text.
Status: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs.
TextSpan: Represents a text span in the input document.
Token: Represents the smallest syntactic building block of the text.
XPSArrayStats: The data statistics of a series of ARRAY values.
XPSBoundingBoxMetricsEntry: Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
XPSBoundingBoxMetricsEntryConfidenceMetricsEntry: Metrics for a single confidence threshold.
XPSCategoryStats: The data statistics of a series of CATEGORY values.
XPSCategoryStatsSingleCategoryStats: The statistics of a single CATEGORY value.
XPSClassificationEvaluationMetrics: Model evaluation metrics for classification problems.
XPSColorMap: Map from color to display name.
XPSColorMapIntColor: RGB color and each channel is represented by an integer.
XPSColumnSpecCorrelatedColumn: Identifies a table's column, and its correlation with the column this ColumnSpec describes.
XPSCommonStats: Common statistics for a column with a specified data type.
XPSConfidenceMetricsEntry: ConfidenceMetricsEntry includes generic precision, recall, f1 score etc.
XPSConfusionMatrix: Confusion matrix of the model running the classification.
XPSConfusionMatrixRow: A row in the confusion matrix.
XPSCoreMlFormat: A model format used for iOS mobile devices.
XPSCorrelationStats: A correlation statistics between two series of DataType values.
XPSDataErrors: Different types of errors and the stats associatesd with each error.
XPSDataStats: The data statistics of a series of values that share the same DataType.
XPSDataType: Indicated the type of data that can be stored in a structured data entity (e.g.
XPSDockerFormat: A model format used for Docker containers.
XPSEdgeTpuTfLiteFormat: A model format used for Edge TPU (https://cloud.google.com/edge-tpu/) devices.
XPSEvaluationMetrics: Contains xPS-specific model evaluation metrics either for a single annotation spec (label), or for the model overall.
XPSEvaluationMetricsSet: Specifies location of model evaluation metrics.
XPSExampleSet: Set of examples or input sources.
XPSFileSpec: Spec of input and output files, on external file systems (for example, Colossus Namespace System or Google Cloud Storage).
XPSFloat64Stats: The data statistics of a series of FLOAT64 values.
XPSFloat64StatsHistogramBucket: A bucket of a histogram.
XPSImageExportModelSpec: Information of downloadable models that are pre-generated as part of training flow and will be persisted in AutoMl backend.
XPSImageModelArtifactSpec: Stores the locations and related metadata of the model artifacts.
XPSImageModelServingSpec: Serving specification for image models.
XPSImageObjectDetectionEvaluationMetrics: Model evaluation metrics for image object detection problems.
XPSImageSegmentationEvaluationMetrics: Model evaluation metrics for image segmentation problems.
XPSImageSegmentationEvaluationMetricsConfidenceMetricsEntry: Metrics for a single confidence threshold.
XPSIntegratedGradientsAttribution: An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure.
XPSModelArtifactItem: A single model artifact item.
XPSRegressionEvaluationMetrics: Model evaluation metrics for regression problems.
XPSRegressionMetricsEntry: A pair of actual & observed values for the model being evaluated.
XPSResponseExplanationMetadataInputMetadata: Metadata of the input of a feature.
XPSResponseExplanationMetadataOutputMetadata: Metadata of the prediction output to be explained.
XPSResponseExplanationSpec: Specification of Model explanation.
XPSStringStats: The data statistics of a series of STRING values.
XPSStringStatsUnigramStats: The statistics of a unigram.
XPSStructStats: The data statistics of a series of STRUCT values.
XPSStructType: `StructType` defines the DataType-s of a STRUCT type.
XPSTablesClassificationMetrics: Metrics for Tables classification problems.
XPSTablesClassificationMetricsCurveMetrics: Metrics curve data point for a single value.
XPSTablesConfidenceMetricsEntry: Metrics for a single confidence threshold.
XPSTablesDatasetMetadata: Metadata for a dataset used for AutoML Tables.
XPSTablesModelColumnInfo: An information specific to given column and Tables Model, in context of the Model and the predictions created by it.
XPSTablesModelStructure: A description of Tables model structure.
XPSTablesModelStructureModelParameters: Model hyper-parameters for a model.
XPSTablesRegressionMetrics: Metrics for Tables regression problems.
XPSTextComponentModel: Component model.
XPSTextSentimentEvaluationMetrics: Model evaluation metrics for text sentiment problems.
XPSTextToSpeechTrainResponse: TextToSpeech train response.
XPSTfJsFormat: A TensorFlow.js (https://www.tensorflow.org/js) model that can be used in the browser and in Node.js using JavaScript.
XPSTfLiteFormat: LINT.IfChange A model format used for mobile and IoT devices.
XPSTfSavedModelFormat: A tensorflow model format in SavedModel format.
XPSTimestampStats: The data statistics of a series of TIMESTAMP values.
XPSTimestampStatsGranularStats: Stats split by a defined in context granularity.
XPSTrackMetricsEntry: Track matching model metrics for a single track match threshold and multiple label match confidence thresholds.
XPSTrackMetricsEntryConfidenceMetricsEntry: Metrics for a single confidence threshold.
XPSTranslationEvaluationMetrics: Evaluation metrics for the dataset.
XPSTranslationPreprocessResponse: Translation preprocess response.
XPSTranslationTrainResponse: Train response for translation.
XPSTuningTrial: Metrics for a tuning job generated, will get forwarded to Stackdriver as model tuning logs.
XPSVideoActionMetricsEntry: The Evaluation metrics entry given a specific precision_window_length.
XPSVideoActionMetricsEntryConfidenceMetricsEntry: Metrics for a single confidence threshold.
XPSVideoActionRecognitionEvaluationMetrics: Model evaluation metrics for video action recognition.
XPSVideoExportModelSpec: Information of downloadable models that are pre-generated as part of training flow and will be persisted in AutoMl backend.
XPSVideoObjectTrackingEvaluationMetrics: Model evaluation metrics for ObjectTracking problems.
XPSVisionErrorAnalysisConfig: The vision model error analysis configuration.
XPSVisualization: Visualization configurations for image explanation.
XPSXraiAttribution: An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure.