Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::PredictResponse.
Response message for PredictionService.Predict.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#metadata
def metadata() -> ::Google::Protobuf::Map{::String => ::String}-
(::Google::Protobuf::Map{::String => ::String}) —
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.
#metadata=
def metadata=(value) -> ::Google::Protobuf::Map{::String => ::String}-
value (::Google::Protobuf::Map{::String => ::String}) —
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.
-
(::Google::Protobuf::Map{::String => ::String}) —
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.
#payload
def payload() -> ::Array<::Google::Cloud::AutoML::V1beta1::AnnotationPayload>- (::Array<::Google::Cloud::AutoML::V1beta1::AnnotationPayload>) — Prediction result. Translation and Text Sentiment will return precisely one payload.
#payload=
def payload=(value) -> ::Array<::Google::Cloud::AutoML::V1beta1::AnnotationPayload>- value (::Array<::Google::Cloud::AutoML::V1beta1::AnnotationPayload>) — Prediction result. Translation and Text Sentiment will return precisely one payload.
- (::Array<::Google::Cloud::AutoML::V1beta1::AnnotationPayload>) — Prediction result. Translation and Text Sentiment will return precisely one payload.
#preprocessed_input
def preprocessed_input() -> ::Google::Cloud::AutoML::V1beta1::ExamplePayload-
(::Google::Cloud::AutoML::V1beta1::ExamplePayload) —
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.
#preprocessed_input=
def preprocessed_input=(value) -> ::Google::Cloud::AutoML::V1beta1::ExamplePayload-
value (::Google::Cloud::AutoML::V1beta1::ExamplePayload) —
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::V1beta1::ExamplePayload) —
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.