com.johnsnowlabs.nlp.annotators.re
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every input annotation. Not necessary one to one relationship
Feature scaling method.
Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)
Get all categories
Get all categories
Get feature scaling method
Get feature scaling method
Get prediction threshold
Get prediction threshold
Get relation pairs
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : CATEGORY
Output annotator type : CATEGORY
List of pairs of named entities ("ENTITY1-ENTITY2", e.g.
List of pairs of named entities ("ENTITY1-ENTITY2", e.g. "Biomarker-RelativeDay"), which will be processed
Set the feature scaling method.
Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)
Set prediction threshold
Set prediction threshold
Set relation pairs
Required input and expected output annotator types
ClassifierDL is a generic Multi-class Text Classification. ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 50 classes
NOTE: This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.
NOTE: UniversalSentenceEncoder and SentenceEmbeddings can be used for the inputCol
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLTestSpec.scala for further reference on how to use this API