com.johnsnowlabs.nlp.annotators.assertion.logreg
Amount of tokens from the context after the target
Amount of tokens from the context before the target
Elastic net parameter
Column that contains the token number for the end of the target
Column with one label per document
Max number of iterations for algorithm
Regularization parameter
Column that contains the token number for the start of the target
This is a main point of interest of this class.
This is a main point of interest of this class. It trains the dataset with recursive pipeline and uses methods trainWithChunk() and trainwithStartEnd() The choice of training happens based on the startCol value of the DL Approach
a collection of inputs to train
an instance of PipelineModel
an instance of trained AssertionLogRegModel
a unique identifier for the instanced AssertionDLApproach
a unique identifier for the instanced AssertionDLApproach
This is a classification method, which uses LogisticRegression algorithm Contains all the methods for training the LogisticRegression Approach, together with trainWithChunk, trainWithStartEnd