com.johnsnowlabs.nlp.annotators.assertion.dl
whether to include confidence scores in annotation metadata
Whether to output to annotators log folder
Whether to include confidence scores in annotation metadata
val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset.
val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset. If set used to calculate statistic on it during training.")
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 AssertionDLModel
a unique identifier for the instanced AssertionDLApproach
a unique identifier for the instanced AssertionDLApproach
Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
Contains all the methods for training the DL Approach, together with trainWithChunk, trainWithStartEnd