nlp_assertion_logreg: Spark NLP AssertionLogRegApproach

In r-spark/sparknlp: R Interface to John Snow Labs Spark NLP

Spark NLP AssertionLogRegApproach

Description

Spark ML estimator that See https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#assertionlogreg

Usage

nlp_assertion_logreg(
  x,
  input_cols,
  output_col,
  label_column = NULL,
  max_iter = NULL,
  reg = NULL,
  enet = NULL,
  before = NULL,
  after = NULL,
  start_col = NULL,
  end_col = NULL,
  lazy_annotator = NULL,
  uid = random_string("assertion_logreg_")
)

Arguments

x	A spark_connection, ml_pipeline, or a tbl_spark.
input_cols	Input columns. String array.
output_col	Output column. String.
label_column	Column with one label per document
max_iter	Max number of iterations for algorithm
reg	Regularization parameter
enet	Elastic net parameter
before	Amount of tokens from the context before the target
after	Amount of tokens from the context after the target
start_col	Column that contains the token number for the start of the target
end_col	Column that contains the token number for the end of the target
lazy_annotator	a Param in Annotators that allows them to stand idle in the Pipeline and do nothing. Can be called by other Annotators in a RecursivePipeline
uid	A character string used to uniquely identify the ML estimator.

Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Estimator object and can be used to compose Pipeline objects.

• ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the NLP estimator appended to the pipeline.

• tbl_spark: When x is a tbl_spark, an estimator is constructed then immediately fit with the input tbl_spark, returning an NLP model.

r-spark/sparknlp documentation built on Oct. 15, 2022, 10:50 a.m.