class_docstr: Converts annotation results into a format that easier to use.
  
      It is useful to extract the results from Spark NLP Pipelines. The Finisher
      outputs annotation(s) values into ``String``.
  
      For more extended examples on document pre-processing see the
      `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/model-downloader/Create%20custom%20pipeline%20-%20NerDL.ipynb
  >`__.
  
      ====================== ======================
      Input Annotation types Output Annotation type
      ====================== ======================
      ``ANY``                ``NONE``
      ====================== ======================
  
      Parameters
      ----------
      inputCols
          Input annotations
      outputCols
          Output finished annotation cols
      valueSplitSymbol
          Character separating values, by default #
      annotationSplitSymbol
          Character separating annotations, by default @
      cleanAnnotations
          Whether to remove annotation columns, by default True
      includeMetadata
          Whether to include annotation metadata, by default False
      outputAsArray
          Finisher generates an Array with the results instead of string, by
          default True
      parseEmbeddingsVectors
          Whether to include embeddings vectors in the process, by default False
  
      Examples
      --------
      >>> import sparknlp
      >>> from sparknlp.base import *
      >>> from sparknlp.annotator import *
      >>> from sparknlp.pretrained import PretrainedPipeline
      >>> data = spark.createDataFrame([[1, "New York and New Jersey aren't that far apart actually."]]).toDF("id", "text")
  
      Define pretrained pipeline that extracts Named Entities amongst other things
      and apply the `Finisher` on it.
  
      >>> pipeline = PretrainedPipeline("explain_document_dl")
      >>> finisher = Finisher().setInputCols("entities").setOutputCols("output")
      >>> explainResult = pipeline.transform(data)
  
      Show results.
  
      >>> explainResult.selectExpr("explode(entities)").show(truncate=False)
      +------------------------------------------------------------------------------------------------------------------------------------------------------+
      |entities                                                                                                                                              |
      +------------------------------------------------------------------------------------------------------------------------------------------------------+
      |[[chunk, 0, 7, New York, [entity -> LOC, sentence -> 0, chunk -> 0], []], [chunk, 13, 22, New Jersey, [entity -> LOC, sentence -> 0, chunk -> 1], []]]|
      +------------------------------------------------------------------------------------------------------------------------------------------------------+
      >>> result = finisher.transform(explainResult)
      >>> result.select("output").show(truncate=False)
      +----------------------+
      |output                |
      +----------------------+
      |[New York, New Jersey]|
      +----------------------+
  
      See Also
      --------
      Finisher : for finishing Strings
      
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docstr: Gets input columns name of annotations.
docstr_tok: ["Gets","input","columns","name","of","annotations","."]
code: def getInputCols(self):
      """Gets input columns name of annotations."""
      return self.getOrDefault(self.inputCols)
  
file_path: spark-nlp/python/sparknlp/base/finisher.py
imports: from pyspark import keyword_only
  from pyspark.ml.param import TypeConverters, Params, Param
  from sparknlp.internal import AnnotatorTransformer
label_desc: Use image node_id 11 for calling the Finisher obj's underlying member method code with example usage: obj.getInputCols() and returns: self