class_docstr: Annotator which normalizes raw text from tagged text, e.g. scraped web
      pages or xml documents, from document type columns into Sentence.
  
      Removes all dirty characters from text following one or more input regex
      patterns. Can apply not wanted character removal with a specific policy.
      Can apply lower case normalization.
  
      For extended examples of usage, see the `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb
  >`__.
  
      ====================== ======================
      Input Annotation types Output Annotation type
      ====================== ======================
      ``DOCUMENT``           ``DOCUMENT``
      ====================== ======================
  
      Parameters
      ----------
      action
          action to perform before applying regex patterns on text, by default
          "clean"
      patterns
          normalization regex patterns which match will be removed from document,
          by default ['<[^>]*>']
      replacement
          replacement string to apply when regexes match, by default " "
      lowercase
          whether to convert strings to lowercase, by default False
      policy
          policy to remove pattern from text, by default "pretty_all"
      encoding
          file encoding to apply on normalized documents, by default "UTF-8"
  
      Examples
      --------
      >>> import sparknlp
      >>> from sparknlp.base import *
      >>> from sparknlp.annotator import *
      >>> from pyspark.ml import Pipeline
      >>> documentAssembler = DocumentAssembler() \
      ...     .setInputCol("text") \
      ...     .setOutputCol("document")
      >>> cleanUpPatterns = ["<[^>]>"]
      >>> documentNormalizer = DocumentNormalizer() \
      ...     .setInputCols("document") \
      ...     .setOutputCol("normalizedDocument") \
      ...     .setAction("clean") \
      ...     .setPatterns(cleanUpPatterns) \
      ...     .setReplacement(" ") \
      ...     .setPolicy("pretty_all") \
      ...     .setLowercase(True)
      >>> pipeline = Pipeline().setStages([
      ...     documentAssembler,
      ...     documentNormalizer
      ... ])
      >>> text = """
      ... <div id="theworldsgreatest" class='my-right my-hide-small my-wide toptext' style="font-family:'Segoe UI',Arial,sans-serif">
      ...     THE WORLD'S LARGEST WEB DEVELOPER SITE
      ...     <h1 style="font-size:300%;">THE WORLD'S LARGEST WEB DEVELOPER SITE</h1>
      ...     <p style="font-size:160%;">Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum..</p>
      ... </div>
      ... </div>"""
      >>> data = spark.createDataFrame([[text]]).toDF("text")
      >>> pipelineModel = pipeline.fit(data)
      >>> result = pipelineModel.transform(data)
      >>> result.selectExpr("normalizedDocument.result").show(truncate=False)
      +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      |result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
      +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      |[ the world's largest web developer site the world's largest web developer site lorem ipsum is simply dummy text of the printing and typesetting industry. lorem ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. it has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. it was popularised in the 1960s with the release of letraset sheets containing lorem ipsum passages, and more recently with desktop publishing software like aldus pagemaker including versions of lorem ipsum..]|
      +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      
docstr: Sets replacement string to apply when regexes match, by default " ".
  
  Parameters
  ----------
  value : str
      Replacement string to apply when regexes match
code: def setReplacement(self, value):
      """Sets replacement string to apply when regexes match, by default " ".
  
      Parameters
      ----------
      value : str
          Replacement string to apply when regexes match
      """
      return self._set(replacement=value)
  
file_path: spark-nlp/python/sparknlp/annotator/document_normalizer.py
imports: from sparknlp.common import *
label_desc: Use image node_id 4 for calling the DocumentNormalizer obj's underlying member method code with example usage: obj.setReplacement(value) and returns: self