class_docstr: Converts a IOB or IOB2 representation of NER to a user-friendly one, by
  associating the tokens of recognized entities and their label. Results in
  ``CHUNK`` Annotation type.
  
  NER chunks can then be filtered by setting a whitelist with
  ``setWhiteList``. Chunks with no associated entity (tagged "O") are
  filtered.
  
  See also `Inside–outside–beginning (tagging)
  <https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>`__
  for more information.
  
  ================================= ======================
  Input Annotation types            Output Annotation type
  ================================= ======================
  ``DOCUMENT, TOKEN, NAMED_ENTITY`` ``CHUNK``
  ================================= ======================
  
  Parameters
  ----------
  whiteList
      If defined, list of entities to process. The rest will be ignored. Do
      not include IOB prefix on labels
  preservePosition
      Whether to preserve the original position of the tokens in the original document
      or use the modified tokens, by default `True`
  
  Examples
  --------
  This is a continuation of the example of the :class:`.NerDLModel`. See that
  class on how to extract the entities. The output of the NerDLModel follows
  the Annotator schema and can be converted like so:
  
  >>> result.selectExpr("explode(ner)").show(truncate=False)
  +----------------------------------------------------+
  |col                                                 |
  +----------------------------------------------------+
  |[named_entity, 0, 2, B-ORG, [word -> U.N], []]      |
  |[named_entity, 3, 3, O, [word -> .], []]            |
  |[named_entity, 5, 12, O, [word -> official], []]    |
  |[named_entity, 14, 18, B-PER, [word -> Ekeus], []]  |
  |[named_entity, 20, 24, O, [word -> heads], []]      |
  |[named_entity, 26, 28, O, [word -> for], []]        |
  |[named_entity, 30, 36, B-LOC, [word -> Baghdad], []]|
  |[named_entity, 37, 37, O, [word -> .], []]          |
  +----------------------------------------------------+
  
  After the converter is used:
  
  >>> converter = NerConverter() \
  ...     .setInputCols(["sentence", "token", "ner"]) \
  ...     .setOutputCol("entities")
  >>> converter.transform(result).selectExpr("explode(entities)").show(truncate=False)
  +------------------------------------------------------------------------+
  |col                                                                     |
  +------------------------------------------------------------------------+
  |[chunk, 0, 2, U.N, [entity -> ORG, sentence -> 0, chunk -> 0], []]      |
  |[chunk, 14, 18, Ekeus, [entity -> PER, sentence -> 0, chunk -> 1], []]  |
  |[chunk, 30, 36, Baghdad, [entity -> LOC, sentence -> 0, chunk -> 2], []]|
  +------------------------------------------------------------------------+
docstr: Sets list of entities to process. The rest will be ignored.
  
  Does not include IOB prefix on labels.
  
  Parameters
  ----------
  entities : List[str]
      If defined, list of entities to process. The rest will be ignored.
docstr_tok: ["Sets","list","of","entities","to","process",".","The","rest","will","be","ignored",".","Does","not","include","IOB","prefix","on","labels",".","Parameters","--","--","--","--","--","entities",":","List","[","str","]","If","defined",",","list","of","entities","to","process",".","The","rest","will","be","ignored","."]
code: def setWhiteList(self, entities):
      """Sets list of entities to process. The rest will be ignored.
  
      Does not include IOB prefix on labels.
  
      Parameters
      ----------
      entities : List[str]
          If defined, list of entities to process. The rest will be ignored.
  
      """
      return self._set(whiteList=entities)
  
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the NerConverter obj's underlying member method code with example usage: obj.setWhiteList(entities) and returns: self