class_docstr: Overwrites entities of specified strings.
  
  The input for this Annotator have to be entities that are already extracted,
  Annotator type ``NAMED_ENTITY``. The strings specified with
  :meth:`.NerOverwriter.setStopWords` will have new entities assigned to, specified
  with :meth:`.NerOverwriter.setNewResult`.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``NAMED_ENTITY``       ``NAMED_ENTITY``
  ====================== ======================
  
  Parameters
  ----------
  nerWords
      The words to be overwritten
  newNerEntity
      new NER class to apply to those stopwords, by default I-OVERWRITE
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  
  First extract the prerequisite Entities
  
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token")
  >>> embeddings = WordEmbeddingsModel.pretrained() \
  ...     .setInputCols(["sentence", "token"]) \
  ...     .setOutputCol("bert")
  >>> nerTagger = NerDLModel.pretrained() \
  ...     .setInputCols(["sentence", "token", "bert"]) \
  ...     .setOutputCol("ner")
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentence,
  ...     tokenizer,
  ...     embeddings,
  ...     nerTagger
  ... ])
  >>> data = spark.createDataFrame([["Spark NLP Crosses Five Million Downloads, John Snow Labs Announces."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(ner)").show(truncate=False)
  +------------------------------------------------------+
  |col                                                   |
  +------------------------------------------------------+
  |[named_entity, 0, 4, B-ORG, [word -> Spark], []]      |
  |[named_entity, 6, 8, I-ORG, [word -> NLP], []]        |
  |[named_entity, 10, 16, O, [word -> Crosses], []]      |
  |[named_entity, 18, 21, O, [word -> Five], []]         |
  |[named_entity, 23, 29, O, [word -> Million], []]      |
  |[named_entity, 31, 39, O, [word -> Downloads], []]    |
  |[named_entity, 40, 40, O, [word -> ,], []]            |
  |[named_entity, 42, 45, B-ORG, [word -> John], []]     |
  |[named_entity, 47, 50, I-ORG, [word -> Snow], []]     |
  |[named_entity, 52, 55, I-ORG, [word -> Labs], []]     |
  |[named_entity, 57, 65, I-ORG, [word -> Announces], []]|
  |[named_entity, 66, 66, O, [word -> .], []]            |
  +------------------------------------------------------+
  
  The recognized entities can then be overwritten
  
  >>> nerOverwriter = NerOverwriter() \
  ...     .setInputCols(["ner"]) \
  ...     .setOutputCol("ner_overwritten") \
  ...     .setNerWords(["Million"]) \
  ...     .setNewNerEntity("B-CARDINAL")
  >>> nerOverwriter.transform(result).selectExpr("explode(ner_overwritten)").show(truncate=False)
  +---------------------------------------------------------+
  |col                                                      |
  +---------------------------------------------------------+
  |[named_entity, 0, 4, B-ORG, [word -> Spark], []]         |
  |[named_entity, 6, 8, I-ORG, [word -> NLP], []]           |
  |[named_entity, 10, 16, O, [word -> Crosses], []]         |
  |[named_entity, 18, 21, O, [word -> Five], []]            |
  |[named_entity, 23, 29, B-CARDINAL, [word -> Million], []]|
  |[named_entity, 31, 39, O, [word -> Downloads], []]       |
  |[named_entity, 40, 40, O, [word -> ,], []]               |
  |[named_entity, 42, 45, B-ORG, [word -> John], []]        |
  |[named_entity, 47, 50, I-ORG, [word -> Snow], []]        |
  |[named_entity, 52, 55, I-ORG, [word -> Labs], []]        |
  |[named_entity, 57, 65, I-ORG, [word -> Announces], []]   |
  |[named_entity, 66, 66, O, [word -> .], []]               |
  +---------------------------------------------------------+
docstr: Sets new NER class to apply to those stopwords, by default
  I-OVERWRITE.
  
  Parameters
  ----------
  value : str
      NER class to apply the stopwords to
docstr_tok: ["Sets","new","NER","class","to","apply","to","those","stopwords",",","by","default","I-OVERWRITE",".","Parameters","--","--","--","--","--","value",":","str","NER","class","to","apply","the","stopwords","to"]
code: def setNewNerEntity(self, value):
      """Sets new NER class to apply to those stopwords, by default
      I-OVERWRITE.
  
      Parameters
      ----------
      value : str
          NER class to apply the stopwords to
      """
      return self._set(newNerEntity=value)
  
imports: from sparknlp.common import *
label_desc: Use image node_id 3 for calling the NerOverwriter obj's underlying member method code with example usage: obj.setNewNerEntity(value) and returns: self