class_docstr: Annotator that detects sentence boundaries using regular expressions.
  
  The following characters are checked as sentence boundaries:
  
  1. Lists ("(i), (ii)", "(a), (b)", "1., 2.")
  2. Numbers
  3. Abbreviations
  4. Punctuations
  5. Multiple Periods
  6. Geo-Locations/Coordinates ("N°. 1026.253.553.")
  7. Ellipsis ("...")
  8. In-between punctuations
  9. Quotation marks
  10. Exclamation Points
  11. Basic Breakers (".", ";")
  
  For the explicit regular expressions used for detection, refer to source of
  `PragmaticContentFormatter <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/pragmatic/PragmaticContentFormatter.scala>`__.
  
  To add additional custom bounds, the parameter ``customBounds`` can be set with an array:
  
  >>> sentence = SentenceDetector() \
  >>>     .setInputCols(["document"]) \
  >>>     .setOutputCol("sentence") \
  >>>     .setCustomBounds(["\n\n"])
  
  If only the custom bounds should be used, then the parameter ``useCustomBoundsOnly`` should be set to ``true``.
  
  Each extracted sentence can be returned in an Array or exploded to separate rows,
  if ``explodeSentences`` is set to ``true``.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT``           ``DOCUMENT``
  ====================== ======================
  
  Parameters
  ----------
  useAbbreviations
      whether to apply abbreviations at sentence detection, by default True
  customBounds
      characters used to explicitly mark sentence bounds, by default []
  useCustomBoundsOnly
      Only utilize custom bounds in sentence detection, by default False
  customBoundsStrategy
      Sets how to return matched custom bounds, by default "none".
  
      Will have no effect if no custom bounds are used.
      Possible values are:
  
      - "none" - Will not return the matched bound
      - "prepend" - Prepends a sentence break to the match
      - "append" - Appends a sentence break to the match
  explodeSentences
      whether to explode each sentence into a different row, for better
      parallelization, by default False
  splitLength
      length at which sentences will be forcibly split
  minLength
      Set the minimum allowed length for each sentence, by default 0
  maxLength
      Set the maximum allowed length for each sentence, by default 99999
  detectLists
      whether detect lists during sentence detection, by default True
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  ...     .setCustomBounds(["\n\n"])
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentence
  ... ])
  >>> data = spark.createDataFrame([["This is my first sentence. This my second.\n\nHow about a third?"]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(sentence) as sentences").show(truncate=False)
  +------------------------------------------------------------------+
  |sentences                                                         |
  +------------------------------------------------------------------+
  |[document, 0, 25, This is my first sentence., [sentence -> 0], []]|
  |[document, 27, 41, This my second., [sentence -> 1], []]          |
  |[document, 43, 60, How about a third?, [sentence -> 2], []]       |
  +------------------------------------------------------------------+
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docstr: Sets whether detect lists during sentence detection, by default True
  
  Parameters
  ----------
  value : bool
      Whether detect lists during sentence detection
docstr_tok: ["Sets","whether","detect","lists","during","sentence","detection",",","by","default","True","Parameters","--","--","--","--","--","value",":","bool","Whether","detect","lists","during","sentence","detection"]
code: def setDetectLists(self, value):
      """Sets whether detect lists during sentence detection, by default True
  
      Parameters
      ----------
      value : bool
          Whether detect lists during sentence detection
      """
      return self._set(detectLists=value)
  
file_path: spark-nlp/python/sparknlp/annotator/sentence/sentence_detector.py
imports: from sparknlp.common import *
label_desc: Use image node_id 4 for calling the SentenceDetector obj's underlying member method code with example usage: obj.setDetectLists(value) and returns: self