class_docstr: This annotator takes a sequence of strings (e.g. the output of a
  Tokenizer, Normalizer, Lemmatizer, and Stemmer) and drops all the stop words
  from the input sequences.
  
  By default, it uses stop words from MLlibs `StopWordsRemover
  <https://spark.apache.org/docs/latest/ml-features#stopwordsremover>`__. Stop
  words can also be defined by explicitly setting them with
  :meth:`.setStopWords` or loaded from pretrained models using ``pretrained``
  of its companion object.
  
  
  >>> stopWords = StopWordsCleaner.pretrained() \
  ...     .setInputCols(["token"]) \
  ...     .setOutputCol("cleanTokens")
  
  This will load the default pretrained model ``"stopwords_en"``.
  
  For available pretrained models please see the `Models Hub
  <https://sparknlp.orgtask=Stop+Words+Removal>`__.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``TOKEN``              ``TOKEN``
  ====================== ======================
  
  Parameters
  ----------
  stopWords
      The words to be filtered out, by default english stopwords from Spark ML
  caseSensitive
      Whether to consider case, by default False
  locale
      Locale of the input. ignored when case sensitive, by default locale of
      the JVM
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentenceDetector = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token")
  >>> stopWords = StopWordsCleaner() \
  ...     .setInputCols(["token"]) \
  ...     .setOutputCol("cleanTokens") \
  ...     .setCaseSensitive(False)
  >>> pipeline = Pipeline().setStages([
  ...       documentAssembler,
  ...       sentenceDetector,
  ...       tokenizer,
  ...       stopWords
  ...     ])
  >>> data = spark.createDataFrame([
  ...     ["This is my first sentence. This is my second."],
  ...     ["This is my third sentence. This is my forth."]
  ... ]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("cleanTokens.result").show(truncate=False)
  +-------------------------------+
  |result                         |
  +-------------------------------+
  |[first, sentence, ., second, .]|
  |[third, sentence, ., forth, .] |
  +-------------------------------+
docstr: Sets whether to do a case sensitive comparison, by default False.
  
  Parameters
  ----------
  value : bool
      Whether to do a case sensitive comparison
docstr_tok: ["Sets","whether","to","do","a","case","sensitive","comparison",",","by","default","False",".","Parameters","--","--","--","--","--","value",":","bool","Whether","to","do","a","case","sensitive","comparison"]
code: def setCaseSensitive(self, value):
      """Sets whether to do a case sensitive comparison, by default False.
  
      Parameters
      ----------
      value : bool
          Whether to do a case sensitive comparison
      """
      return self._set(caseSensitive=value)
  
file_path: spark-nlp/python/sparknlp/annotator/stop_words_cleaner.py
imports: from sparknlp.common import *
label_desc: Use image node_id 3 for calling the StopWordsCleaner obj's underlying member method code with example usage: obj.setCaseSensitive(value) and returns: self