original_docstring: Tokenize, remove stopwords, and stem words.
docstring: Tokenize, remove stopwords, and stem words.
docstring_tokens: Tokenize remove stopwords and stem words .
code: def preprocess_text(text: str) -> list:
      """Tokenize, remove stopwords, and stem words."""
      processed_text = []
      stop_words = set(stopwords.words("english"))
      wnl = WordNetLemmatizer()
      for token in gensim.utils.simple_preprocess(text):
          if token not in stop_words:
              # Lemmatizer leaves input word unchanged if not found in WordNet
              processed_text.append(wnl.lemmatize(token))
      return processed_text
code_tokens: def preprocess_text ( text : str ) -> list : processed_text = [ ] stop_words = set ( stopwords . words ( "english" ) ) wnl = WordNetLemmatizer ( ) for token in gensim . utils . simple_preprocess ( text ) : if token not in stop_words : processed_text . append ( wnl . lemmatize ( token ) ) return processed_text
short_docstring: Tokenize, remove stopwords, and stem words.
short_docstring_tokens: Tokenize remove stopwords and stem words .
comment: """Tokenize, remove stopwords, and stem words."""$SEP$# Lemmatizer leaves input word unchanged if not found in WordNet