def _get_stop_words(self, texts: np.ndarray) -> List[str]:
    """Identify tokens in the extracted vocabulary that contain seed terms.

    Any token containing a seed term will be treated as a stop word (i.e. removed
    from the extracted features).  This prevents the model from training on the same
    tokens used to label the data.

    Args:
      texts: array containing strings for topic assignment

    Returns:
      List specifying which tokens to exclude from the features.
    """
    # Extract vocabulary
    cv = CountVectorizer(tokenizer=self.custom_tokenizer, token_pattern=None)
    cv.fit(texts)
    rawVocabulary = cv.vocabulary_.keys()
    logger.info(f"  Initial vocabulary length: {len(rawVocabulary)}")
    # Identify stop words
    blockedTokens = set()
    for terms in self._seedTerms.values():
      # Remove whitespace and any escaped whitespace characters from seed terms
      blockedTokens |= {re.sub(r"\\s", "", t.strip()) for t in terms}
      # Convert escaped periods to periods
      blockedTokens |= {re.sub(r"\\.", ".", t.strip()) for t in terms}
    logger.info(f"  Total tokens to filter: {len(blockedTokens)}")
    stopWords = [v for v in rawVocabulary if any(t in v for t in blockedTokens)]
    logger.info(f"  Total identified stopwords: {len(stopWords)}")
    return stopWords
Identify tokens in the extracted vocabulary that contain seed terms.

    Any token containing a seed term will be treated as a stop word (i.e. removed
    from the extracted features).  This prevents the model from training on the same
    tokens used to label the data.

    Args:
      texts: array containing strings for topic assignment

    Returns:
      List specifying which tokens to exclude from the features.
    
sourcecode/scoring/topic_model.py