问spaCy中的自定义停止词不起作用

EN

df['parsed_transcript'] = df['transcript'].apply(nlp)

#making a list of stop words to add
my_stop_words = ["thing", "people", "way", "year", " year " "time",  "lot", "day"]

#adding the list to the stop words
for stopword in my_stop_words:
    lexeme = nlp.vocab[stopword]
    lexeme.is_stop = True

#filtering out stop words and all non noun words
def preprocess_texts(texts_as_csv_column):
#Takes a column from a pandas datafram and converts it into a list of nouns.
    lemmas = []
    for doc in texts_as_csv_column: 
    # Append the lemmas of all nouns that are not stop words
        lemma = ([token.lemma_ for token in doc if token.pos_ == 'NOUN' and not token.is_stop])
        lemmas.append(lemma)

    return lemmas

count = 0
for row in df['list_of_words']:
    for word in row:
        if word == "year":
            count +=1

 print(count)