尝试将文档放入CountVectorizer对象时出现错误

Question

尝试将我的文档放入CountVectorizer对象时出现错误

我的代码：

documents = ['Hello, how are you!','Win money, win from home.','Call me now.','Hello, Call hello you tomorrow?']

from sklearn.feature_extraction.text import CountVectorizer

count_vector = CountVectorizer(stop_words='English')

print(count_vector)
>>> CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None, stop_words='English',strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',tokenizer=None, vocabulary=None)

count_vector.fit(documents)

预期结果：

我想从文档列表中删除所有英文的stop_words。

实际结果：

我遇到错误-

ValueError: not a built-in stop list: English