sent1 = 'The cat is walking in the bedroom'
sent2 = 'A dog was running across the kitchen'
 
from sklearn.feature_extraction.text import CountVectorizer
count_vec = CountVectorizer()
 
sentences = [sent1, sent2]
print(count_vec.fit_transform(sentences).toarray())
print(count_vec.get_feature_names())
 
import nltk
tokens_l = nltk.word_tokenize(sent1)
print(tokens_l)

[[0 1 1 0 1 1 0 0 2 1 0]
 [1 0 0 1 0 0 1 1 1 0 1]]
['across', 'bedroom', 'cat', 'dog', 'in', 'is', 'kitchen', 'running', 'the', 'walking', 'was']
['The', 'cat', 'is', 'walking', 'in', 'the', 'bedroom']