cv = CountVectorizer(min_df=0., max_df=1.) cv_matrix = cv.fit_transform(norm_corpus)

As an AI language model, I cannot access any data or information outside of the conversation. However, I can explain what the code snippet means in general terms.

The code snippet is related to natural language processing (NLP) and machine learning. CountVectorizer is a method from the scikit-learn library in Python used for text preprocessing. It transforms a collection of text documents into a matrix of token counts.

The "min_df" parameter specifies the minimum frequency a word needs to appear in the corpus to be included in the vocabulary. The "max_df" parameter specifies the maximum frequency a word can appear in the corpus to be included in the vocabulary.

The "cv_matrix" variable is the output of the CountVectorizer method. It is a sparse matrix that represents the frequency of each word in each document of the corpus.