In3x,net,watch,14zwhrd6,dildo,18 Apr 2026

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

# Tokenize (simple split) tokens = text.split(',') in3x,net,watch,14zwhrd6,dildo,18

# Your data text = "in3x,net,watch,14zwhrd6,dildo,18" from sklearn

# Viewing features feature_names = vectorizer.get_feature_names_out() print("Features:", feature_names) print("TF-IDF Features:", tfidf_features.toarray()) This example uses CountVectorizer and TfidfTransformer from scikit-learn to create basic features from your text. Adjustments would be needed based on your specific use case and data. from sklearn.feature_extraction.text import CountVectorizer

# Let's create a dummy dataset data = [' '.join(tokens)]