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

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

# Let's create a dummy dataset data = [' '.join(tokens)] in3x,net,watch,14zwhrd6,dildo,18

# TF-IDF transformer tfidf = TfidfTransformer() tfidf_features = tfidf.fit_transform(count_features) # Your data text = "in3x

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer feature_names) print("TF-IDF Features:"

# Vectorizer to convert text into a matrix of token counts vectorizer = CountVectorizer() count_features = vectorizer.fit_transform(data)

# 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.

# Tokenize (simple split) tokens = text.split(',')