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Exercise

Applying TF-IDF to book descriptions

PyBooks has collected several book descriptions and wants to identify important words within them using the TF-IDF encoding technique. By doing this, they hope to gain more insights into the unique attributes of each book to help with their book recommendation system.

The following packages have been imported for you: torch, torchtext.

Instructions

100 XP
  • Import the class from sklearn.feature_extraction.text that converts a collection of raw documents to a matrix of TF-IDF features.
  • Instantiate an object of this class, then use this object to encode the descriptions into a TF-IDF matrix of vectors.
  • Retrieve and display the first five feature names from the vectorizer and encoded vectors from tfidf_encoded_descriptions.