One-hot encoded book titles

PyBooks wants to catalog and analyze the book genres in its library. Apply one-hot encoding to a list of book genres to make them machine-readable.

torch has been imported for you.

This exercise is part of the course

Deep Learning for Text with PyTorch

Exercise instructions

Define the size of the vocabulary and save to vocab_size.
Create one-hot vectors using the appropriate torch technique and vocab_size.
Create a dictionary mapping genres to their corresponding one-hot vectors using dictionary comprehension; the dictionary keys should be the genre.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

genres = ['Fiction','Non-fiction','Biography', 'Children','Mystery']

# Define the size of the vocabulary
vocab_size = ____(____)

# Create one-hot vectors
one_hot_vectors = torch.____(____)

# Create a dictionary mapping genres to their one-hot vectors
one_hot_dict = {____: ____[i] for i, genre in enumerate(genres)}

for genre, vector in one_hot_dict.items():
    print(f'{genre}: {vector.numpy()}')

Edit and Run Code

This exercise is part of the course

Deep Learning for Text with PyTorch

AdvancedSkill Level

4.8+

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This chapter introduces you to deep learning for text and its applications. Learn how to use PyTorch for text processing and get hands-on experience with techniques such as tokenization, stemming, stopword removal, and more. Understand the importance of encoding text data and implement encoding techniques using PyTorch. Finally, consolidate your knowledge by building a text processing pipeline combining these techniques.

Exercise 1: Introduction to preprocessing for text Exercise 2: Word frequency analysis Exercise 3: Preprocessing text Exercise 4: Encoding text data Exercise 5: One-hot encoded book titles

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