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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

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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()}')
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