Advanced Chatbots with Deep Learning and Python - Vectorize Stories

What is the purpose of defining the 'vectorized_stories_stories' function?

• To find the maximum question length

• To calculate the average story length

• To vectorize stories, questions, and answers

• To create a new tokenizer

Which data structures are initialized for storing stories, questions, and answers?

• X, Y, Z

• S, Q, A

• A, B, C

• X, XQ, Y

How are words in stories converted during the iteration process?

• By converting them into word indices

• By converting them to uppercase

• By reversing the word order

• By using a dictionary of synonyms

What is the role of numpy arrays in handling answers?

• To calculate the average question length

• To vectorize 'yes' or 'no' answers

• To append new data to the tokenizer

• To store the length of each story

What is the final step in the 'vectorized_stories_stories' function?

• Returning padded sequences for stories and questions

• Calculating the average word index

• Returning the maximum story length

• Appending data to smaller structures

Why is maximum length used for padding sequences?

• To make the sequences as short as possible

• To avoid using any padding

• To minimize the memory usage

• To ensure consistent padding across all data

What is the purpose of appending smaller X, XQ, and Y to their larger counterparts?

• To reduce the number of stories processed

• To ensure all data is included in the final output

• To create a backup of the data

• To increase the size of the tokenizer