How can you ensure sequences are truncated to length 5 keeping the last tokens and padded if shorter using Keras?

hard📝 Application Q9 of 15

NLP - Sequence Models for NLP

Apad_sequences(sequences, maxlen=5, truncating='pre', padding='post')

Bpad_sequences(sequences, maxlen=5, truncating='post', padding='post')

Cpad_sequences(sequences, maxlen=5, truncating='post', padding='pre')

Dpad_sequences(sequences, maxlen=5, truncating='pre', padding='pre')

Step-by-Step Solution

Solution:

Step 1: Understand truncating parameter
'truncating="post"' removes tokens from the end, so to keep last tokens, use 'post'.
Step 2: Understand padding parameter
'padding="post"' adds padding tokens at the end to reach maxlen.
Step 3: Combine parameters
To keep last tokens and pad at the end, use truncating='post' and padding='post'.
Final Answer:
pad_sequences(sequences, maxlen=5, truncating='post', padding='post') -> Option B
Quick Check:
Use truncating='post' to keep last tokens [OK]

Quick Trick: Use truncating='post' and padding='post' to keep last tokens [OK]

Common Mistakes:

MISTAKES

Master "Sequence Models for NLP" in NLP

9 interactive learning modes - each teaches the same concept differently

Want More Practice?

15+ quiz questions · All difficulty levels · Free

More NLP Quizzes