[Solved] Given the following Python code simulating a sequence model:words = ['deep', 'learning'] state = 0 for word in words:... | NLP

NLP - Sequence Models for NLP

Given the following Python code simulating a sequence model:

words = ['deep', 'learning']
state = 0
for word in words:
    state += len(word)
print(state)

What does this code demonstrate about sequence models?

AThey ignore the length of words and only count the number of words

BThey process all words simultaneously without updating state

CThey process words one by one, updating state based on each word

DThey reset the state after processing each word

Step-by-Step Solution

Solution:

Step 1: Analyze the code
The loop iterates over each word, adding its length to the state variable.
Step 2: Understand sequence processing
This simulates how sequence models update their internal state step-by-step as they process each word.
Final Answer:
They process words one by one, updating state based on each word -> Option C
Quick Check:
State accumulates word lengths sequentially [OK]

Quick Trick: Sequence models update state incrementally per word [OK]

Common Mistakes:

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