Summarization in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the summarization pipeline from Hugging Face Transformers.

Prompt Engineering / GenAI

from transformers import pipeline
summarizer = pipeline([1])

Drag options to blanks, or click blank then click option'

A"text-generation"

B"sentiment-analysis"

C"summarization"

D"translation"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'translation' instead of 'summarization'.

Using 'text-generation' which creates new text, not summaries.

The pipeline type for text summarization is "summarization".

2fill in blank

medium

Complete the code to summarize the input text using the summarizer pipeline.

Prompt Engineering / GenAI

text = "Machine learning helps computers learn from data."
summary = summarizer([1], max_length=20, min_length=5, do_sample=False)
print(summary[0]['summary_text'])

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Atext

Btext.split()

Ctext.lower()

Dtext.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of words instead of a string.

Passing an uppercase or lowercase transformed string unnecessarily.

The summarizer expects a string input, so passing 'text' directly is correct.

3fill in blank

hard

Fix the error in the code to correctly get the summary text from the output.

Prompt Engineering / GenAI

result = summarizer(text, max_length=30, min_length=10, do_sample=False)
summary_text = result[1]
print(summary_text)

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A[0]

B['summary_text']

C['text']

D[0]['summary_text']

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to access 'summary_text' directly from the list.

Using wrong keys like 'text' instead of 'summary_text'.

The summarizer returns a list with one dict; access the first item and then the 'summary_text' key.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each sentence to its summary length.

Prompt Engineering / GenAI

sentences = ["AI is fascinating.", "It can learn patterns.", "Summarization helps understand text."]
summary_lengths = {sentence: len(summarizer(sentence, max_length=10, min_length=5, do_sample=False)[1]) for sentence in sentences if len(sentence) [2] 10}
print(summary_lengths)

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A[0]['summary_text']

B>

C<

D[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using '[0]' only returns a dict, not the text string.

Using '<' instead of '>' in the filter condition.

Access the summary text with [0]['summary_text'] and filter sentences longer than 10 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each sentence's uppercase form to its summary.

Prompt Engineering / GenAI

sentences = ["AI is cool.", "It learns fast.", "Summarization is useful."]
summary_info = [1]: [2] for s in sentences if len(s) [3] 8
print(summary_info)

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As.upper()

Bsummarizer(s, max_length=15, min_length=5, do_sample=False)[0]['summary_text']

C>

Dlen(s)

Attempts:

3 left

💡 Hint

Common Mistakes

Using s instead of s.upper() as key.

Using '<' instead of '>' in the filter condition.

Use s.upper() as key, summary text as value, and filter sentences longer than 8 characters.