Context window handling in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split text into chunks of fixed size for context window processing.

NLP

def split_text(text, chunk_size):
    return [text[i:i+[1]] for i in range(0, len(text), chunk_size)]

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

Atext

Blen(text)

Cchunk_size

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Using the entire text length instead of chunk size.

Using the loop variable i as the slice size.

The chunk size determines the length of each text segment for the context window.

2fill in blank

medium

Complete the code to create overlapping context windows with a given stride.

NLP

def sliding_windows(text, window_size, stride):
    return [text[i:i+window_size] for i in range(0, len(text) - window_size + 1, [1])]

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

Awindow_size

Blen(text)

Ci

Dstride

Attempts:

3 left

💡 Hint

Common Mistakes

Using window size as the step, which causes no overlap.

Using the loop variable i as the step size.

The stride controls how much the window moves forward each step, allowing overlap.

3fill in blank

hard

Fix the error in the code that tries to pad a context window to a fixed size.

NLP

def pad_window(window, fixed_size, pad_token):
    if len(window) < fixed_size:
        window += [1] * (fixed_size - len(window))
    return window

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

Awindow

Bpad_token

Cfixed_size

Dlen(window)

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to add the window to itself instead of padding.

Using fixed_size or length as the padding token.

Padding uses the pad_token repeated to fill the window to fixed size.

4fill in blank

hard

Fill both blanks to create a dictionary mapping each chunk to its start index in the text.

NLP

def chunk_indices(text, chunk_size):
    return {text[i:i+[1]]: i for i in range(0, len(text), [2])}

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

Achunk_size

Blen(text)

Cchunk_size // 2

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Using different values for slice length and step size causing errors.

Using half chunk size for step causing overlapping.

Both blanks use chunk_size: slice length and step size for non-overlapping chunks.

5fill in blank

hard

Fill all three blanks to filter chunks longer than min_length and map them to their lengths.

NLP

def filter_chunks(chunks, min_length):
    return {chunk: len(chunk) for chunk in chunks if len(chunk) [1] [2] and chunk [3] ''}

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

A>

Bmin_length

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of != to check for non-empty strings.

Using < instead of > for length comparison.

Filter chunks longer than min_length and not empty strings.

Practice

1. What does the term context window mean in natural language processing?

easy

A. A method to remove stop words from text

B. The entire document used for training a model

C. A list of all words in a sentence

D. A small part of text around a word used to understand its meaning

Solution

1. Step 1: Understand the definition of context window

   The context window refers to a limited number of words surrounding a target word to help understand its meaning.

2. Step 2: Compare options with the definition

   Only A small part of text around a word used to understand its meaning correctly describes this as a small part of text around a word. Other options describe unrelated concepts.

3. Final Answer:

   A small part of text around a word used to understand its meaning -> Option D

4. Quick Check:

   Context window = small text part around word [OK]

Hint: Context window = nearby words around a target word [OK]

Common Mistakes:

• Confusing context window with entire document
• Thinking it means all words in a sentence
• Mixing it up with stop word removal

2. Which of the following is the correct way to define a context window of size 3 around the word at index 5 in a list words?

easy

A. words[4:7]

B. words[3:8]

C. words[2:7]

D. words[5:8]

Solution

1. Step 1: Understand context window size and indexing

   A window size of 3 means 3 words total, usually centered on the target word. For index 5, the window covers indices 4, 5, 6.

2. Step 2: Check each option's slice range

   words[4:7] slices words[4:7], which includes indices 4, 5, 6 (3 words). Others include wrong ranges or counts.

3. Final Answer:

   words[4:7] -> Option A

4. Quick Check:

   Window size 3 around index 5 = indices 4 to 6 [OK]

Hint: Slice from index-1 to index+2 for window size 3 [OK]

Common Mistakes:

• Using wrong slice indices causing off-by-one errors
• Including too many or too few words
• Not centering window on target word

3. Given the code below, what will be the output?

words = ['I', 'love', 'to', 'eat', 'apples', 'and', 'bananas']
index = 4
window_size = 3
start = max(0, index - window_size // 2)
end = min(len(words), index + window_size // 2 + 1)
context = words[start:end]
print(context)

medium

A. ['to', 'eat', 'apples']

B. ['eat', 'apples', 'and']

C. ['apples', 'and', 'bananas']

D. ['love', 'to', 'eat']

Solution

1. Step 1: Calculate start and end indices

   window_size is 3, so window_size // 2 = 1. start = max(0, 4 - 1) = 3, end = min(7, 4 + 1 + 1) = 6.

2. Step 2: Extract words from start to end

   words[3:6] = ['eat', 'apples', 'and'].

3. Final Answer:

   ['eat', 'apples', 'and'] -> Option B

4. Quick Check:

   Slice words[3:6] = ['eat', 'apples', 'and'] [OK]

Hint: Calculate start/end with floor division and slice accordingly [OK]

Common Mistakes:

• Off-by-one errors in slicing
• Ignoring max/min boundaries
• Misunderstanding integer division

4. The following code tries to get a context window but sometimes throws an error. What is the main issue?

def get_context(words, index, window_size):
    start = index - window_size // 2
    end = index + window_size // 2 + 1
    return words[start:end]

words = ['hello', 'world']
print(get_context(words, 0, 3))

medium

A. index is out of range

B. window_size must be even

C. start can be negative causing an IndexError

D. The function does not return a list

Solution

1. Step 1: Analyze start index calculation

   For index=0 and window_size=3, start = 0 - 1 = -1, which is negative.

2. Step 2: Understand Python slicing with negative start

   Negative start in slicing accesses from the end, which may cause unexpected results or errors if out of range.

3. Final Answer:

   start can be negative causing an IndexError -> Option C

4. Quick Check:

   Negative start index causes slicing issues [OK]

Hint: Check if start index is negative before slicing [OK]

Common Mistakes:

• Assuming negative indices always work safely
• Thinking window_size must be even
• Ignoring index bounds

5. You want to build a model that uses a context window of size 5 to understand words in sentences. Which approach best handles sentences shorter than 5 words without errors?

hard

A. Pad the sentence with special tokens to length 5 before extracting the window

B. Always extract 5 words ignoring sentence length, causing errors if too short

C. Use only the first word as context if sentence is short

D. Skip sentences shorter than 5 words during training

Solution

1. Step 1: Understand the problem with short sentences

   Sentences shorter than the window size cause indexing errors or incomplete context.

2. Step 2: Evaluate options for handling short sentences

   Padding with special tokens ensures fixed length and avoids errors, unlike skipping or ignoring length.

3. Final Answer:

   Pad the sentence with special tokens to length 5 before extracting the window -> Option A

4. Quick Check:

   Padding fixes short sentence context window issues [OK]

Hint: Pad short sentences to window size to avoid errors [OK]

Common Mistakes:

• Ignoring short sentences causing runtime errors
• Skipping data reduces training quality
• Using incomplete context weakens model understanding