Custom pipeline components in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a custom pipeline component function that takes a doc and returns it.

NLP

def custom_component(doc):
    # Process the doc here
    return [1]

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

Atext

Btokens

Cdoc

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Returning None instead of the doc object.

Returning the text string instead of the doc object.

The custom pipeline component must return the processed doc object to continue the pipeline.

2fill in blank

medium

Complete the code to add the custom component to the pipeline named 'custom_component'.

NLP

nlp.add_pipe([1], name='custom_component', last=True)

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

Apipeline

Bprocess_doc

Ccomponent

Dcustom_component

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string instead of the function name.

Using a wrong function name that is not defined.

You add the custom component function by passing its name to add_pipe.

3fill in blank

hard

Fix the error in the custom component that tries to add an attribute 'is_positive' to each token.

NLP

def custom_component(doc):
    for token in doc:
        token.[1] = token.text.startswith('good')
    return doc

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

Apos

B_.is_positive

Cis_positive

Dset_attr

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to set token.is_positive directly without registering the attribute.

Using a wrong attribute name or syntax.

Custom token attributes must be set using the underscore accessor '_.attribute_name'.

4fill in blank

hard

Fill both blanks to register a custom token attribute 'is_positive' with default False.

NLP

from spacy.tokens import Token
Token.set_extension('[1]', default=[2])

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

Ais_positive

BTrue

CFalse

Dsentiment

Attempts:

3 left

💡 Hint

Common Mistakes

Using True as default when you want False.

Using a different attribute name than used in the component.

You register the attribute name 'is_positive' with default value False.

5fill in blank

hard

Fill all three blanks to create a custom pipeline component that sets 'is_positive' on tokens and returns the doc.

NLP

def [1](doc):
    for token in doc:
        token._.[2] = token.text.lower() in [3]
    return doc

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

Acustom_component

Bis_positive

C{'good', 'great', 'happy'}

Dpositive_tokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different function name than registered.

Not using a set for the positive words.

Forgetting to return the doc.

The function name is 'custom_component', attribute is 'is_positive', and the set of positive words is {'good', 'great', 'happy'}.

Practice

1. What is the main purpose of a custom pipeline component in an NLP pipeline?

easy

A. To store the processed documents in a database

B. To replace the entire NLP model with a new one

C. To visualize the text data in charts

D. To add your own processing steps that modify the document

Solution

1. Step 1: Understand the role of pipeline components

   Pipeline components process text step-by-step, modifying or analyzing it.

2. Step 2: Identify what custom components do

   Custom components let you add your own processing steps that change the document or add data.

3. Final Answer:

   To add your own processing steps that modify the document -> Option D

4. Quick Check:

   Custom pipeline components = add processing steps [OK]

Hint: Custom components add steps that change the document [OK]

Common Mistakes:

• Thinking custom components replace the whole model
• Confusing visualization with processing
• Assuming storage is part of pipeline components

2. Which of the following is the correct way to define a custom pipeline component function in Python?

easy

A. def custom_component(text): return text

B. def custom_component(doc): print(doc)

C. def custom_component(doc): return doc

D. def custom_component(): return None

Solution

1. Step 1: Recall the function signature for custom components

   Custom components take a doc object and return it after processing.

2. Step 2: Check each option

   def custom_component(doc): return doc matches the signature and returns the doc. Others either take wrong input or don't return doc.

3. Final Answer:

   def custom_component(doc): return doc -> Option C

4. Quick Check:

   Function takes doc and returns doc [OK]

Hint: Custom component functions take and return doc objects [OK]

Common Mistakes:

• Using text instead of doc as input
• Not returning the doc object
• Missing the doc parameter

3. Given this custom component code:

def add_custom_attr(doc):
    for token in doc:
        token._.is_custom = token.text.isalpha()
    return doc

nlp.add_pipe(add_custom_attr, last=True)

text = 'Hello 123!'
doc = nlp(text)
print([token._.is_custom for token in doc])

What will be the printed output?

medium

A. [True, True, False]

B. [True, False, False]

C. [True, False, True]

D. [False, False, False]

Solution

1. Step 1: Analyze the tokens in the text

   The text 'Hello 123!' splits into tokens: 'Hello', '123', '!'.

2. Step 2: Check the custom attribute logic

   For each token, isalpha() returns True if all characters are letters. 'Hello' is True, '123' and '!' are False.

3. Final Answer:

   [True, False, False] -> Option B

4. Quick Check:

   isalpha() per token = [True, False, False] [OK]

Hint: Check token text with isalpha() for True/False [OK]

Common Mistakes:

• Assuming punctuation is alpha
• Counting tokens incorrectly
• Forgetting to return doc

4. What is wrong with this custom pipeline component code?

def faulty_component(doc):
    for token in doc:
        token._.is_custom = token.text.isdigit()
    # Missing return statement

nlp.add_pipe(faulty_component, last=True)

medium

A. It does not return the doc object

B. It uses an invalid attribute name

C. It modifies tokens outside the loop

D. It should not be added to the pipeline

Solution

1. Step 1: Check the function structure

   The function loops over tokens and sets a custom attribute but does not return the doc.

2. Step 2: Recall pipeline component requirements

   Custom components must return the doc object to continue the pipeline correctly.

3. Final Answer:

   It does not return the doc object -> Option A

4. Quick Check:

   Missing return doc causes pipeline failure [OK]

Hint: Always return doc at end of custom component [OK]

Common Mistakes:

• Forgetting to return doc
• Using wrong attribute names without registration
• Adding component incorrectly

5. You want to create a custom pipeline component that counts how many tokens in a document are uppercase and stores this count as doc._.uppercase_count. Which of the following is the correct approach?

hard

A. Register a doc extension for 'uppercase_count', define a component that counts uppercase tokens, assign the count to doc._.uppercase_count, and return doc

B. Add a token extension for 'uppercase_count' and count uppercase tokens per token

C. Modify tokens in place without registering any extension and return doc

D. Create a new NLP model that outputs uppercase counts directly

Solution

1. Step 1: Understand extension registration

   To add a new attribute to doc._, you must register a doc extension first.

2. Step 2: Implement counting and assignment

   Count uppercase tokens in the component, assign the count to doc._.uppercase_count, then return doc.

3. Final Answer:

   Register a doc extension for 'uppercase_count', define a component that counts uppercase tokens, assign the count to doc._.uppercase_count, and return doc -> Option A

4. Quick Check:

   Doc extension + count + assign + return doc [OK]

Hint: Register doc extension before assigning custom doc attributes [OK]

Common Mistakes:

• Not registering the doc extension before use
• Using token extension for doc-level data
• Not returning doc at the end