Custom pipeline components in NLP

Introduction

Custom pipeline components let you add your own steps to process text in NLP. This helps you tailor the pipeline to your specific needs.

You want to clean or modify text in a special way before analysis.

You need to add extra information to text, like tags or labels.

You want to run your own code between standard NLP steps.

You want to customize how the pipeline handles your data.

You want to measure or log something during processing.

Syntax

NLP

def custom_component(doc):
    # your code here
    return doc

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

The function takes a doc object and returns it after processing.

Use nlp.add_pipe() to add your component to the pipeline.

Examples

This component adds an uppercase version of each token as a custom attribute.

NLP

def uppercase_component(doc):
    for token in doc:
        token._.upper = token.text.upper()
    return doc

nlp.add_pipe(uppercase_component, name='uppercase', last=True)

This component prints the number of tokens in the document during processing.

NLP

def count_tokens(doc):
    print(f'Tokens in doc: {len(doc)}')
    return doc

nlp.add_pipe(count_tokens, name='count_tokens', last=False)

Sample Model

This program adds a custom pipeline component that marks tokens longer than 5 characters. It then prints each token with this info.

NLP

import spacy
from spacy.tokens import Token

# Load small English model
nlp = spacy.load('en_core_web_sm')

# Register a custom token attribute 'is_long'
Token.set_extension('is_long', default=False)

# Define custom component to mark tokens longer than 5 characters
@spacy.Language.component('long_token_marker')
def long_token_marker(doc):
    for token in doc:
        token._.is_long = len(token.text) > 5
    return doc

# Add the component to the pipeline
nlp.add_pipe('long_token_marker', last=True)

# Process text
text = 'Spacy is a great library for natural language processing.'
doc = nlp(text)

# Print tokens and if they are long
for token in doc:
    print(f'{token.text}: is_long={token._.is_long}')

Important Notes

Custom components must always return the doc object.

You can add custom attributes to tokens, spans, or docs using set_extension.

Use @spacy.Language.component decorator to register components cleanly.

Summary

Custom pipeline components let you add your own processing steps in NLP pipelines.

They take a doc and return it after changes.

You can add custom data or behavior to tokens or documents this way.

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