Learn Spacy - 10 Code Examples & CST Typing Practice Test

spaCy is an open-source Python library for advanced natural language processing (NLP). It provides efficient tools for text parsing, tokenization, named entity recognition, part-of-speech tagging, and integration with machine learning workflows.

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spaCy Named Entity Recognition Example spaCy Tokenization Example spaCy Part-of-Speech Tagging Example spaCy Dependency Parsing Example spaCy Lemmatization Example spaCy Sentence Segmentation Example spaCy Matcher Example spaCy Entity Ruler Example spaCy Text Similarity Example spaCy Custom Component Example

Learn SPACY with Real Code Examples

Updated Nov 24, 2025

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Code Sample Descriptions

spaCy Named Entity Recognition Example

import spacy

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

# Sample text
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')

# Print named entities
for ent in doc.ents:
    print(ent.text, ent.label_)

A minimal spaCy example performing named entity recognition on a sample text.

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spaCy Tokenization Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is an amazing NLP library.'
doc = nlp(text)

# Print tokens
for token in doc:
    print(token.text)

Splits text into tokens using spaCy tokenizer.

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spaCy Part-of-Speech Tagging Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is fast and accurate.'
doc = nlp(text)

# Print token POS tags
for token in doc:
    print(token.text, token.pos_, token.tag_)

Tags each token with its part-of-speech (POS) label.

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spaCy Dependency Parsing Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy parses text efficiently.'
doc = nlp(text)

# Print dependencies
for token in doc:
    print(token.text, token.dep_, token.head.text)

Displays syntactic dependencies between tokens.

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spaCy Lemmatization Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'running runs ran'
doc = nlp(text)

# Print lemmas
for token in doc:
    print(token.text, token.lemma_)

Extracts the base form (lemma) of each token.

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spaCy Sentence Segmentation Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is fast. It is easy to use.'
doc = nlp(text)

# Print sentences
for sent in doc.sents:
    print(sent.text)

Splits text into sentences.

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spaCy Matcher Example

import spacy
from spacy.matcher import Matcher

nlp = spacy.load('en_core_web_sm')
doc = nlp('I love NLP and machine learning')

matcher = Matcher(nlp.vocab)
pattern = [{'LOWER':'nlp'}]
matcher.add('NLP_PATTERN', [pattern])

matches = matcher(doc)
for match_id, start, end in matches:
    print(doc[start:end].text)

Matches specific token patterns in text using spaCy Matcher.

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spaCy Entity Ruler Example

import spacy
from spacy.pipeline import EntityRuler

nlp = spacy.load('en_core_web_sm')
ruler = EntityRuler(nlp)
ruler.add_patterns([{'label':'ORG','pattern':'OpenAI'}])
nlp.add_pipe(ruler, before='ner')

doc = nlp('OpenAI develops AI models.')
for ent in doc.ents:
    print(ent.text, ent.label_)

Adds custom named entities using spaCy's EntityRuler.

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spaCy Text Similarity Example

import spacy

nlp = spacy.load('en_core_web_md')
text1 = nlp('I love machine learning')
text2 = nlp('I enjoy NLP')

similarity = text1.similarity(text2)
print(similarity)

Computes similarity between two texts using spaCy vectors.

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spaCy Custom Component Example

import spacy

nlp = spacy.load('en_core_web_sm')

def custom_component(doc):
    print('Processing text:', doc.text)
    return doc

nlp.add_pipe(custom_component, last=True)
doc = nlp('SpaCy pipelines are powerful.')

Adds a custom pipeline component to process text.

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Frequently Asked Questions about Spacy

What is Spacy?

What are the primary use cases for Spacy?

Tokenization, lemmatization, and text normalization. Named entity recognition (NER) and part-of-speech tagging. Dependency parsing and syntactic analysis. Text classification and sentiment analysis. Integration with machine learning pipelines for NLP tasks

What are the strengths of Spacy?

Fast and efficient NLP processing. Supports multiple languages and models. Easy integration with ML/DL pipelines. Extensible pipelines and custom components. Excellent documentation and active community

What are the limitations of Spacy?

Limited high-level sentiment analysis or summarization out-of-the-box. Some models are large and memory-intensive. Requires familiarity with NLP concepts for advanced tasks. GPU support is optional and requires setup. Not ideal for training very large language models from scratch

How can I practice Spacy typing speed?

CodeSpeedTest offers 10+ real Spacy code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

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Code Sample Descriptions

spaCy Named Entity Recognition Example

import spacy

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

# Sample text
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')

# Print named entities
for ent in doc.ents:
    print(ent.text, ent.label_)

A minimal spaCy example performing named entity recognition on a sample text.

Let’s Try →

spaCy Tokenization Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is an amazing NLP library.'
doc = nlp(text)

# Print tokens
for token in doc:
    print(token.text)

Splits text into tokens using spaCy tokenizer.

Let’s Try →

spaCy Part-of-Speech Tagging Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is fast and accurate.'
doc = nlp(text)

# Print token POS tags
for token in doc:
    print(token.text, token.pos_, token.tag_)

Tags each token with its part-of-speech (POS) label.

Let’s Try →

spaCy Dependency Parsing Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy parses text efficiently.'
doc = nlp(text)

# Print dependencies
for token in doc:
    print(token.text, token.dep_, token.head.text)

Displays syntactic dependencies between tokens.

Let’s Try →

spaCy Lemmatization Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'running runs ran'
doc = nlp(text)

# Print lemmas
for token in doc:
    print(token.text, token.lemma_)

Extracts the base form (lemma) of each token.

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spaCy Sentence Segmentation Example

import spacy

nlp = spacy.load('en_core_web_sm')
text = 'SpaCy is fast. It is easy to use.'
doc = nlp(text)

# Print sentences
for sent in doc.sents:
    print(sent.text)

Splits text into sentences.

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spaCy Matcher Example

import spacy
from spacy.matcher import Matcher

nlp = spacy.load('en_core_web_sm')
doc = nlp('I love NLP and machine learning')

matcher = Matcher(nlp.vocab)
pattern = [{'LOWER':'nlp'}]
matcher.add('NLP_PATTERN', [pattern])

matches = matcher(doc)
for match_id, start, end in matches:
    print(doc[start:end].text)

Matches specific token patterns in text using spaCy Matcher.

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spaCy Entity Ruler Example

import spacy
from spacy.pipeline import EntityRuler

nlp = spacy.load('en_core_web_sm')
ruler = EntityRuler(nlp)
ruler.add_patterns([{'label':'ORG','pattern':'OpenAI'}])
nlp.add_pipe(ruler, before='ner')

doc = nlp('OpenAI develops AI models.')
for ent in doc.ents:
    print(ent.text, ent.label_)

Adds custom named entities using spaCy's EntityRuler.

Let’s Try →

spaCy Text Similarity Example

import spacy

nlp = spacy.load('en_core_web_md')
text1 = nlp('I love machine learning')
text2 = nlp('I enjoy NLP')

similarity = text1.similarity(text2)
print(similarity)

Computes similarity between two texts using spaCy vectors.

Let’s Try →

spaCy Custom Component Example

import spacy

nlp = spacy.load('en_core_web_sm')

def custom_component(doc):
    print('Processing text:', doc.text)
    return doc

nlp.add_pipe(custom_component, last=True)
doc = nlp('SpaCy pipelines are powerful.')

Adds a custom pipeline component to process text.

Let’s Try →

Frequently Asked Questions about Spacy

What is Spacy?

What are the primary use cases for Spacy?

What are the strengths of Spacy?

What are the limitations of Spacy?

How can I practice Spacy typing speed?

CodeSpeedTest offers 10+ real Spacy code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.