Custom Component Example - Spacy Typing CST Test
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Custom Component Example — Spacy Code
Adds a custom pipeline component to process text.
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.')Spacy Language Guide
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.
Primary Use Cases
- ▸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
Notable Features
- ▸Industrial-strength performance and speed
- ▸Pre-trained models for multiple languages
- ▸Rule-based matching and custom pipelines
- ▸Integration with deep learning frameworks
- ▸Extensible with custom components and vectors
Origin & Creator
spaCy was created by Matthew Honnibal and Ines Montani in 2015, aiming to provide industrial-strength NLP in Python with speed and accuracy.
Industrial Note
spaCy is widely used in chatbots, text analytics, sentiment analysis, information extraction, recommendation systems, and any application that requires structured NLP pipelines.