📚 spaCy Tutorial: Natural Language Processing Made Easy

Welcome to the spaCy tutorial! 🌟 Whether you're new to NLP or looking to enhance your skills, this guide will walk you through the essentials of using spaCy for text analysis. Let's dive in!

🧩 What is spaCy?

spaCy is an open-source library for natural language processing (NLP) in Python. It's designed to be fast, efficient, and easy to use. 🚀

• Key Features:
  • Pre-trained models for 40+ languages
  • Tokenization, part-of-speech tagging, named entity recognition
  • Support for custom pipeline components
  • Excellent performance for production use

📝 Getting Started

1. Install spaCy:

   pip install spacy
   

   Then download a language model:

   python -m spacy download en_core_web_sm
   

   ✅ This installs the English model for spaCy.

2. Load the Model:

   import spacy
   nlp = spacy.load("en_core_web_sm")
   

   📌 Use this to process text.

3. Process Text:

   doc = nlp("spaCy is a powerful NLP library.")
   for token in doc:
       print(token.text, token.pos_)
   

   🧠 Output will show tokens and their parts of speech.

📘 Expand Your Knowledge

Want to explore more? Check out our spaCy Introduction for a deeper dive into its architecture and use cases. 📚

🤝 Example: Named Entity Recognition

doc = nlp("Apple is looking to buy a U.S. startup.")
for ent in doc.ents:
    print(ent.text, ent.label_)

📍 Output:

• Apple (ORG)
• U.S. (LOC)

🌐 Additional Resources

• spaCy Documentation – Official guides and API references
• Community Tutorials – User-shared examples and tips
• GitHub Repository – Explore the code and contribute

Let me know if you'd like to dive into specific topics like text classification or dependency parsing! 📈