spaCy

spaCy is "Industrial Strength" NLP. Unlike NLTK (academic), spaCy focuses on providing the best single algorithm for a task. v3.8 supports Python 3.13.

When to Use

• Production-Ready Natural Language Processing: Fast, industrial-grade tokenization, lemmatization, and dependency parsing.
• Named Entity Recognition (NER): Extracting organizations, people, locations, dates, and domain-specific entities.
• Rule-Based Matching & Pattern Extraction: Combining linguistic rules with regex and token attributes using Matcher and EntityRuler.
• Large-Scale Text Processing Pipelines: Batch processing millions of documents with nlp.pipe() and GPU acceleration.

Quick Start

import spacy

# Load transformer or core English pipeline
nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is acquiring a London startup for $1 billion.")