GLiNER2: Unified Schema-Based Information Extraction

GLiNER2: Unified Schema-Based Information Extraction

Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.

GLiNER2 unifies Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies.

✨ Why GLiNER2?

🎯 One Model, Four Tasks : Entities, classification, structured data, and relations in a single forward pass

: Entities, classification, structured data, and relations in a single forward pass 💻 CPU First : Lightning-fast inference on standard hardware—no GPU required

: Lightning-fast inference on standard hardware—no GPU required 🛡️ Privacy: 100% local processing, zero external dependencies

🚀 Installation & Quick Start

pip install gliner2

from gliner2 import GLiNER2 # Load model once, use everywhere extractor = GLiNER2 . from_pretrained ( "fastino/gliner2-base-v1" ) # Extract entities in one line text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor . extract_entities ( text , [ "company" , "person" , "product" , "location" ]) print ( result ) # {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}}

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