GLiNER2: Unified Schema Based Information Extraction and Text Classification 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. Fine tune via Pioneer. Additional documentation via Pioneer docs. Join discussions on Discord and Reddit. ✨ Why GLiNER2? 🎯 One Model, Four Tasks : Entities, classification, structured data, and relations in a single forward pass 💻 CPU First : Lightning fast inference on standard hardware—no GPU required 🛡️ Privacy : 100% local processing, zero external dependencies Installation Usage Entity Extraction Text Classification Structured Data Extraction Multi Task Schema Composition Model Details Model Type: Bidirectional Transformer Encoder (BERT based) Parameters: 340M Input: Text sequences Output: Entities, classifications, and structured data Architecture: Based on GLiNER with multi task extensions (large variant) Training Data: Multi domain da…
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