GLiNER2: Unified Schema-Based Information Extraction

GLiNER2 is an efficient, unified information extraction system that combines Named Entity Recognition (NER), Text Classification, Structured Data Extraction, and Relation Extraction into a single 205M-parameter model. Built on a fine-tuned transformer encoder, it provides CPU-based inference for local processing without requiring complex pipelines or external API dependencies, offering a powerful alternative to larger language models.