GliNER2: Extracting Structured Information from Text

GliNER2 is a lightweight, CPU-efficient NLP framework that unifies named entity recognition, text classification, and relation extraction. It introduces a schema-driven approach, allowing users to define extraction requirements and execute multiple tasks in a single inference call. The tool is presented as an alternative to larger models like LLMs for classic NLP tasks such as building knowledge graphs. The article demonstrates GliNER2's capabilities by extracting entities, relations, and structured JSON from a text about Ada Lovelace. This highlights the model's ability to transform unstructured text into clean, structured data without the overhead of a large language model.