GLiNER2: Unified AI Model for Information Extraction and Text Classification
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apwheele
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Summary
GLiNER2 is a unified AI model for information extraction that combines Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction into a single 205M parameter model. It offers efficient CPU-based inference without requiring complex pipelines or external API dependencies, and supports quantization and compilation for faster performance. The model is available as an open-source project on GitHub.
Key quotes
· 4 pulledGLiNER2 unifies Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction into a single 205M parameter model.
Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.
It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies.
Enable fp16 and/or torch.compile for faster inference — no extra dependencies.
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