fastino-ai/GLiNER2

Health v2 · maintenance only

Adoption not part of the score

1813 stars · 169 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GLiNER2 is a Python library for schema-based information extraction and text classification, supporting named entity recognition, structured data extraction, relation extraction, and span attributes in a single local model. It offers two extraction architectures (span and boundary) behind a unified AutoExtractor API and runs efficiently on CPU without external dependencies.

Use cases

• extract named entities from text
• classify text with custom labels
• extract structured records from documents
• detect PII in text locally
• extract entity relations into typed graphs
• run information extraction on CPU without GPU
• build training data for extraction models

When to choose

• you need schema-driven NER and classification in one model
• privacy requires fully local inference with no external calls
• you want fast CPU-based extraction without GPU infrastructure
• you need entities, relations, and classification in a single forward pass

When to avoid

• you need generative LLM-style free-form extraction beyond schema constraints
• you require state-of-the-art accuracy on complex domain-specific extraction that fine-tuned large models provide
• your stack is not Python 3.10+

Facets

library · maturity active

nlp machine-learning parser llm-inference machine-learning data-science privacy python cli cross-platform named-entity-recognition information-extraction text-classification relation-extraction schema-driven cpu-inference local-inference pii-detection transformers natural-language-processing

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