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--- |
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library_name: gliner2 |
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--- |
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## Model Description |
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GLiNER2 extends the original GLiNER architecture to support multi-task information extraction with a schema-driven interface. This base model provides efficient CPU-based inference while maintaining high accuracy across diverse extraction tasks. |
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**Key Features:** |
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- Multi-task capability: NER, classification, and structured extraction |
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- Schema-driven interface with field types and constraints |
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- CPU-first design for fast inference without GPU requirements |
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- 100% local processing with zero external dependencies |
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## Installation |
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```bash |
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pip install gliner2 |
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``` |
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## Usage |
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### Entity Extraction |
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```python |
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from gliner2 import GLiNER2 |
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# Load the model |
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extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1") |
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# Extract entities |
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text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." |
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result = extractor.extract_entities(text, ["company", "person", "product", "location"]) |
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print(result) |
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# Output: {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}} |
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``` |
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### Text Classification |
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```python |
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# Single-label classification |
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result = extractor.classify_text( |
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"This laptop has amazing performance but terrible battery life!", |
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{"sentiment": ["positive", "negative", "neutral"]} |
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) |
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print(result) |
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# Output: {'sentiment': 'negative'} |
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# Multi-label classification |
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result = extractor.classify_text( |
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"Great camera quality, decent performance, but poor battery life.", |
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{ |
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"aspects": { |
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"labels": ["camera", "performance", "battery", "display", "price"], |
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"multi_label": True, |
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"cls_threshold": 0.4 |
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} |
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} |
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) |
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print(result) |
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# Output: {'aspects': ['camera', 'performance', 'battery']} |
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``` |
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### Structured Data Extraction |
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```python |
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text = "iPhone 15 Pro Max with 256GB storage, A17 Pro chip, priced at $1199." |
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result = extractor.extract_json( |
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text, |
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{ |
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"product": [ |
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"name::str::Full product name and model", |
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"storage::str::Storage capacity", |
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"processor::str::Chip or processor information", |
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"price::str::Product price with currency" |
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] |
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} |
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) |
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print(result) |
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# Output: { |
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# 'product': [{ |
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# 'name': 'iPhone 15 Pro Max', |
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# 'storage': '256GB', |
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# 'processor': 'A17 Pro chip', |
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# 'price': '$1199' |
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# }] |
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# } |
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``` |
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### Multi-Task Schema Composition |
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```python |
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# Combine all extraction types |
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schema = (extractor.create_schema() |
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.entities({ |
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"person": "Names of people or individuals", |
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"company": "Organization or business names", |
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"product": "Products or services mentioned" |
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}) |
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.classification("sentiment", ["positive", "negative", "neutral"]) |
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.structure("product_info") |
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.field("name", dtype="str") |
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.field("price", dtype="str") |
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.field("features", dtype="list") |
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) |
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text = "Apple CEO Tim Cook unveiled the iPhone 15 Pro for $999." |
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results = extractor.extract(text, schema) |
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print(results) |
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# Output: { |
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# 'entities': {'person': ['Tim Cook'], 'company': ['Apple'], 'product': ['iPhone 15 Pro']}, |
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# 'sentiment': 'positive', |
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# 'product_info': [{'name': 'iPhone 15 Pro', 'price': '$999', 'features': [...]}] |
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# } |
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``` |
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## Model Details |
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- **Model Type:** Bidirectional Transformer Encoder (BERT-based) |
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- **Parameters:** 205M |
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- **Input:** Text sequences |
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- **Output:** Entities, classifications, and structured data |
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- **Architecture:** Based on GLiNER with multi-task extensions |
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- **Training Data:** Multi-domain datasets for NER, classification, and structured extraction |
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## Performance |
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This model is optimized for: |
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- Fast CPU inference (no GPU required) |
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- Low latency applications |
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- Resource-constrained environments |
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- Multi-task extraction scenarios |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{zaratiana2025gliner2efficientmultitaskinformation, |
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title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface}, |
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author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis}, |
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year={2025}, |
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eprint={2507.18546}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2507.18546}, |
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} |
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``` |
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## License |
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This project is licensed under the Apache License 2.0. |
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## Links |
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- **Repository:** https://github.com/fastino-ai/GLiNER2 |
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- **Paper:** https://arxiv.org/abs/2507.18546 |
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- **Organization:** [Fastino AI](https://fastino.ai) |
