GLiNER-base
| Property | Value |
|---|---|
| Parameters | 209M |
| Language | English |
| License | cc-by-nc-4.0 |
| Paper | arXiv:2311.08526 |
What is gliner_base?
GLiNER-base is a sophisticated Named Entity Recognition (NER) model that bridges the gap between traditional NER systems and large language models. Built on a bidirectional transformer architecture, it offers the unique ability to identify custom entity types without being constrained to predefined categories, while maintaining a relatively lightweight footprint of 209M parameters.
Implementation Details
The model implements a BERT-like architecture optimized for token classification tasks. It's trained on the Universal-NER/Pile-NER-type dataset and can be easily integrated using the GLiNER Python library. The implementation supports dynamic entity type specification during inference, making it highly versatile for various NER applications.
- Flexible entity type recognition with custom label support
- PyTorch-based implementation with efficient inference
- Trained on comprehensive NER datasets
- Simple API with predict_entities method
Core Capabilities
- Custom entity type recognition without retraining
- Support for multiple entity types in a single pass
- Efficient processing with lower resource requirements than LLMs
- High accuracy in entity detection and classification
Frequently Asked Questions
Q: What makes this model unique?
GLiNER-base's ability to recognize any entity type without retraining, combined with its moderate size of 209M parameters, makes it a practical solution for both specific and general NER tasks. It offers the flexibility of larger language models while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is ideal for applications requiring custom entity recognition, such as information extraction from documents, content analysis, and automated data processing. It's particularly suitable for scenarios where predefined entity types are limiting or where using large language models would be computationally prohibitive.
