GLiNER Multi
| Property | Value |
|---|---|
| Parameters | 209M |
| License | cc-by-nc-4.0 |
| Paper | arXiv:2311.08526 |
| Language Support | Multilingual |
What is gliner_multi?
GLiNER Multi is a sophisticated Named Entity Recognition (NER) model that leverages bidirectional transformer architecture to identify custom entity types across multiple languages. Built on BERT-like architecture, it offers a practical solution for organizations needing flexible entity recognition without the computational demands of larger language models.
Implementation Details
The model employs a transformer-based architecture with 209M parameters, trained on the Pile-NER dataset. It's designed to be easily integrated through the GLiNER Python library, allowing for straightforward implementation in various NLP pipelines.
- Bidirectional transformer encoder architecture
- Custom entity type support
- Multilingual capability
- Efficient resource utilization compared to larger LLMs
Core Capabilities
- Flexible entity type recognition
- Support for multiple languages including English and Russian
- High performance on standard NER benchmarks
- Easy integration through Python API
- Efficient processing with moderate computational requirements
Frequently Asked Questions
Q: What makes this model unique?
GLiNER Multi combines the flexibility of custom entity recognition with multilingual support, offering a balance between the limitations of traditional NER models and the resource requirements of large language models.
Q: What are the recommended use cases?
The model is ideal for multilingual NER tasks where custom entity types need to be identified, such as information extraction from international documents, cross-language entity recognition, and specialized domain applications like medical or technical document processing.
