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Gliner Small V1

Developed by urchade
GLiNER is a general-purpose named entity recognition model capable of identifying any entity type, providing a lightweight alternative to traditional NER models and large language models.
Downloads 629
Release Time : 3/9/2024

Model Overview

GLiNER uses a bidirectional Transformer encoder (similar to BERT) for named entity recognition, supports dynamically defined entity types, and is suitable for resource-constrained scenarios.

Model Features

Dynamic Entity Recognition
Can recognize any user-defined entity type, not limited to predefined entity sets
Lightweight Design
Smaller in size and consumes fewer resources compared to large language models
Flexible Application
Supports instant recognition of multiple entity types without retraining the model

Model Capabilities

Text Entity Recognition
Multi-category Entity Labeling
Custom Entity Type Recognition

Use Cases

Information Extraction
News Person Identification
Identify entities such as people, organizations, and locations from news text
Accurately identifies various named entities in the text
Sports Event Analysis
Extract information about teams, players, and events from sports news
Successfully identified football players, teams, and awards in the example
Knowledge Graph Construction
Entity Relation Extraction
Serves as a preliminary processing step for knowledge graph construction
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