G

Gliner Small V2.1

Developed by urchade
GLiNER is a named entity recognition (NER) model capable of recognizing any entity type. It is implemented using a bidirectional Transformer encoder and is suitable for resource-constrained scenarios.
Downloads 4,585
Release Time : 4/9/2024

Model Overview

GLiNER is a general named entity recognition model that can recognize various entity types in text. It breaks through the limitations of traditional NER models on predefined entities and is more resource-friendly compared to large language models.

Model Features

General Entity Recognition
Capable of recognizing any entity type, breaking through the limitations of traditional NER models on predefined entities.
Resource-friendly
Has an advantage in resource-constrained scenarios compared to large language models.
Multilingual Support
Some versions support multilingual entity recognition.

Model Capabilities

Recognize named entities in text
Support custom entity types
Process complex context information

Use Cases

Information Extraction
Person Information Extraction
Recognize person names and related information from text
Successfully recognized Cristiano Ronaldo dos Santos Aveiro as a person
Event Information Extraction
Recognize date, award, and competition information from text
Successfully recognized 5 February 1985 as a date and Ballon d'Or as an award
Sports News Analysis
Team and Competition Recognition
Recognize team and competition information from sports news
Successfully recognized Al Nassr and Portugal national team as teams and UEFA Champions Leagues as a competition
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase