G

Gliner Base

Developed by urchade
GLiNER is a general-purpose Named Entity Recognition (NER) model capable of identifying any type of entity through a bidirectional Transformer encoder, providing a practical alternative to traditional NER models.
Downloads 4,921
Release Time : 2/16/2024

Model Overview

GLiNER is a Named Entity Recognition model based on a bidirectional Transformer encoder, capable of identifying various types of entities in text. It is more flexible than traditional NER models and more lightweight and efficient compared to large language models.

Model Features

Flexible Entity Recognition
Can identify any user-defined entity type without being limited by predefined entity types
Lightweight and Efficient
Smaller in size and consumes fewer resources compared to large language models (LLMs)
Multilingual Support
Offers a multilingual version (gliner_multi) supporting entity recognition in multiple languages

Model Capabilities

Named Entity Recognition
Custom Entity Type Recognition
Text Analysis

Use Cases

Information Extraction
Person Information Extraction
Identify names and related information of people from text
Successfully identified football player Cristiano Ronaldo in the example
Event Information Extraction
Identify date, event, and other related information in text
Correctly identified dates such as February 5, 1985 in the example
Knowledge Management
Knowledge Graph Construction
Extract structured entity information from unstructured text
Can be used to build entity nodes for knowledge graphs
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