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Gliner Medium V2.5

Developed by gliner-community
GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models while addressing the high resource consumption issues of large language models.
Downloads 678
Release Time : 6/17/2024

Model Overview

GLiNER uses a bidirectional Transformer encoder (similar to BERT) to recognize any type of entity, making it suitable for resource-constrained scenarios.

Model Features

General Entity Recognition
Capable of identifying any type of entity, not limited to predefined entity types.
Resource Efficiency
Compared to large language models, GLiNER is more resource-efficient, making it suitable for resource-constrained scenarios.
Multilingual Support
Supports named entity recognition in multiple languages.

Model Capabilities

Named Entity Recognition
Multilingual Entity Recognition
Custom Entity Type Recognition

Use Cases

Information Extraction
Person Identification
Identify person names from text.
Cristiano Ronaldo dos Santos Aveiro => Person
Date Identification
Identify date information from text.
February 5, 1985 => Date
Award Identification
Identify award names from text.
Ballon d'Or => Award
Sports Analysis
Team Identification
Identify team names from sports news.
Al-Nassr FC => Team
Event Identification
Identify event names from sports news.
UEFA Champions League => Event
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