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Gliner Small V2.5

Developed by gliner-community
GLiNER is a general-purpose Named Entity Recognition (NER) model capable of identifying any entity type through a bidirectional Transformer encoder.
Downloads 2,252
Release Time : 6/17/2024

Model Overview

GLiNER provides a practical alternative to traditional NER models, which are limited to predefined entities, while large language models (LLMs), though flexible, are costly and bulky in resource-constrained scenarios.

Model Features

Flexible Entity Recognition
Capable of recognizing any user-defined entity type, not limited to predefined entities.
Efficient Performance
More efficient and compact compared to large language models in resource-constrained scenarios.
Multilingual Support
Supports Named Entity Recognition tasks in multiple languages.

Model Capabilities

Named Entity Recognition
Multilingual Text Processing
Custom Entity Type Recognition

Use Cases

Information Extraction
Person Information Extraction
Identify and extract person names and related information from text.
E.g., recognizing 'Cristiano Ronaldo dos Santos Aveiro' as a 'person' entity
Award Information Extraction
Identify and extract award names from text.
E.g., recognizing 'Ballon d'Or' as an 'award' entity
Date Information Extraction
Identify and extract date information from text.
E.g., recognizing '5 February 1985' as a 'date' entity
Sports News Analysis
Team Information Extraction
Identify and extract team names from sports news.
E.g., recognizing 'Al Nassr' and 'Portugal national team' as 'teams' entities
Match Information Extraction
Identify and extract competition names from sports news.
E.g., recognizing 'UEFA Champions Leagues' and 'UEFA European Championship' as 'competitions' entities
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