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Gliner Multi V2.1

Developed by urchade
GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any entity type, providing a practical alternative to traditional NER models.
Downloads 5,018
Release Time : 4/9/2024

Model Overview

GLiNER uses a bidirectional Transformer encoder (similar to BERT) to recognize any entity type, addressing the limitation of traditional NER models that are restricted to predefined entities while avoiding the high cost and large size of large language models (LLMs).

Model Features

General Entity Recognition
Capable of recognizing any entity type, not limited to predefined entities.
Lightweight
Smaller in size compared to large language models (LLMs), suitable for resource-constrained scenarios.
Multilingual Support
Supports entity recognition in multilingual texts.

Model Capabilities

Recognize any entity type
Multilingual text processing
Lightweight inference

Use Cases

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