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Gliner Large V2.1

Developed by urchade
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 and large language models.
Downloads 10.31k
Release Time : 4/10/2024

Model Overview

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

Model Features

General Entity Recognition
Capable of identifying any type of entity, not limited to predefined entity types.
Multilingual Support
Supports named entity recognition in multiple languages.
Resource Efficiency
Smaller in size and lower in cost compared to large language models, suitable for resource-constrained scenarios.

Model Capabilities

Named Entity Recognition
Multilingual Text Processing

Use Cases

Information Extraction
Entity Recognition in News Articles
Extract entities such as people, locations, and organizations from news articles.
Accurately identifies various types of entities, such as people, dates, awards, etc.
Entity Recognition in Academic Literature
Extract specialized terms, authors, and institutions from academic literature.
Supports multiple languages, applicable to academic literature in different languages.
Business Intelligence
Customer Feedback Analysis
Extract key entities such as product names and issue types from customer feedback.
Helps businesses quickly understand key information in customer feedback.
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