CGRE CNDBPedia Generative Relation Extraction
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CGRE CNDBPedia Generative Relation Extraction
Developed by fanxiao
CGRE is a Chinese end-to-end relation extraction model based on BART, employing a generative approach to achieve entity relation extraction, demonstrating excellent performance on multiple Chinese relation extraction datasets.
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Release Time : 6/17/2022
Model Overview
CGRE is a generative entity relation extraction model based on BART as the backbone network, capable of directly generating linearized triplet relations from sentences.
Model Features
Generative Relation Extraction
Uses a generative approach to directly output triplets, simplifying the traditional relation extraction process.
Chinese Optimization
Specifically optimized for Chinese text, achieving excellent performance on multiple Chinese relation extraction datasets.
End-to-End Model
No complex preprocessing or postprocessing required, enabling end-to-end relation extraction.
Distant Supervision Training
Trained using distantly supervised data from Chinese encyclopedias to enhance model generalization.
Model Capabilities
Entity Relation Extraction
Chinese Text Understanding
Triplet Generation
Use Cases
Information Extraction
Encyclopedia Knowledge Extraction
Extract entity relations from encyclopedia-like texts
Achieves industry-leading performance on datasets like DuIE
Business Intelligence Analysis
Extract company-product relations from business news
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