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Umlsbert ENG

Developed by GanjinZero
CODER is a knowledge-infused cross-language medical terminology embedding model focused on medical terminology standardization tasks.
Downloads 3,400
Release Time : 3/2/2022

Model Overview

CODER provides high-quality vector representations for cross-language medical terminology by combining knowledge graph embedding and contrastive learning techniques, supporting terminology standardization and semantic matching tasks.

Model Features

Knowledge infusion
The model integrates knowledge graph information to enhance medical terminology representation capability
Cross-language support
Capable of handling medical terminology standardization tasks in multiple languages
Contrastive learning
Uses contrastive learning techniques to improve the discriminative power of terminology representations

Model Capabilities

Medical terminology vector representation
Cross-language terminology matching
Terminology standardization
Semantic similarity calculation

Use Cases

Medical information processing
Electronic medical record terminology standardization
Mapping medical terms from different sources to standard terminology systems
Improves consistency and interoperability of medical data
Cross-language medical terminology alignment
Matching equivalent medical terms across different languages
Supports multilingual medical information retrieval and analysis
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