Hwtcmner
A BERT-fine-tuned named entity recognition model specifically designed for the TCM field, achieving leading performance in NER tasks within this domain.
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Release Time : 6/7/2024
Model Overview
TCMNER is a BERT-fine-tuned named entity recognition model specifically designed for the TCM field, capable of identifying six types of entities: prescriptions, herbs, sources, disease names, symptoms, and syndromes.
Model Features
Specialized for TCM Field
Specifically designed for the TCM field, achieving leading performance in NER tasks within this domain.
Six-category Entity Recognition
Capable of identifying six types of entities: prescriptions, herbs, sources, disease names, symptoms, and syndromes.
Optimized Based on RoBERTa
TCMRoBERTa, a TCM-specific model optimized based on the RoBERTa architecture.
Model Capabilities
Named entity recognition in TCM texts
Prescription recognition
Herb recognition
Source recognition
Disease name recognition
Symptom recognition
Syndrome recognition
Use Cases
TCM Text Processing
TCM Literature Analysis
Used to analyze key entities in TCM literature, such as prescriptions and herbs.
Accurately identifies six types of entities in the literature.
TCM Knowledge Graph Construction
Used to construct TCM knowledge graphs by extracting key entity information.
Provides high-quality entity recognition results to support knowledge graph construction.
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