Biomedical Ner All
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Biomedical Ner All
Developed by d4data
An English named entity recognition model trained on distilbert-base-uncased, specifically designed for identifying biomedical entities (107 entity categories), suitable for text corpora such as case reports.
Downloads 112.41k
Release Time : 6/19/2022
Model Overview
This model can identify 107 different types of entities from biomedical texts and is particularly suitable for processing specialized medical texts such as case reports.
Model Features
Comprehensive Biomedical Entity Recognition
Capable of identifying 107 different types of biomedical entities
Efficient Model Architecture
Based on distilbert-base-uncased, improving efficiency while maintaining performance
Specialized for Medical Texts
Specially optimized for processing specialized medical texts such as case reports
Model Capabilities
Biomedical Entity Recognition
Case Report Analysis
Medical Text Processing
Use Cases
Medical Research
Case Analysis
Extract key medical entity information from case reports
Automatically identifies key information such as symptoms, diagnoses, and treatments
Clinical Decision Support
Patient Information Extraction
Extract key medical information from patient records
Helps doctors quickly understand patient history and symptoms
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