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Epiclassify4gard

Developed by ncats
This model is a fine-tuned text classification model based on BioBERT, excelling in medical text classification tasks.
Text Classification
Transformers
#Biomedical Text Classification#High Accuracy#BioBERT Fine-tuning
Downloads 27
Release Time : 3/2/2022

Model Overview

A text classification model fine-tuned from dmis-lab/biobert-base-cased-v1.2, specifically optimized for medical text classification tasks, achieving high accuracy on the epi_classify4_gard dataset.

Model Features

High Accuracy
Achieves 98.6% accuracy on the evaluation set, demonstrating excellent performance.
Medical Domain Optimization
Based on the BioBERT architecture, it is particularly suitable for processing medical domain texts.
Balanced Performance
Exhibits balanced precision (0.875) and recall (0.903), with an F1 score of 0.889.

Model Capabilities

Medical Text Classification
Multi-class Text Classification

Use Cases

Medical Research
Medical Literature Classification
Automatically classify medical research literature
High accuracy (98.6%) classification results
Epidemiological Research
Classify texts related to epidemiology
Excellent performance on the epi_classify4_gard dataset
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