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Rubert Base Cased Sentiment Med
R
Rubert Base Cased Sentiment Med
Developed by blanchefort
This model is a fine-tuned RuBERT pre-trained model on a medical review corpus, used to analyze the sentiment tendencies of medical reviews.
Downloads 290
Release Time : 3/2/2022
Model Overview
Fine-tuned based on the DeepPavlov/rubert-base-cased-conversational pre-trained model, specifically designed for sentiment classification tasks in the medical field, capable of identifying neutral, positive, and negative sentiments.
Model Features
Specialized for the medical field
Fine-tuned specifically for medical review data, excelling in sentiment analysis tasks within the medical domain.
Three-class sentiment analysis
Accurately identifies neutral, positive, and negative sentiment tendencies.
Based on a powerful pre-trained model
Fine-tuned from DeepPavlov's Russian BERT model, equipped with strong language understanding capabilities.
Model Capabilities
Russian text understanding
Sentiment classification
Medical text analysis
Use Cases
Medical feedback analysis
Medical institution evaluation analysis
Analyzes the sentiment tendencies of patient reviews about hospitals, clinics, and doctors.
Automatically classifies reviews as positive, neutral, or negative, helping medical institutions improve services.
Medical service quality monitoring
Monitors patient feedback in real-time to identify service issues.
Timely detection of negative reviews to enhance medical service quality.
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