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Sentiment Hts5 Xlm Roberta Hungarian
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Sentiment Hts5 Xlm Roberta Hungarian
Developed by NYTK
This is a Hungarian sentence-level sentiment analysis model fine-tuned based on the XLM-RoBERTa pre-trained model, supporting 5 sentiment classifications.
Downloads 2,920
Release Time : 3/2/2022
Model Overview
This model is specifically designed for sentiment analysis of Hungarian texts, capable of classifying input texts into five sentiment categories: extremely negative, negative, neutral, positive, or extremely positive.
Model Features
Multi-level sentiment classification
Supports 5 sentiment level classifications (0-4), more detailed than traditional positive/negative binary classification.
Hungarian language optimization
Specifically fine-tuned for Hungarian, performing excellently on the Hungarian Twitter Sentiment (HTS) corpus.
Cross-lingual pre-training foundation
Based on the XLM-RoBERTa pre-trained model, equipped with strong cross-lingual understanding capabilities.
Model Capabilities
Hungarian text sentiment analysis
Multi-level sentiment classification (0-4)
Short text sentiment recognition
Use Cases
Social media analysis
Twitter sentiment monitoring
Analyze the sentiment tendencies of Hungarian Twitter content.
Accurately identifies user emotional states.
Customer feedback analysis
Product review sentiment analysis
Automatically classify the sentiment tendencies of Hungarian product reviews.
Helps businesses understand customer satisfaction.
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