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Distilbert Sst2
D
Distilbert Sst2
Developed by mattchurgin
A text classification model fine-tuned on the GLUE dataset based on distilbert-base-uncased
Downloads 15
Release Time : 3/2/2022
Model Overview
This is a lightweight Transformer model optimized for SST-2 sentiment analysis tasks, suitable for sentiment classification of English texts.
Model Features
Lightweight and Efficient
Based on the DistilBERT architecture, it is 40% smaller and 60% faster than standard BERT models while maintaining comparable performance.
High Accuracy
Achieves 89.11% accuracy on the SST-2 evaluation set.
Fast Inference
Evaluation speed reaches 483 samples per second, suitable for real-time applications.
Model Capabilities
English Text Classification
Sentiment Analysis
Sentence-level Feature Extraction
Use Cases
Social Media Analysis
Comment Sentiment Analysis
Analyze the sentiment tendency (positive/negative) of user comments
89.11% accuracy
Customer Service
Customer Feedback Classification
Automatically classify the sentiment tendency of customer feedback
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