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Distilbert Base Uncased Sentiment Sst2
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Distilbert Base Uncased Sentiment Sst2
Developed by bhadresh-savani
A lightweight sentiment analysis model based on the DistilBERT architecture, fine-tuned specifically for the SST2 dataset, capable of identifying positive or negative emotions in text.
Downloads 16
Release Time : 3/2/2022
Model Overview
This model is a variant of DistilBERT, specifically designed for sentiment analysis tasks, efficiently determining the emotional tendency (positive/negative) of input text.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller in size than the original BERT model while retaining 97% of its performance.
High Accuracy
Achieves 90.94% accuracy on the SST2 test set.
Fast Inference
Evaluation speed reaches 242 samples per second, suitable for real-time applications.
Model Capabilities
Text Sentiment Analysis
Binary Sentiment Classification
English Text Processing
Use Cases
Social Media Analysis
Comment Sentiment Analysis
Automatically analyzes the sentiment tendency of user comments.
Accurately identifies over 90% of comment sentiments.
Customer Service
Customer Feedback Classification
Automatically classifies customer feedback as positive or negative.
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