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Distilbert Imdb
A text classification model fine-tuned on the IMDB movie review dataset based on DistilBERT-base-uncased for sentiment analysis tasks
Downloads 14.18k
Release Time : 3/2/2022
Model Overview
This model is a lightweight version of DistilBERT, specifically fine-tuned for the IMDB movie review dataset for binary sentiment analysis (positive/negative reviews). Achieves 92.8% accuracy on the evaluation set.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller and 60% faster than the standard BERT model while retaining 97% of its performance.
High Accuracy
Achieves 92.8% classification accuracy on the IMDB test set.
Easy to Use
Quick deployment and usage via the Hugging Face Transformers library.
Model Capabilities
Text Classification
Sentiment Analysis
English Text Processing
Use Cases
Sentiment Analysis
Movie Review Sentiment Classification
Automatically determines the sentiment tendency of movie reviews (positive/negative).
92.8% accuracy
Product Review Analysis
Analyzes the sentiment tendency of user reviews on e-commerce platforms.
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