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Finetuning Sentiment Ditilbert
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Finetuning Sentiment Ditilbert

Developed by Neo111x
A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Text Classification
Downloads 15
Release Time : 6/26/2024

Model Overview

This model is a lightweight BERT variant fine-tuned for sentiment analysis tasks, suitable for text sentiment classification

Model Features

Efficient and Lightweight
Based on DistilBERT architecture, 40% smaller than standard BERT while retaining 97% of its performance
High Accuracy
Achieves 87.67% accuracy and 0.8795 F1 score on the evaluation set
Fast Training
Requires only 2 training epochs to achieve good results

Model Capabilities

Text Sentiment Classification
English Text Analysis

Use Cases

Social Media Analysis
Comment Sentiment Analysis
Analyze sentiment tendencies (positive/negative) in user comments
87.67% accuracy
Customer Feedback Analysis
Customer Service Dialogue Evaluation
Automatically classify sentiment tendencies in customer feedback
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