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Indobert Base Uncased Finetuned Indonlu Smsa
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Indobert Base Uncased Finetuned Indonlu Smsa

Developed by ayameRushia
This model is a text classification model fine-tuned on the indonlu dataset based on indobert-base-uncased, specifically designed for Indonesian language sentiment analysis tasks.
Text Classification
Transformers
 OtherOpen Source License:MIT#Indonesian Sentiment Analysis#High Accuracy BERT#Short Text Classification
Downloads 31
Release Time : 3/2/2022

Model Overview

This is a BERT model for sentiment analysis of Indonesian text, excelling in the SMSA (Sentiment Analysis) task.

Model Features

High Accuracy
Achieves 93% accuracy on the evaluation set, demonstrating excellent performance
Specifically for Indonesian
Based on a BERT model pre-trained on Indonesian, offering better understanding of the local language
Fine-tuned
Underwent 10 epochs of fine-tuning on the indonlu dataset, optimizing sentiment analysis performance

Model Capabilities

Indonesian Text Classification
Sentiment Analysis
Short Text Understanding

Use Cases

Social Media Analysis
Product Review Sentiment Analysis
Analyze the sentiment tendencies of Indonesian user reviews on products
Accurately identifies positive, negative, and neutral reviews
Customer Feedback Classification
Automatically classify the sentiment tendencies of customer feedback
Helps businesses quickly understand customer satisfaction
Market Research
Brand Reputation Monitoring
Monitor changes in sentiment tendencies towards brands on social media
Provides real-time brand sentiment metrics
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