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Cls Sentimento Sebrae

Developed by ggrazzioli
This is a sentiment classification model for internal datasets of Brazil's SEBRAE-RS institution, capable of classifying Portuguese texts into positive, neutral, or negative sentiments.
Text Classification
Transformers
 Other#Portuguese sentiment analysis#Enterprise service evaluation#High-precision classification
Downloads 26
Release Time : 10/20/2023

Model Overview

This model is specifically trained on internal data from Brazil's SEBRAE-RS institution for sentiment analysis tasks, supporting Portuguese text classification.

Model Features

High accuracy
Achieves an accuracy of 96.5% on the validation set, demonstrating excellent performance.
Multi-category classification
Supports classifying texts into three sentiment categories: positive, neutral, and negative.
Eco-friendly training
The training process emits only 0.6308 grams of CO2, emphasizing environmental sustainability.

Model Capabilities

Portuguese text understanding
Sentiment classification
Multi-category prediction

Use Cases

Customer feedback analysis
Service evaluation classification
Analyze customer service reviews and automatically classify them into positive, neutral, or negative feedback.
Accurately identifies customer sentiment tendencies to help improve service quality.
Social media monitoring
Public sentiment analysis
Monitor discussions about brands or products on social media and analyze public sentiment tendencies.
Detects negative public sentiment promptly, enabling quick responses.
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