Finetuning Sentiment Model with 3000 Samples_1: Open-Source Sentiment Analysis Model with an Evaluation Accuracy of 85.67%

Finetuning Sentiment Model 3000 Samples 1

Developed by nayaksaroj

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving an accuracy of 85.67% on the evaluation set

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Release Time : 2/20/2025

Model Overview

This model is a fine-tuned DistilBERT model for sentiment analysis tasks, suitable for text sentiment classification.

Efficient and Lightweight

Based on the DistilBERT architecture, significantly reducing model size and computational requirements while maintaining performance

High Accuracy

Achieves 85.67% accuracy on the evaluation set, demonstrating excellent performance

Fast Training

Requires only 2 training epochs to achieve good performance

Text Sentiment Analysis

English Text Classification

Social Media Analysis

Comment Sentiment Analysis

Analyze the sentiment tendency of user comments

Accurately identifies over 85% of sentiment tendencies

Customer Service

Customer Feedback Classification

Automatically classify customer feedback as positive or negative

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