Sentiment Analysis with distilbert-base-uncased Open-source Model - Achieving High-accuracy Text Sentiment Analysis

Sentiment Analysis With Distilbert Base Uncased

Developed by sherif-911

This is a sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 93.2% accuracy on the evaluation set.

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Release Time : 4/12/2025

Model Overview

This model is used for text sentiment analysis tasks, capable of determining the emotional tendency of text (e.g., positive/negative).

High Accuracy

Achieves 93.2% accuracy on the evaluation set

Lightweight Model

Based on the DistilBERT architecture, more lightweight and efficient than standard BERT

Fast Inference

Distilled model design provides faster inference speed

Text sentiment analysis

English text processing

Social Media Analysis

Comment Sentiment Analysis

Analyze the sentiment tendency of social media comments

Accurately identifies 93.2% of sentiment tendencies

Customer Feedback Analysis

Product Review Analysis

Automatically analyze customer sentiment towards products

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2219	1.0	1563	0.2076	0.9194
0.146	2.0	3126	0.2310	0.932

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