Open-Source Sentiment Analysis Tool - sentiment_analysis_model with Over 93% Accuracy Trained on IMDB Data

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Sentiment Analysis Model

Developed by tashrifmahmud

A sentiment analysis model fine-tuned on DistilBERT-base-uncased, trained on the IMDB dataset with an accuracy of 93.1%

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #IMDB Sentiment Analysis #High Accuracy (93.1%)#Fine-tuned DistilBERT

Downloads 25

Release Time : 11/24/2024

Model Overview

This model is designed for text sentiment analysis tasks, capable of determining the emotional tone (positive/negative) of text. Based on the lightweight DistilBERT architecture, it maintains high performance while reducing computational resource requirements.

Model Features

High Accuracy

Achieves 93.1% accuracy on the IMDB test set

Lightweight Architecture

Based on DistilBERT, smaller and faster than standard BERT models

Fast Inference

Suitable for real-time sentiment analysis applications

Model Capabilities

Text Sentiment Classification

Natural Language Processing

English Text Analysis

Use Cases

Content Analysis

Movie Review Sentiment Analysis

Analyze sentiment tendencies of movie reviews on platforms like IMDB

Accurately distinguishes between positive/negative reviews

Social Media Monitoring

Monitor user sentiment towards brands/products on social media

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2215	1.0	1563	0.2071	0.9213
0.1455	2.0	3126	0.2366	0.9310

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Sentiment Analysis Model

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 sentiment_analysis_model

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📚 Documentation

Model description

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Training and evaluation data

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