Open-source text classification model of finetuned sentiment analysis - Free deployment to implement sentiment analysis

Finetuned Sentiment Analysis Model

Developed by federicopascual

This model is a text classification model fine-tuned on the IMDb dataset based on distilbert-base-uncased for sentiment analysis tasks.

Text Classification

Transformers

Open Source License:Apache-2.0 #IMDb Sentiment Analysis #High Accuracy #Text Classification

Downloads 16

Release Time : 3/2/2022

Model Overview

The model is primarily used for text sentiment analysis, capable of classifying movie reviews and similar texts as positive or negative.

Model Features

High Accuracy

Achieves 90.9% accuracy on the IMDb evaluation set

Lightweight Architecture

Based on DistilBERT architecture, more lightweight and efficient than full BERT models

Fast Inference

Distilled model design enables faster inference speed

Model Capabilities

Text Classification

Sentiment Analysis

English Text Processing

Use Cases

Movie Review Analysis

Movie Review Sentiment Classification

Classify IMDb movie reviews as positive or negative sentiment

90.9% accuracy

Social Media Analysis

User Feedback Sentiment Analysis

Analyze sentiment tendencies of user feedback about products or services on social media

Property	Details
Model Type	Fine - tuned DistilBERT for sentiment analysis
Training Data	IMDb dataset
Metrics	Accuracy, Precision, Recall

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Finetuned Sentiment Analysis Model

Model Overview

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