Distilbert Sentiment Analysis

🚀 DistilBERT for Sentiment Analysis

A fine - tuned DistilBERT model for sentiment analysis on social media texts.

🚀 Quick Start

This model is designed to perform sentiment analysis, especially on social media texts. It classifies text into non - negative and negative sentiments.

✨ Features

• Fine - tuned: Based on distilbert-base-uncased, fine - tuned on a social media dataset.
• Efficient Architecture: Utilizes the DistilBertForSequenceClassification architecture, which is computationally efficient while maintaining comparable performance.
• Multilingual Applicability: Suitable for analyzing texts from various sources and demographics.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model Description

Overview

This model is a fine - tuned version of distilbert-base-uncased on a social media dataset for the purpose of sentiment analysis. It can classify text into non - negative and negative sentiments.

Intended Use

This model is intended for sentiment analysis tasks, particularly for analyzing social media texts.

Model Architecture

This model is based on the DistilBertForSequenceClassification architecture, a distilled version of BERT that maintains comparable performance on downstream tasks while being more computationally efficient.

Training

Training Data

The model was trained on a dataset consisting of social media posts, surveys and interviews, labeled for sentiment (non - negative and negative). The dataset includes texts from a variety of sources and demographics.

Training Procedure

The model was trained using the following parameters:

• Optimizer: AdamW
• Learning Rate: 5e - 5
• Batch Size: 32
• Epochs: 30

Training was conducted on Kaggle, utilizing two GPUs for accelerated training.

📄 License

This project is licensed under the MIT License.