Text Classification

Overview

This repository contains a model fine-tuned on a pre-trained BERT (Bidirectional Encoder Representations from Transformers) architecture for the task of emotion classification. The model is designed to categorize text into predefined emotion labels, making it useful for applications such as sentiment analysis, chatbots, customer feedback systems, and more.

Features

• Pre-trained BERT Model: Built on top of the BERT architecture, leveraging its ability to understand context in natural language.
• Emotion Classification: Fine-tuned to classify text into multiple emotion categories (e.g., happiness, sadness, anger, etc.).
• Custom Dataset: The model was trained on a custom dataset that includes labeled emotional content from various sources.
• Text Input: Accepts raw text input and outputs the predicted emotion category.

Getting Started

Prerequisites

All prerequisites in requirements.txt file

Installation

Follow the steps below to set up the project on your local machine:

1. Clone the repository:

   git clone https://github.com/MohamedSameh410/Text-Classification.git
   cd Text-Classification

2. Install dependencies:

   pip install -r requirements.txt

3. Run the command-line tool to use the model

   • If you want to use the model through the UI:

     cd BERT_emotion_model
     python app.py

   • If you want to use the model through the cmd:

     cd BERT_emotion_model
     python test_model.py

Emotion Labels

The model can classify text into the following emotion categories:

• Joy
• Sadness
• Anger
• Surprise
• Fear
• Love

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue if you find any bugs or have suggestions for improvements.