Sentiment Analysis with Transformer

This project implements a sentiment analysis model using a custom Transformer architecture. The model predicts whether a movie review is positive or negative based on the IMDB dataset.

🚀 Features

• Custom Transformer Model: Built from scratch using TensorFlow and Keras.
• IMDB Dataset: Preprocessed and padded for consistency.
• Visualization: Includes loss and accuracy plots for training and validation.

📂 Project Structure

.
├── main.py            # Main script with the Transformer model
├── requirements.txt   # Dependencies for the project
├── .gitignore         # Ignored files for Git
└── README.md          # Project documentation

🛠️ Requirements

Install the dependencies using:

pip install -r requirements.txt

🧪 Usage

1. Run the main script:

   python main.py

2. Enter a movie review when prompted, and the model will predict its sentiment.

📊 Visualization

Training and validation metrics are plotted after training:

Loss vs Epochs

Accuracy vs Epochs

✍️ Example Output

Enter a film review: This movie was amazing! I loved it.
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 63ms/step
Prediction result is positive -> score: 0.9998332262039185

📈 Model Summary

The model uses a 4-layer Transformer with multi-head attention and feedforward layers, optimized for binary classification.

📋 Dataset

The project uses the IMDB dataset, which is loaded via TensorFlow's keras.datasets.imdb.

🤝 Contributing

Feel free to fork the repository, submit pull requests, or open issues to contribute to this project.

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