RoBERTa Sentiment Analysis

Introduction

Welcome to our RoBERTa Sentiment Analysis project! In this repository, we explore the world of Natural Language Processing (NLP) by fine-tuning a RoBERTa Transformer for sentiment analysis.

Our Team

Our team, consisting of BOUSSOU Walid, HINDA Abdeljebar, ASTIGHFAR Ismail, KAIS Zakaria, and AYAD Mounir, excited to share our journey with you.

About RoBERTa

RoBERTa, short for "Robustly optimized BERT approach," is an advanced natural language processing model developed by Facebook AI Research. Built on the Transformer architecture, RoBERTa enhances BERT (Bidirectional Encoder Representations from Transformers) by addressing limitations and improving the pre-training procedure.

Project Structure

Application in Sentiment Analysis: Explore how we applied RoBERTa to sentiment analysis, specifically on a dataset of movie reviews.
Environment Setup: Learn about the libraries and tools we used, including PyTorch, Transformers, and the Hugging Face library.
Data Preparation: Details on loading and preparing the training data from a provided file.
Defining Variables: Key variables defined for training and validation.
Dataset and DataLoader: Creation of a custom dataset class and corresponding data loaders.
Neural Network Architecture: Introduction to the fine-tuning neural network based on RoBERTa.
Loss Function and Optimizer: Information on the loss function and optimizer used during training.
Training Function: Explanation of the training process and how the model learns from the data.
Validation Function: Insights into the validation stage to evaluate the model's performance on unseen data.

Prerequisites

Ensure you have Python installed (preferably Python 3.x).
Download the training data file train.tsv.zip from Kaggle and place it in the project directory.

Acknowledgments

We extend our gratitude to the open-source community, particularly the developers behind PyTorch, Transformers, and Hugging Face. Without their contributions, this project wouldn't be possible.

Feel free to explore the code, learn from our experiences, and adapt it for your own NLP projects! If you have any questions or suggestions, don't hesitate to reach out.

Happy coding! 🚀

License

This project operates under the MIT License. Refer to the LICENSE file for detailed information.

Feedback and Contributions

Your feedback and contributions are invaluable! Feel free to open issues, submit pull requests, or connect for discussions.

If you'd like to contribute to the project, please follow these steps:
1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.
If you encounter any issues or have suggestions, please open an issue on the Issues page.
Contact me in LinkedIn for questions.