elsayedelmandoh/Ubuntu-Automated-Customer-Service

Developed a Transformer-based model and fine-tuned GPT-2 for building an automated customer service chatbot using the Ubuntu Dialogue Corpus. The Transformer notebook covers data preprocessing, model architecture design, and training, while the GPT-2 notebook focuses on data preparation, fine-tuning, and saving the enhanced model.

Ubuntu Automated Customer Service

Table of Contents

1. Introduction
2. Data Acquisition and Preprocessing
3. Model Development
4. Training, Validation, and Testing
5. Chatbot Integration
6. Evaluation and Testing
7. Iteration and Improvement
8. Fine-Tuning GPT-2
9. Contributing
10. Author

Introduction

This repository contains an implementation of a Transformer-based model and fine-tuned GPT-2 for an automated customer service chatbot using the Ubuntu Dialogue Corpus. The Transformer notebook includes data preprocessing with NLTK, model architecture design, and training using TensorFlow, while the GPT-2 notebook emphasizes data preparation, fine-tuning, and saving the enhanced model using Transformers for improved conversational performance.

Data Acquisition and Preprocessing

• Load and preprocess data: Load the Ubuntu Dialogue Corpus and clean the text.
• Clean and tokenize text: Apply text preprocessing techniques and tokenize the text.
• Prepare input and target sequences for model training: Format the data for training the model.

Model Development

• Define Transformer model architecture: Include Encoder and Decoder components.
• Implement attention mechanisms and other components: Ensure the model utilizes effective attention mechanisms.

Training, Validation, and Testing

• Train the model: Train the Transformer model on the preprocessed data.
• Validate and test the model: Evaluate the model's performance using validation and test sets.

Chatbot Integration

• Integrate the trained model into a chatbot application: Build a functional chatbot using the trained model.

Evaluation and Testing

• Evaluate the chatbot's performance: Assess how well the chatbot performs on test data.
• Refine and improve the model: Use feedback to enhance the model's accuracy and functionality.

Iteration and Improvement

• Ongoing improvements: Continuously update and improve the chatbot's performance and features.

Fine-Tuning GPT-2

This section describes how to fine-tune the GPT-2 model on the Ubuntu Dialogue Corpus.

Fine-Tuning Notebook

The fine-tuning notebook includes the following steps:

1. Setup and Installation: Install necessary libraries (transformers, datasets).
2. Load and Preprocess Data: Load the dataset and apply preprocessing steps such as cleaning and tokenization.
3. Prepare Your Dataset: Convert the dataset into the format required for training GPT-2.
4. Fine-Tune: Train GPT-2 on the preprocessed data.
5. Save the Fine-Tuned Model: Save the model and tokenizer after fine-tuning.

Contributing

Contributions are welcome! If you have suggestions, improvements, or additional content to contribute, feel free to open issues, submit pull requests, or provide feedback.

Author

This repository is maintained by Elsayed Elmandoh, an AI Engineer. You can connect with Elsayed on LinkedIn and Twitter/X for updates and discussions related to Machine learning, deep learning and NLP.

Happy coding!