Generative Python Transformers

Overview

This project focuses on building a generative model using the Transformer architecture in Python. It leverages pre-trained models to generate coherent and contextually relevant text based on input prompts.

Objectives

• Implement a generative model using the Transformer architecture.
• Explore various pre-trained models and their applications in text generation.
• Evaluate the model's performance and quality of generated text.

Methodology

1. Data Collection: Gather a dataset of text for training and fine-tuning.
2. Model Selection: Choose a suitable pre-trained Transformer model (e.g., GPT-2, BERT).
3. Fine-Tuning: Fine-tune the model on the collected dataset to adapt it for specific tasks.
4. Text Generation: Generate text based on input prompts and analyze the results.
5. Evaluation: Assess the quality of generated text using metrics such as BLEU score and human evaluation.

Conclusion

The project demonstrates the capabilities of generative Transformers in producing high-quality text. Further improvements can include exploring different architectures and training techniques.