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.
- 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.
- Data Collection: Gather a dataset of text for training and fine-tuning.
- Model Selection: Choose a suitable pre-trained Transformer model (e.g., GPT-2, BERT).
- Fine-Tuning: Fine-tune the model on the collected dataset to adapt it for specific tasks.
- Text Generation: Generate text based on input prompts and analyze the results.
- Evaluation: Assess the quality of generated text using metrics such as BLEU score and human evaluation.
The project demonstrates the capabilities of generative Transformers in producing high-quality text. Further improvements can include exploring different architectures and training techniques.