Welcome to the code repository for the Retrieval-Augmented Generation (RAG) model implementation. This repository contains the necessary code and documentation to get started with RAG, a powerful model that combines the benefits of dense retrieval and language generation.
The RAG model leverages a retriever-generator framework to enhance the quality of generated text. By retrieving relevant documents and using them as context, the RAG model can produce more informative and contextually relevant outputs.
To begin using the RAG model, please follow the link below to access the interactive Colab notebook:
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RAG
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ATLAS
- Detailed implementation of the RAG model.
- Interactive Colab notebook for easy experimentation.
- Step-by-step instructions for model training and evaluation.
Contributions to this repository are welcome. Please feel free to submit pull requests or create issues if you have suggestions or find any bugs.
This project is open-sourced under the Apache License 2.0. See the LICENSE file for details.
If you have any questions or comments about the RAG implementation, please open an issue in this repository.
Thank you for visiting the RAG code collection!