Welcome to the tutorials directory for the RAGEval project! This directory contains step-by-step guides and resources to help you get started with RAG evaluation methods, best practices, and practical implementations.
The RAG evaluation project aims to provide a comprehensive framework for evaluating the performance of retrieval-augmented models. This directory is dedicated to tutorials that will guide you through various aspects of RAG evaluation, helping you understand both the theory and practical applications.
To begin, ensure you have the necessary dependencies installed. Follow the installation instructions to set up your environment.
- Description: Learn how to perform a simple evaluation of RAG models using standard metrics.
- Link: Basic RAG Evaluation Tutorial
- Description: Explore advanced evaluation techniques that enhance your understanding of model performance.
- Link: Advanced Techniques Tutorial
- Description: A guide on how to evaluate RAG models on your custom datasets, including preprocessing steps and metric selection.
- Link: Custom Dataset Evaluation Tutorial
We welcome contributions! If you have ideas for additional tutorials or improvements to existing ones, please submit a pull request or open an issue. For more information, check our Contributing Guidelines.
This project is licensed under the Apache License. See the LICENSE file for details.
If you have any questions or need further assistance, feel free to open an issue in the main repository. Happy evaluating!