Introduction to Evals

The Evals project, hosted on GitHub, is designed to improve the evaluation of outputs generated by AI models, particularly those based on generative language models. With a focus on postprocessing, Evals ensures that the outputs are not only correct in essence but also formatted appropriately for evaluation. This blog post will delve into the core features, technical architecture, installation process, and community aspects of the Evals project.

What are Postprocessors?

Postprocessors serve as an essential output-tidying step for solvers. Many AI models may produce answers that are technically correct but not in the expected format. For instance, a multiple-choice answer might be expected as `A`, `B`, or `C`, but a language model might output something like "B.". This discrepancy can lead to false negatives in evaluations. Postprocessors help clean up such outputs, making them suitable for accurate evaluation.

Main Features of Evals

• Output Formatting: Ensures that outputs from AI models are formatted correctly for evaluation.
• Customizability: Users can create their own postprocessors by subclassing the PostProcessor class.
• Multiple Postprocessors: Supports a variety of built-in postprocessors to handle common output formatting issues.
• Community Contributions: Encourages contributions from developers to enhance the functionality of the project.

Technical Architecture and Implementation

The Evals project consists of 1766 files and 75121 lines of code, indicating a robust and well-structured codebase. The architecture is designed to facilitate easy integration of postprocessors into various solver classes. The postprocessors are applied in a specific order, which is crucial for achieving the desired output format.

For example, in the defaults.yaml configuration file, you can see how postprocessors are defined:

generation/direct/gpt-3.5-turbo:
  class: evals.solvers.providers.openai.openai_solver:OpenAISolver
  args:
    completion_fn_options:
      model: gpt-3.5-turbo-0125
      extra_options:
        temperature: 1
        max_tokens: 512
    postprocessors: &postprocessors
      - evals.solvers.postprocessors.postprocessors:Strip
      - evals.solvers.postprocessors.postprocessors:RemoveQuotes
      - evals.solvers.postprocessors.postprocessors:RemovePeriod

Setup and Installation Process

To get started with Evals, follow these simple steps:

1. Clone the repository from GitHub: git clone https://github.com/openai/evals.git
2. Navigate to the project directory: cd evals
3. Install the required dependencies using pip install -r requirements.txt.
4. Configure your solvers and postprocessors in the defaults.yaml file.

For detailed instructions, refer to the official documentation.

Usage Examples and API Overview

Once installed, you can utilize Evals in your projects by importing the necessary classes and configuring your solvers. Here’s a brief example:

from evals.solvers.providers.openai import OpenAISolver

solver = OpenAISolver(
    completion_fn_options={
        'model': 'gpt-3.5-turbo',
        'temperature': 0.7,
        'max_tokens': 150
    },
    postprocessors=[
        'evals.solvers.postprocessors.postprocessors:Strip',
        'evals.solvers.postprocessors.postprocessors:RemoveQuotes'
    ]
)

This example demonstrates how to set up an OpenAI solver with specific postprocessors to clean the output.

Community and Contribution Aspects

The Evals project thrives on community contributions. Developers are encouraged to submit their own postprocessors and enhancements to the existing codebase. To contribute, you can:

• Fork the repository on GitHub.
• Create a new branch for your feature or bug fix.
• Submit a pull request with a clear description of your changes.

Engaging with the community through issues and discussions is also highly encouraged.

License and Legal Considerations

Evals is licensed under the Apache License 2.0, which allows for both personal and commercial use. It is important to adhere to the terms outlined in the license, especially regarding redistribution and modification of the code. For more details, refer to the full license text in the repository.

Conclusion

The Evals project is a powerful tool for developers working with AI models, providing essential postprocessing capabilities to ensure outputs are clean and evaluable. With its robust architecture, community-driven development, and comprehensive documentation, Evals stands out as a valuable resource in the AI landscape.

For more information, visit the official GitHub repository: GitHub – Evals.

FAQ

Here are some frequently asked questions about the Evals project: