This PR integrates the oproof Python package into the Tau project, enhancing the validation of prompt-response pairs using Ollama and Python. The integration includes updates to the BaseTask class and the implementation of iterative proofing to refine error handling. This change aims to improve the accuracy and robustness of the dataset processing pipeline.
Type of Change
Please delete options that are not relevant.
[x] New feature (non-breaking change which adds functionality)
[ ] Bug fix (non-breaking change which fixes an issue)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Documentation update
How Has This Been Tested?
The integration has been tested with both small and large datasets to ensure the oproof process works correctly and efficiently. The following tests were conducted:
[x] Test A: Processed a small dataset (tiny.json) to verify initial integration.
[x] Test B: Processed a larger dataset (data_A0_ophrase.json) to test scalability and performance.
Test Configuration:
Firmware version: N/A
Hardware: 3080 Ti Laptop
Toolchain: Unity ML Agents, Python
SDK: N/A
Checklist:
[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] I have commented my code, particularly in hard-to-understand areas
[x] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[x] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[x] Any dependent changes have been merged and published in downstream modules
Additional Notes
The iterative proofing process has been designed to continue until the number of errors stabilizes, ensuring a high-quality dataset. Future improvements may include further optimization of the proofing algorithm and additional validation steps.
Description
This PR integrates the
oproofPython package into the Tau project, enhancing the validation of prompt-response pairs using Ollama and Python. The integration includes updates to theBaseTaskclass and the implementation of iterative proofing to refine error handling. This change aims to improve the accuracy and robustness of the dataset processing pipeline.Type of Change
Please delete options that are not relevant.
How Has This Been Tested?
The integration has been tested with both small and large datasets to ensure the
oproofprocess works correctly and efficiently. The following tests were conducted:tiny.json) to verify initial integration.data_A0_ophrase.json) to test scalability and performance.Test Configuration:
Checklist:
Additional Notes
The iterative proofing process has been designed to continue until the number of errors stabilizes, ensuring a high-quality dataset. Future improvements may include further optimization of the proofing algorithm and additional validation steps.