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yuma-hirakawa

PROFILE

Yuma-hirakawa

Yuma Hirakawa enhanced the sbintuitions/flexeval repository by developing F1-based evaluation metrics for multiple-choice question assessment, focusing on both macro and micro scoring approaches. He refactored the evaluate_multiple_choice function to improve clarity and maintainability, ensuring correct variable usage and output structure. Using Python and leveraging the scikit-learn library, Yuma also improved logging formatting and expanded test coverage to verify the presence of expected metric keys. Additionally, he updated project dependencies to maintain compatibility with scikit-learn 1.6.1. The work demonstrated a methodical approach to code quality, metric evaluation, and dependency management within a data science and machine learning context.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

9Total
Bugs
0
Commits
9
Features
2
Lines of code
579
Activity Months1

Work History

June 2025

9 Commits • 2 Features

Jun 1, 2025

June 2025 performance summary for sbintuitions/flexeval highlighting feature delivery, bug fixes, and impact. Implemented F1-based evaluation metrics for MCQ evaluation, refactored code for clarity, improved logging, added tests to verify metric keys, and updated dependencies for compatibility.

Activity

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Quality Metrics

Correctness97.8%
Maintainability100.0%
Architecture97.8%
Performance97.8%
AI Usage20.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

Code FormattingCode RefactoringData ScienceDebuggingDependency ManagementEvaluation MetricsLoggingMachine LearningPython PackagingTestingUnit Testing

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

sbintuitions/flexeval

Jun 2025 Jun 2025
1 Month active

Languages Used

Python

Technical Skills

Code FormattingCode RefactoringData ScienceDebuggingDependency ManagementEvaluation Metrics

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