NVIDIA/GenerativeAIExamples
Mar 2025 – Mar 2025
1 Month active
Languages Used
JSONPython
Technical Skills
Data AnalysisData VisualizationJupyter NotebooksMachine Learning
Felipe Soares
PROFILE
Felipe Soares
Developed a Reliability Scoring Notebook for human evaluations in the NVIDIA/GenerativeAIExamples repository, focusing on robust model comparison workflows. The solution leveraged Python and Jupyter Notebooks to compute and visualize reliability metrics, including win-tie-loss scenarios and accuracy calculations, using data analysis and visualization techniques. By integrating SME and QC annotation benchmarking, the notebook enabled reproducible assessments of model disagreements and improved alignment between subject matter experts and quality control. The work emphasized end-to-end metric computation and matrix-based visualization, providing a clear framework for evaluating human-based model comparisons. No major bug fixes were reported, with efforts concentrated on feature development and workflow reproducibility.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
444
Activity Months1
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025Monthly summary for 2025-03 focusing on NVIDIA/GenerativeAIExamples. Key deliverable: Reliability Scoring Notebook for Human Evaluations, with metrics computation and visualization, enabling robust model comparisons and SME/QC alignment. No major bug fixes reported this month; core work emphasizes establishing a reproducible evaluation workflow and data-driven insights.
1 Commits • 1 Features
Mar 1, 2025Monthly summary for 2025-03 focusing on NVIDIA/GenerativeAIExamples. Key deliverable: Reliability Scoring Notebook for Human Evaluations, with metrics computation and visualization, enabling robust model comparisons and SME/QC alignment. No major bug fixes reported this month; core work emphasizes establishing a reproducible evaluation workflow and data-driven insights.
March 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
Data AnalysisData VisualizationJupyter NotebooksMachine Learning
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