microsoft/eureka-ml-insights
Nov 2024 – Dec 2024
2 Months active
Languages Used
PythonJinja
Technical Skills
Configuration ManagementData EngineeringMachine LearningNatural Language ProcessingPython DevelopmentDataset Integration
Vaishnavi Shrivastava
PROFILE
Vaishnavi Shrivastava
Vaish Shrivastava contributed to the microsoft/eureka-ml-insights repository by integrating two complex datasets, DROP and GPQA, into the platform over a two-month period. Using Python and Jinja, Vaish engineered end-to-end data ingestion, processing, and evaluation pipelines, introducing new configuration management and data engineering utilities. The work included implementing a dedicated evaluation metric for DROP and developing shuffling and column mapping utilities to support multiple-choice formats in GPQA. These integrations expanded the platform’s benchmarking capabilities for natural language processing and geometric reasoning tasks, demonstrating depth in dataset integration and maintainability within the existing machine learning pipeline architecture.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
523
Activity Months2
Your Network
12 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month 2024-12: GPQA Dataset Integration completed for the eureka-ml-insights project, establishing end-to-end GPQA ingestion and evaluation workflow. Implemented configuration, data processing logic, and utilities for shuffling and column mapping to support multiple-choice question formats. This enables processing and evaluation on the GPQA benchmark for geometric reasoning and lays groundwork for broader benchmarking in future sprints.
1 Commits • 1 Features
Dec 1, 2024Month 2024-12: GPQA Dataset Integration completed for the eureka-ml-insights project, establishing end-to-end GPQA ingestion and evaluation workflow. Implemented configuration, data processing logic, and utilities for shuffling and column mapping to support multiple-choice question formats. This enables processing and evaluation on the GPQA benchmark for geometric reasoning and lays groundwork for broader benchmarking in future sprints.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for microsoft/eureka-ml-insights: Delivered DROP dataset integration into the library, enabling end-to-end processing, inference, and evaluation on the DROP dataset. Implemented a dedicated pipeline configuration and a new evaluation metric to assess model performance on DROP. This work expands data-source coverage, enhances benchmarking capabilities, and strengthens the platform’s value for customers requiring robust, diverse evaluations. The work emphasizes maintainability and compatibility with the existing pipeline architecture and aligns with the repository work in microsoft/eureka-ml-insights, including the associated commit.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for microsoft/eureka-ml-insights: Delivered DROP dataset integration into the library, enabling end-to-end processing, inference, and evaluation on the DROP dataset. Implemented a dedicated pipeline configuration and a new evaluation metric to assess model performance on DROP. This work expands data-source coverage, enhances benchmarking capabilities, and strengthens the platform’s value for customers requiring robust, diverse evaluations. The work emphasizes maintainability and compatibility with the existing pipeline architecture and aligns with the repository work in microsoft/eureka-ml-insights, including the associated commit.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPython
Technical Skills
Configuration ManagementData EngineeringDataset IntegrationMachine LearningNatural Language ProcessingPython Development
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