UKGovernmentBEIS/inspect_evals
Dec 2025 – Dec 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Python programmingalgorithm optimizationdata analysisdata evaluationdata presentationdocumentation
Vyas Sidhi
PROFILE
Vyas Sidhi
Siddhi Vyas enhanced the evaluation workflow for the UKGovernmentBEIS/inspect_evals repository by enabling dynamic dataset support in the StereoSet benchmarking pipeline. Using Python and Markdown, Siddhi removed the hardcoded five-sample limit, allowing full-dataset evaluation and more comprehensive model benchmarking, including support for ollama/llama3.2. The work involved optimizing algorithms for data analysis and evaluation, as well as updating documentation to clearly present results and workflow details. These changes improved the reliability, scalability, and reproducibility of model evaluation, providing a single, well-documented source of truth for StereoSet results and supporting future reviews and audits with transparent, traceable reporting.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
1
Lines of code
30
Activity Months1
Your Network
101 peopleWork History
December 2025
6 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Strengthened the evaluation workflow and benchmark reporting for the StereoSet/inspection eval pipeline in UKGovernmentBEIS/inspect_evals. Delivered dynamic dataset support by removing the hardcoded 5-sample limit, expanded evaluation reporting to full StereoSet dataset across models (including ollama/llama3.2), and refreshed documentation. Result: more accurate, scalable, and reproducible model benchmarking with clearer stakeholder-facing results. What changed: - Removed hardcoded 5-sample limit in StereoSet evaluation, enabling full-dataset benchmarking. - Added StereoSet benchmark evaluation results for the full dataset. - Added StereoSet evaluation results for ollama/llama3.2. - Updated README to surface StereoSet evaluation results and workflow details. Impact: - Improves reliability and scalability of model evaluation, enabling fair, end-to-end benchmarking across datasets and models. - Enhances documentation and reproducibility for future reviews and audits. - Provides a single source of truth for StereoSet-related results. Tech/skill signals: - StereoSet benchmarking, model evaluation, dataset handling, documentation discipline, git-based traceability.
6 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Strengthened the evaluation workflow and benchmark reporting for the StereoSet/inspection eval pipeline in UKGovernmentBEIS/inspect_evals. Delivered dynamic dataset support by removing the hardcoded 5-sample limit, expanded evaluation reporting to full StereoSet dataset across models (including ollama/llama3.2), and refreshed documentation. Result: more accurate, scalable, and reproducible model benchmarking with clearer stakeholder-facing results. What changed: - Removed hardcoded 5-sample limit in StereoSet evaluation, enabling full-dataset benchmarking. - Added StereoSet benchmark evaluation results for the full dataset. - Added StereoSet evaluation results for ollama/llama3.2. - Updated README to surface StereoSet evaluation results and workflow details. Impact: - Improves reliability and scalability of model evaluation, enabling fair, end-to-end benchmarking across datasets and models. - Enhances documentation and reproducibility for future reviews and audits. - Provides a single source of truth for StereoSet-related results. Tech/skill signals: - StereoSet benchmarking, model evaluation, dataset handling, documentation discipline, git-based traceability.
December 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Python programmingalgorithm optimizationdata analysisdata evaluationdata presentationdocumentationmodel evaluation
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