Shubhamsaboo/Qwen3-Coder
Mar 2025 – Mar 2025
1 Month active
Languages Used
Python
Technical Skills
Code RefactoringData EvaluationMachine LearningNatural Language ProcessingSQL
lfy79001
PROFILE
Lfy79001
Developed the Bird-Spider SQL Generation Evaluation Framework within the Qwen3-Coder repository, establishing a unified approach for benchmarking SQL generation models across the Bird and Spider datasets. The work involved designing components for data preprocessing, model generation, and execution-based evaluation of SQL queries, all implemented in Python with a focus on SQL and data evaluation techniques. Legacy evaluation files were refactored and removed to streamline the framework and reduce maintenance overhead. This foundation enables reproducible benchmarking and accelerates iteration on machine learning models for natural language processing tasks, providing clearer insights into model performance and supporting ongoing development efficiency.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
3,142
Activity Months1
Your Network
15 peopleWork History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered the Bird-Spider SQL Generation Evaluation Framework for Qwen3-Coder, establishing a unified benchmarking approach for SQL generation across Bird and Spider datasets. Included components for data preprocessing, model generation, and execution-based evaluation of SQL queries, plus cleanup/refactor removing legacy evaluation files to streamline maintenance. This work enables reproducible benchmarking, faster iteration, and clearer visibility into model performance.
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered the Bird-Spider SQL Generation Evaluation Framework for Qwen3-Coder, establishing a unified benchmarking approach for SQL generation across Bird and Spider datasets. Included components for data preprocessing, model generation, and execution-based evaluation of SQL queries, plus cleanup/refactor removing legacy evaluation files to streamline maintenance. This work enables reproducible benchmarking, faster iteration, and clearer visibility into model performance.
March 2025
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code RefactoringData EvaluationMachine LearningNatural Language ProcessingSQL
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