Red-Hat-AI-Innovation-Team/sdg_hub
Mar 2025 – Mar 2025
1 Month active
Languages Used
JSON
Technical Skills
Data EngineeringMachine LearningNatural Language Processing
Abhishek Bhandwaldar
PROFILE
Abhishek Bhandwaldar
Developed and released a foundational emotion analysis dataset resource in the Red-Hat-AI-Innovation-Team/sdg_hub repository, focusing on standardizing sentence classification into six emotion categories. Leveraged data engineering and natural language processing skills to design structured prompts, labels, and detailed task descriptions, ensuring consistent labeling and evaluation across teams. Incorporated example-driven Q&A to support reproducible experiments and facilitate onboarding for data scientists. The resource, formatted in JSON, established a reproducible framework for benchmarking and model development, enabling improved collaboration and faster iteration on emotion detection features. This work laid the groundwork for robust downstream machine learning model evaluation and comparison protocols.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
11,589
Activity Months1
Your Network
21 peopleShared Repositories
21
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Red-Hat-AI-Innovation-Team/sdg_hub focused on delivering a foundational emotion analysis dataset resource that enables standardized model development and benchmarking. The release provides a structured resource for classifying sentences into six emotion categories, with clear prompts, labels, task descriptions, examples, and guided Q&A to ensure consistent labeling and evaluation across teams. This lays the groundwork for reproducible experiments, faster onboarding for data scientists, and measurable improvements in model development velocity and comparison."
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Red-Hat-AI-Innovation-Team/sdg_hub focused on delivering a foundational emotion analysis dataset resource that enables standardized model development and benchmarking. The release provides a structured resource for classifying sentences into six emotion categories, with clear prompts, labels, task descriptions, examples, and guided Q&A to ensure consistent labeling and evaluation across teams. This lays the groundwork for reproducible experiments, faster onboarding for data scientists, and measurable improvements in model development velocity and comparison."
March 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSON
Technical Skills
Data EngineeringMachine LearningNatural Language Processing
Repositories Contributed To
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