embeddings-benchmark/mteb
Dec 2024 – Aug 2025
2 Months active
Languages Used
Python
Technical Skills
Embedding ModelsModel IntegrationPython Development
Aashka Trivedi
PROFILE
Aashka Trivedi
Worked on expanding the embeddings-benchmark/mteb repository by integrating IBM Granite Embedding Models, including both multilingual and English variants with multiple parameter counts. Focused on end-to-end model integration and metadata extension, the work enabled broader benchmarking coverage and improved model selection for enterprise use cases. Leveraged Python to implement new model configurations, such as granite-embedding-english-r2 and granite-embedding-small-english-r2, and ensured that metadata like parameter count, memory usage, and embedding dimensions were available for analysis. The contributions enhanced benchmarking configurability and stability, supporting more comprehensive performance comparisons across embedding models without introducing new bugs during the development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
177
Activity Months2
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Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on embeddings-benchmark/mteb. Delivered new Granite Embedding English Model Options to broaden benchmarking coverage, enabling better model comparison and selection for English language embeddings. Maintained stability across the benchmark suite and prepared metadata for quick analysis of configurations.
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on embeddings-benchmark/mteb. Delivered new Granite Embedding English Model Options to broaden benchmarking coverage, enabling better model comparison and selection for English language embeddings. Maintained stability across the benchmark suite and prepared metadata for quick analysis of configurations.
August 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary: Delivered IBM Granite Embedding Models support in the MTEB benchmark suite, expanding evaluation coverage to include Granite embeddings with multilingual and English variants across multiple parameter counts, and integrated into the MTEB model overview. This work enhances enterprise benchmarking capabilities and aids in model selection decisions. No major bugs reported in connection with this feature during the period. The effort showcases end-to-end integration within the MTEB pipeline and demonstrates strong capabilities in extending framework metadata and multilingual support.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary: Delivered IBM Granite Embedding Models support in the MTEB benchmark suite, expanding evaluation coverage to include Granite embeddings with multilingual and English variants across multiple parameter counts, and integrated into the MTEB model overview. This work enhances enterprise benchmarking capabilities and aids in model selection decisions. No major bugs reported in connection with this feature during the period. The effort showcases end-to-end integration within the MTEB pipeline and demonstrates strong capabilities in extending framework metadata and multilingual support.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Embedding ModelsModel IntegrationPython Development
Repositories Contributed To
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