embeddings-benchmark/mteb
Dec 2024 – Aug 2025
2 Months active
Languages Used
Python
Technical Skills
Embedding ModelsModel IntegrationPython Development
Aashka Trivedi
PROFILE
Aashka Trivedi
Aashka Trivedi developed and integrated support for IBM Granite Embedding Models within the embeddings-benchmark/mteb repository, expanding the MTEB benchmark suite to include multilingual and English variants across multiple parameter counts. Using Python, she extended the framework’s metadata and model overview to reflect new embedding options, enabling more comprehensive benchmarking and model selection for enterprise users. Her work included adding detailed configuration metadata such as parameter count, memory usage, and embedding dimensions, which improved the suite’s configurability and analysis capabilities. Throughout the two-month period, Aashka focused on embedding models and model integration, delivering robust, end-to-end enhancements without introducing bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
177
Activity Months2
Your Network
91 people
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
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