embeddings-benchmark/mteb
Feb 2025 – Jun 2025
5 Months active
Languages Used
PythonMarkdownYAML
Technical Skills
Frontend DevelopmentAPI DevelopmentBackend DevelopmentBenchmark DevelopmentBenchmarkingCode Maintenance
Munot Ayush Sunil
PROFILE
Munot Ayush Sunil
Ayush Munot contributed to the embeddings-benchmark/mteb repository by developing and integrating features that enhanced benchmarking coverage, multilingual support, and evaluation fidelity for embedding models. He implemented new model integrations, such as KaLM and VDR, and improved UI elements for better usability and data visualization. Using Python and Pandas, Ayush refactored code for dependency management, standardized language codes with BCP-47 tags, and automated CI workflows with GitHub Actions. His work addressed robustness in embedding generation, improved documentation, and ensured traceability through clear commit practices. The depth of his contributions strengthened reliability, maintainability, and cross-task comparability within the benchmarking framework.
Overall Statistics
Feature vs Bugs
53%Features
Repository Contributions
24Total
Bugs
9
Commits
24
Features
10
Lines of code
4,657
Activity Months5
Your Network
96 peopleSame Organization
@kgpian.iitkgp.ac.in5
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered KaLM-Embedding Models integration into the MTEB Benchmark for embeddings-benchmark/mteb. Implemented three HIT-TMG KaLM embedding models, added a wrapper class, integrated these models into the MTEB framework, and updated model metadata and instruction handling to support multiple tasks. The work enhances benchmarking coverage, improves evaluation fidelity for KaLM embeddings, and enables consistent cross-task comparisons. Commit traceability maintained with 03e084bc37d48809dd9ce6f6bc43311ede77570d.
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered KaLM-Embedding Models integration into the MTEB Benchmark for embeddings-benchmark/mteb. Implemented three HIT-TMG KaLM embedding models, added a wrapper class, integrated these models into the MTEB framework, and updated model metadata and instruction handling to support multiple tasks. The work enhances benchmarking coverage, improves evaluation fidelity for KaLM embeddings, and enables consistent cross-task comparisons. Commit traceability maintained with 03e084bc37d48809dd9ce6f6bc43311ede77570d.
June 2025
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 — Embeddings Benchmark (embeddings-benchmark/mteb). Focused on reliability, CI/QA automation, and robustness of embedding generation. Key work included implementing Leaderboard Stability Testing and CI Automation, correcting documentation and dependency guidance, and hardening OpenAI Text Embedding3-Small for edge cases. These efforts improved stability, reduced debugging time, and clarified onboarding and usage for dependencies, delivering measurable business value and a maintainable codebase.
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 — Embeddings Benchmark (embeddings-benchmark/mteb). Focused on reliability, CI/QA automation, and robustness of embedding generation. Key work included implementing Leaderboard Stability Testing and CI Automation, correcting documentation and dependency guidance, and hardening OpenAI Text Embedding3-Small for edge cases. These efforts improved stability, reduced debugging time, and clarified onboarding and usage for dependencies, delivering measurable business value and a maintainable codebase.
April 2025
7 Commits • 3 Features
Apr 1, 2025April 2025 (Month: 2025-04) — Embeddings Benchmark / MTEB repository: consolidated documentation improvements, critical bug fixes, and metadata enhancements to boost reliability, usability, and data quality across tasks and languages. Delivered features and fixes emphasize robust loading, standardized language handling, and richer benchmarking metadata, driving better reproducibility and business value for benchmarking teams and users.
7 Commits • 3 Features
Apr 1, 2025April 2025 (Month: 2025-04) — Embeddings Benchmark / MTEB repository: consolidated documentation improvements, critical bug fixes, and metadata enhancements to boost reliability, usability, and data quality across tasks and languages. Delivered features and fixes emphasize robust loading, standardized language handling, and richer benchmarking metadata, driving better reproducibility and business value for benchmarking teams and users.
April 2025
March 2025
12 Commits • 5 Features
Mar 1, 2025March 2025 performance summary for embeddings-benchmark/mteb: Delivered feature-rich enhancements and stability improvements to visualization, retrieval, and build processes, enabling clearer benchmarking insights, multilingual evaluation, and faster iteration cycles. Key outcomes include improved visual readability, multilingual data support, data modality filtering, and UI enhancements, along with foundational quality improvements in logging and dependency management.
March 2025
12 Commits • 5 Features
Mar 1, 2025March 2025 performance summary for embeddings-benchmark/mteb: Delivered feature-rich enhancements and stability improvements to visualization, retrieval, and build processes, enabling clearer benchmarking insights, multilingual evaluation, and faster iteration cycles. Key outcomes include improved visual readability, multilingual data support, data modality filtering, and UI enhancements, along with foundational quality improvements in logging and dependency management.
February 2025
1 Commits
Feb 1, 2025Feb 2025 monthly summary for embeddings-benchmark/mteb: Delivered a focused UI bug fix to ensure task dropdowns display items in alphabetical order, improving consistency and usability for benchmark task selection. The change was implemented as a small, low-risk patch and tracked under commit "fee6fc065508cae0a2d34dae478d5423bcd2e155" with message "fix: Alphabetical ordering of tasks in dropdowns (#2191)". This fix enhances UX and reduces potential user errors when navigating task lists across the benchmark suite.
1 Commits
Feb 1, 2025Feb 2025 monthly summary for embeddings-benchmark/mteb: Delivered a focused UI bug fix to ensure task dropdowns display items in alphabetical order, improving consistency and usability for benchmark task selection. The change was implemented as a small, low-risk patch and tracked under commit "fee6fc065508cae0a2d34dae478d5423bcd2e155" with message "fix: Alphabetical ordering of tasks in dropdowns (#2191)". This fix enhances UX and reduces potential user errors when navigating task lists across the benchmark suite.
February 2025
Activity
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Quality Metrics
Correctness89.2%
Maintainability88.2%
Architecture84.6%
Performance78.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
API DevelopmentAPI IntegrationBCP-47 Language TagsBackend DevelopmentBenchmark DevelopmentBenchmarkingBug FixCI/CDCode CorrectionCode MaintenanceCode RefactoringCode ValidationConfiguration ManagementData AnalysisData Cleaning
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