embeddings-benchmark/mteb
May 2026 – May 2026
1 Month active
Languages Used
Python
Technical Skills
Data ScienceDeep LearningMachine LearningModel DevelopmentNatural Language Processing
Abdelrahman Abdallah
PROFILE
Abdelrahman Abdallah
Contributed to the embeddings-benchmark/mteb repository by expanding the Argus-Colqwen3.5 model family, introducing new 4B and 9B variants with both fp32 and bf16 precision support. Focused on enhancing retrieval capabilities, the work included implementing region-aware query conditioning to improve cross-region performance and updating the model catalog for easier discovery and deployment. Refactored the ArgusColQwen35Wrapper to streamline inheritance and eliminate redundant code paths, ensuring compatibility with AutoModel.from_pretrained. Applied targeted linting and code cleanups to improve maintainability. Leveraged Python and deep learning frameworks, with an emphasis on model development, data science, and natural language processing techniques throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
250
Activity Months1
Your Network
117 peopleWork History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for embeddings-benchmark/mteb: Delivered substantive expansion of the Argus-Colqwen3.5 model family with new 4B and 9B variants, enhanced retrieval capabilities, and updated catalog and tooling to boost search quality and scalability.
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for embeddings-benchmark/mteb: Delivered substantive expansion of the Argus-Colqwen3.5 model family with new 4B and 9B variants, enhanced retrieval capabilities, and updated catalog and tooling to boost search quality and scalability.
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceDeep LearningMachine LearningModel DevelopmentNatural Language Processing
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