embeddings-benchmark/mteb
Jan 2025 – Jan 2025
1 Month active
Languages Used
Python
Technical Skills
Benchmark DevelopmentData EngineeringMachine LearningNatural Language Processing
HSILA
PROFILE
Hsila
Ali Shiraee developed and integrated the ChemTEB benchmark into the embeddings-benchmark/mteb repository, enabling comprehensive evaluation of text embedding models for chemical-domain applications. Using Python, he designed benchmarking pipelines that introduced chemistry-focused classification, bitext mining, and retrieval tasks, broadening the benchmark’s coverage and relevance for chemical research. His work emphasized robust feature integration and reproducible change management through a single, well-documented commit. By expanding the benchmark’s scope, Ali addressed the need for more domain-specific model assessment, supporting better R&D decisions in chemical informatics. His contributions demonstrated depth in benchmark development, data engineering, and natural language processing within a collaborative environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,703
Activity Months1
Your Network
92 peopleSame Organization
@partners.basf.com1
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered ChemTEB Benchmark Integration in embeddings-benchmark/mteb to evaluate text embedding models in the chemical domain, adding chemistry-focused classification, bitext mining, and retrieval tasks. No major bugs fixed. Result: broader benchmark coverage enabling more robust model comparison for chemical-domain use cases, driving better R&D decisions and faster time-to-value. Technologies/skills: Python benchmarking pipelines, feature integration, and Git-based change management with a clearly referenced commit.
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered ChemTEB Benchmark Integration in embeddings-benchmark/mteb to evaluate text embedding models in the chemical domain, adding chemistry-focused classification, bitext mining, and retrieval tasks. No major bugs fixed. Result: broader benchmark coverage enabling more robust model comparison for chemical-domain use cases, driving better R&D decisions and faster time-to-value. Technologies/skills: Python benchmarking pipelines, feature integration, and Git-based change management with a clearly referenced commit.
January 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Benchmark DevelopmentData EngineeringMachine LearningNatural Language Processing
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