embeddings-benchmark/mteb
Jan 2025 – Jan 2025
1 Month active
Languages Used
Python
Technical Skills
Benchmark DevelopmentData EngineeringMachine LearningNatural Language Processing
HSILA
PROFILE
Hsila
Developed and integrated the ChemTEB benchmark into the embeddings-benchmark/mteb repository to enable comprehensive evaluation of text embedding models within the chemical domain. This work introduced chemistry-specific classification, bitext mining, and retrieval tasks, broadening the benchmark’s coverage and enhancing the relevance of model comparisons for chemical applications. The implementation relied on Python for benchmarking pipelines and leveraged data engineering and natural language processing skills to ensure robust integration. All changes were managed through a single, clearly referenced Git commit, improving traceability and reproducibility. No major bugs were addressed during this period, with efforts focused on feature development and integration.
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
116 peopleSame Organization
@partners.basf.com1
Shared Repositories
115
DunZhangMember
Quan YuhanMember
HSILAMember
Aashka TrivediMember
AdnanElAssadiMember
Abdelrahman AbdallahMember
Heng CaiMember
ahxgwMember
Andrej RidzikMember
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
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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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