dice-group/dice-embeddings
Mar 2025 – Oct 2025
2 Months active
Languages Used
Python
Technical Skills
Code CleanupDebuggingDeep LearningMachine LearningPyTorchBug Fixing
Louis-Mozart
PROFILE
Louis-mozart
Louis Mozart Teyou contributed to the dice-embeddings repository by focusing on stability, correctness, and data robustness in deep learning pipelines. Over two months, he improved the reliability of model training and inference by fixing tensor dimensionality issues in negative sampling and refining internal sigma computations for DeCaL models. He enhanced data ingestion by refactoring file format detection using Python’s os.path utilities, ensuring robust handling of diverse dataset formats. His work emphasized code cleanup, debugging, and maintainability, reducing edge-case failures and production risks. Using Python and PyTorch, Louis delivered targeted engineering solutions that improved the overall quality and reliability of the codebase.
Overall Statistics
Feature vs Bugs
25%Features
Repository Contributions
4Total
Bugs
3
Commits
4
Features
1
Lines of code
57
Activity Months2
Your Network
8 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 performance summary for the dice-embeddings repository. Focused on stabilizing training workflows and improving data ingestion robustness to support broader data formats and reliable model iteration cycles.
2 Commits • 1 Features
Oct 1, 2025October 2025 performance summary for the dice-embeddings repository. Focused on stabilizing training workflows and improving data ingestion robustness to support broader data formats and reliable model iteration cycles.
October 2025
March 2025
2 Commits
Mar 1, 2025March 2025: Focused on stability and correctness in the dice-embeddings repository. No feature releases; main value delivered through targeted bug fixes and production hygiene improvements that reduce risk in training and inference pipelines, enhancing the reliability of embeddings.
March 2025
2 Commits
Mar 1, 2025March 2025: Focused on stability and correctness in the dice-embeddings repository. No feature releases; main value delivered through targeted bug fixes and production hygiene improvements that reduce risk in training and inference pipelines, enhancing the reliability of embeddings.
Activity
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Quality Metrics
Correctness85.0%
Maintainability90.0%
Architecture85.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Bug FixingCode CleanupData PreprocessingDebuggingDeep LearningFile HandlingMachine LearningModel TrainingPyTorch
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