dice-group/dice-embeddings
Oct 2024 – Aug 2025
7 Months active
Languages Used
PythonJinjaSQLC++Jupyter NotebookJSONMarkdown
Technical Skills
Deep LearningGraph EmbeddingsKnowledge Graph EmbeddingsMachine LearningPyTorchData Preprocessing
sapkotaruz11
PROFILE
Sapkotaruz11
Rupesh Sapkota developed advanced knowledge graph embedding features for the dice-group/dice-embeddings repository, focusing on literal value prediction, distributed training, and robust evaluation. He engineered joint training pipelines and ensemble methods using Python and PyTorch, integrating literal handling with regression models and normalization to improve model accuracy and reproducibility. His work included scalable data loading with rdflib, adaptive learning rate scheduling, and distributed data parallelism for multi-GPU environments. By refactoring evaluation metrics and enhancing test infrastructure, Rupesh improved code maintainability and experiment reliability. His contributions addressed both architectural depth and practical ML workflow needs, supporting scalable, numerically grounded knowledge graph applications.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
86Total
Bugs
20
Commits
86
Features
35
Lines of code
10,029
Activity Months7
Your Network
8 people
Work History
August 2025
27 Commits • 11 Features
Aug 1, 2025August 2025 monthly wrap-up for the dice-embeddings project focused on scalability, reliability, and performance enhancements across distributed training, evaluation, and data pipelines. Implemented SWA-driven optimization and robust testing, accelerated training workflows, and improved developer experience through documentation and lint/maintainability improvements.
27 Commits • 11 Features
Aug 1, 2025August 2025 monthly wrap-up for the dice-embeddings project focused on scalability, reliability, and performance enhancements across distributed training, evaluation, and data pipelines. Implemented SWA-driven optimization and robust testing, accelerated training workflows, and improved developer experience through documentation and lint/maintainability improvements.
August 2025
July 2025
21 Commits • 8 Features
Jul 1, 2025July 2025 (2025-07): Delivered notable enhancements to the dice-embeddings project with practical ML tooling improvements and improved reliability across the training and evaluation pipeline. Key outcomes include: new KGE Literal Prediction tutorials and example directory fixes; expanded ensemble capabilities with report saving and snapshot-based ensemble enhancements; and enhanced training observability with adaptive learning rate logging and robust periodic evaluation. Several bug fixes improved stability and reproducibility, including fixes to literals tests, experiment arg logging, CUDA device counting, deprecation warnings, and general code quality. These efforts increased model experimentation velocity, reliability of results, and readiness for production deployment.
July 2025
21 Commits • 8 Features
Jul 1, 2025July 2025 (2025-07): Delivered notable enhancements to the dice-embeddings project with practical ML tooling improvements and improved reliability across the training and evaluation pipeline. Key outcomes include: new KGE Literal Prediction tutorials and example directory fixes; expanded ensemble capabilities with report saving and snapshot-based ensemble enhancements; and enhanced training observability with adaptive learning rate logging and robust periodic evaluation. Several bug fixes improved stability and reproducibility, including fixes to literals tests, experiment arg logging, CUDA device counting, deprecation warnings, and general code quality. These efforts increased model experimentation velocity, reliability of results, and readiness for production deployment.
June 2025
26 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary for the dice-embeddings project: Delivered substantial improvements in data ingestion, model reliability, and evaluation scalability. Key features include literal loading via rdflib integrated into the main load/validate flow and adaptive learning rate scheduling for more stable training. Significant enhancements to evaluation include batching for ensemble link prediction, revised KGE evaluation, and a borda-rank ensemble metric, plus foundational refactors for KGE literals and tests. Strengthened code quality and maintainability through test script restructuring, linting/sanity checks updates, and documentation enhancements. Notable bug fixes include reverting unintended formatting changes and fixing LP evaluation logic in ensemble evaluation. Business impact: improved data loading reliability, faster and more stable model training, more accurate ensemble predictions, and a maintainable codebase reducing regression risk.
26 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary for the dice-embeddings project: Delivered substantial improvements in data ingestion, model reliability, and evaluation scalability. Key features include literal loading via rdflib integrated into the main load/validate flow and adaptive learning rate scheduling for more stable training. Significant enhancements to evaluation include batching for ensemble link prediction, revised KGE evaluation, and a borda-rank ensemble metric, plus foundational refactors for KGE literals and tests. Strengthened code quality and maintainability through test script restructuring, linting/sanity checks updates, and documentation enhancements. Notable bug fixes include reverting unintended formatting changes and fixing LP evaluation logic in ensemble evaluation. Business impact: improved data loading reliability, faster and more stable model training, more accurate ensemble predictions, and a maintainable codebase reducing regression risk.
June 2025
May 2025
3 Commits • 2 Features
May 1, 2025Month: 2025-05 – Performance review-ready monthly summary for the dice-embeddings repo (dice-group/dice-embeddings). Focused on delivering value through enhanced knowledge-graph embeddings, reproducibility, and architectural improvements that enable reliable experiments and better handling of numerical literals in knowledge graphs. Key features delivered: - Literal Embeddings and Prediction Enhancements for Knowledge Graph Embedding: Introduced a new literal embedding module with normalization of literal values and the ability to predict numerical literals; updated LiteralDataset and LiteralEmbeddings to support better data processing and model architecture. - Reproducibility and Architecture Improvements for Model Training: Ensured reproducible experiments by fixing CUDA device allocation and seed handling, moving relevant tensors to CUDA when available, and refactoring the GatedLinearUnit to support a gated residual connection for improved information fusion. Major bugs fixed: - CUDA device allocation handling to prevent non-deterministic device usage. Overall impact and accomplishments: - Enhanced capability to train and evaluate models on numerical literals in knowledge graphs, increasing model usefulness for numerically grounded predictions. - Improved experimental reproducibility and stability, enabling faster experimentation cycles and trustworthy results. - Architectural refinements improve the model's ability to combine information from multiple streams, contributing to stronger predictive performance potential. Technologies/skills demonstrated: - Knowledge graph embedding techniques, LiteralDataset and LiteralEmbeddings engineering, PyTorch-based model development, CUDA device management and seed handling, Gated Linear Unit with gated residuals, and data pipeline improvements.
May 2025
3 Commits • 2 Features
May 1, 2025Month: 2025-05 – Performance review-ready monthly summary for the dice-embeddings repo (dice-group/dice-embeddings). Focused on delivering value through enhanced knowledge-graph embeddings, reproducibility, and architectural improvements that enable reliable experiments and better handling of numerical literals in knowledge graphs. Key features delivered: - Literal Embeddings and Prediction Enhancements for Knowledge Graph Embedding: Introduced a new literal embedding module with normalization of literal values and the ability to predict numerical literals; updated LiteralDataset and LiteralEmbeddings to support better data processing and model architecture. - Reproducibility and Architecture Improvements for Model Training: Ensured reproducible experiments by fixing CUDA device allocation and seed handling, moving relevant tensors to CUDA when available, and refactoring the GatedLinearUnit to support a gated residual connection for improved information fusion. Major bugs fixed: - CUDA device allocation handling to prevent non-deterministic device usage. Overall impact and accomplishments: - Enhanced capability to train and evaluate models on numerical literals in knowledge graphs, increasing model usefulness for numerically grounded predictions. - Improved experimental reproducibility and stability, enabling faster experimentation cycles and trustworthy results. - Architectural refinements improve the model's ability to combine information from multiple streams, contributing to stronger predictive performance potential. Technologies/skills demonstrated: - Knowledge graph embedding techniques, LiteralDataset and LiteralEmbeddings engineering, PyTorch-based model development, CUDA device management and seed handling, Gated Linear Unit with gated residuals, and data pipeline improvements.
January 2025
3 Commits • 2 Features
Jan 1, 2025January 2025 monthly performance summary focusing on delivering a joint knowledge graph (KG) embeddings and literal value prediction capability, improving evaluation metrics, and enabling reproducible experiments with clear business value.
3 Commits • 2 Features
Jan 1, 2025January 2025 monthly performance summary focusing on delivering a joint knowledge graph (KG) embeddings and literal value prediction capability, improving evaluation metrics, and enabling reproducible experiments with clear business value.
January 2025
November 2024
4 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered substantial enhancements to Knowledge Graph Embeddings (literal training and evaluation) for the repository dice-group/dice-embeddings. Refactored training/prediction pipelines to improve clarity and efficiency, enabling faster iteration and easier maintenance. Switched evaluation to Mean Absolute Error (MAE) with added RMSE to provide richer feedback on literal predictions. Introduced Z-normalization for literals and dropout for regularization, and updated the loss function to MAE with an adjusted optimizer for better convergence. These changes yield more reliable metrics for literal predictions, improving model selection and downstream decision-making in embedding workflows. No major defects reported this month; minor stability tweaks were performed as part of the refactor. All changes tracked across four commits.
November 2024
4 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered substantial enhancements to Knowledge Graph Embeddings (literal training and evaluation) for the repository dice-group/dice-embeddings. Refactored training/prediction pipelines to improve clarity and efficiency, enabling faster iteration and easier maintenance. Switched evaluation to Mean Absolute Error (MAE) with added RMSE to provide richer feedback on literal predictions. Introduced Z-normalization for literals and dropout for regularization, and updated the loss function to MAE with an adjusted optimizer for better convergence. These changes yield more reliable metrics for literal predictions, improving model selection and downstream decision-making in embedding workflows. No major defects reported this month; minor stability tweaks were performed as part of the refactor. All changes tracked across four commits.
October 2024
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10. Focused on delivering a feature to improve knowledge graph embeddings by predicting literal values and ensuring robust literal handling. The work enhances downstream KG tasks (search, inference, analytics) by boosting accuracy and reliability of embeddings.
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10. Focused on delivering a feature to improve knowledge graph embeddings by predicting literal values and ensuring robust literal handling. The work enhances downstream KG tasks (search, inference, analytics) by boosting accuracy and reliability of embeddings.
October 2024
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Quality Metrics
Correctness86.2%
Maintainability86.0%
Architecture83.0%
Performance76.6%
AI Usage22.2%
Skills & Technologies
Programming Languages
C++JSONJinjaJupyter NotebookMarkdownPythonSQL
Technical Skills
Argument ParsingBackend DevelopmentBatch ProcessingCUDACallback FunctionsCallback ImplementationCode CleanupCode ConsolidationCode FormattingCode LintingCode OrganizationCode RefactoringConfigurationConfiguration ManagementData Analysis
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