dice-group/dice-embeddings
Oct 2024 – Apr 2026
12 Months active
Languages Used
PythonJinjaSQLC++Jupyter NotebookJSONMarkdown
Technical Skills
Deep LearningGraph EmbeddingsKnowledge Graph EmbeddingsMachine LearningPyTorchData Preprocessing
sapkotaruz11
PROFILE
Sapkotaruz11
Over twelve months, contributed to the dice-group/dice-embeddings repository by engineering robust knowledge graph embedding and literal value prediction workflows. Leveraging Python and PyTorch, developed distributed training pipelines, ensemble evaluation methods, and weight averaging frameworks to improve model reliability and scalability. Enhanced data ingestion with rdflib, optimized training through adaptive learning rate scheduling, and strengthened reproducibility with rigorous testing and configuration management. Refactored core modules for maintainability, introduced continual learning support, and expanded documentation for onboarding and experiment clarity. The work enabled more accurate, stable, and production-ready machine learning models, supporting both research and deployment in knowledge graph-driven applications.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
131Total
Bugs
21
Commits
131
Features
44
Lines of code
13,994
Activity Months12
Your Network
9 peopleShared Repositories
9
Ajay KumarMember
alkidbaciMember
ashishtiwari03Member
Caglar DemirMember
DuygubirolMember
Jean-KOUAGOUMember
Louis-MozartMember
Luke FriedrichsMember
Shivam SharmaMember
Work History
April 2026
11 Commits • 3 Features
Apr 1, 2026April 2026 performance summary for dice-embeddings focused on reliability, experimentation robustness, and evaluability enhancements. Delivered three major feature areas with meaningful business value: (1) Replaced the memory-mapped KGE loader with a function-based loader, increasing flexibility and reliability in knowledge graph loading/creation. (2) Strengthened experiment reproducibility by enhancing directory reuse tests, ensuring configurations persist across runs without unnecessary data duplication. (3) Expanded evaluation capabilities and output handling for KGE with new link-prediction options and a use_logits parameter to support raw logits in predictions. Also implemented targeted bug fixes to improve stability and observability across the repository.
11 Commits • 3 Features
Apr 1, 2026April 2026 performance summary for dice-embeddings focused on reliability, experimentation robustness, and evaluability enhancements. Delivered three major feature areas with meaningful business value: (1) Replaced the memory-mapped KGE loader with a function-based loader, increasing flexibility and reliability in knowledge graph loading/creation. (2) Strengthened experiment reproducibility by enhancing directory reuse tests, ensuring configurations persist across runs without unnecessary data duplication. (3) Expanded evaluation capabilities and output handling for KGE with new link-prediction options and a use_logits parameter to support raw logits in predictions. Also implemented targeted bug fixes to improve stability and observability across the repository.
April 2026
March 2026
4 Commits • 2 Features
Mar 1, 2026Monthly summary for 2026-03 (dice-group/dice-embeddings). Focused on distributed training robustness, configurability, and maintainability improvements. The work enhances reliability and scalability of multinode training, simplifies initialization, expands trainer options, and clarifies CUDA Graph health checks, enabling faster experimentation and broader deployment readiness.
March 2026
4 Commits • 2 Features
Mar 1, 2026Monthly summary for 2026-03 (dice-group/dice-embeddings). Focused on distributed training robustness, configurability, and maintainability improvements. The work enhances reliability and scalability of multinode training, simplifies initialization, expands trainer options, and clarifies CUDA Graph health checks, enabling faster experimentation and broader deployment readiness.
February 2026
5 Commits • 2 Features
Feb 1, 2026February 2026 performance summary for the dice-group/dice-embeddings repository. Focus areas included stability, maintainability, and verifiability of continual learning workflows. Key deliveries span enhancements to continual learning (evaluator integration, backward-compatible data loading) and updated documentation clarifying the continual training process and configuration reuse. The month also strengthened testing coverage with regression tests for continual learning using adaptive SWA and periodic evaluation, along with notes on threading stability for the CoKE model. Overall, these efforts reduced regression risk, improved reliability of production pipelines, and accelerated iteration cycles. Technologies demonstrated include Python, PyTorch, continual learning workflows, SWA, regression testing, threading considerations, and comprehensive documentation.
5 Commits • 2 Features
Feb 1, 2026February 2026 performance summary for the dice-group/dice-embeddings repository. Focus areas included stability, maintainability, and verifiability of continual learning workflows. Key deliveries span enhancements to continual learning (evaluator integration, backward-compatible data loading) and updated documentation clarifying the continual training process and configuration reuse. The month also strengthened testing coverage with regression tests for continual learning using adaptive SWA and periodic evaluation, along with notes on threading stability for the CoKE model. Overall, these efforts reduced regression risk, improved reliability of production pipelines, and accelerated iteration cycles. Technologies demonstrated include Python, PyTorch, continual learning workflows, SWA, regression testing, threading considerations, and comprehensive documentation.
February 2026
November 2025
5 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for the dice-group/dice-embeddings project. Delivered comprehensive Weight Averaging enhancements (SWA, ASWA, TWA, SWAG) with focused improvements to evaluation and training workflows, along with robust documentation and distributed-training safeguards. These changes improve model reliability, reproducibility, and experimentation speed, driving clearer performance signals for product and research teams.
November 2025
5 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for the dice-group/dice-embeddings project. Delivered comprehensive Weight Averaging enhancements (SWA, ASWA, TWA, SWAG) with focused improvements to evaluation and training workflows, along with robust documentation and distributed-training safeguards. These changes improve model reliability, reproducibility, and experimentation speed, driving clearer performance signals for product and research teams.
September 2025
20 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for repo dice-group/dice-embeddings: Delivered a unified weight averaging framework (SWA, SWAG, EMA, and TWA) with core methods, sampling/configuration options, documentation, and regression tests to improve training stability, convergence, and performance. Implemented C-steps weight averaging and spacing for TWA sampling to enhance stability and sample efficiency. Fixed minor SWA-G errors and expanded regression tests and sanity checks for WA approaches, increasing reliability. Updated documentation and configuration to enable repeatable, production-ready experiments. Impact: Strengthened model generalization, faster iteration cycles, and more reliable embedding performance across experiments. Facilitated onboarding and production readiness through comprehensive tests and clear guidance.
20 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for repo dice-group/dice-embeddings: Delivered a unified weight averaging framework (SWA, SWAG, EMA, and TWA) with core methods, sampling/configuration options, documentation, and regression tests to improve training stability, convergence, and performance. Implemented C-steps weight averaging and spacing for TWA sampling to enhance stability and sample efficiency. Fixed minor SWA-G errors and expanded regression tests and sanity checks for WA approaches, increasing reliability. Updated documentation and configuration to enable repeatable, production-ready experiments. Impact: Strengthened model generalization, faster iteration cycles, and more reliable embedding performance across experiments. Facilitated onboarding and production readiness through comprehensive tests and clear guidance.
September 2025
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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Quality Metrics
Correctness87.0%
Maintainability86.0%
Architecture84.4%
Performance79.8%
AI Usage24.0%
Skills & Technologies
Programming Languages
C++JSONJinjaJupyter NotebookMarkdownPythonSQL
Technical Skills
Argument ParsingBackend DevelopmentBatch ProcessingCUDACallback FunctionsCallback ImplementationCode CleanupCode ConsolidationCode FormattingCode LintingCode OrganizationCode ReadabilityCode RefactoringConfigurationConfiguration Management
Repositories Contributed To
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