PriorLabs/tabpfn-extensions
Mar 2025 – Mar 2025
1 Month active
Languages Used
MarkdownPythonShell
Technical Skills
Data ScienceDocumentationMachine LearningPythonScikit-learnTabPFN
6sy666
PROFILE
6sy666
Developed K-fold cross-validation support for TabPFNEmbedding within the PriorLabs/tabpfn-extensions repository, enabling more robust and reproducible embedding extraction and evaluation workflows. Leveraged Python and Scikit-learn to implement the new embedding class, which supports both standard and cross-validated embedding generation for downstream machine learning tasks. Supplemented the feature with comprehensive documentation in Markdown, including a detailed README and end-to-end example scripts for classification and regression. This work improved evaluation reliability and facilitated easier integration of TabPFN embeddings into various data science pipelines, addressing reproducibility and adoption challenges for users working with TabPFN v2 in practical ML scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
231
Activity Months1
Your Network
20 peopleWork History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for PriorLabs/tabpfn-extensions: Delivered K-fold cross-validation support for TabPFNEmbedding in TabPFN v2, enabling robust embedding extraction and evaluation. Added end-to-end example scripts for classification and regression and a comprehensive README detailing usage, vanilla embeddings, and citations. This work enhances evaluation reliability, reproducibility, and ease of adoption for downstream ML tasks. Commit references: e54441b33a3a8a0b8aac28c04a39afc839a592d2; 93a39bc1f2fef971c64f41eddaced61025050326.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for PriorLabs/tabpfn-extensions: Delivered K-fold cross-validation support for TabPFNEmbedding in TabPFN v2, enabling robust embedding extraction and evaluation. Added end-to-end example scripts for classification and regression and a comprehensive README detailing usage, vanilla embeddings, and citations. This work enhances evaluation reliability, reproducibility, and ease of adoption for downstream ML tasks. Commit references: e54441b33a3a8a0b8aac28c04a39afc839a592d2; 93a39bc1f2fef971c64f41eddaced61025050326.
March 2025
Activity
Loading activity data...
Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
Data ScienceDocumentationMachine LearningPythonScikit-learnTabPFN
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline