dsu-cs/csc702_fall2025
Sep 2025 – Sep 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Code ManagementData CleaningData ScienceGensimMachine LearningNLTK
Will-Crumb
PROFILE
Will-crumb
Will Crumb developed a comprehensive Word Embeddings Notebook for the dsu-cs/csc702_fall2025 repository, enabling end-to-end workflows for training Word2Vec models, performing similarity lookups, comparing models, and merging embeddings from multiple sources. He used Python and Jupyter Notebook to implement these features, leveraging libraries such as Gensim and NLTK for natural language processing tasks. Will also improved project maintainability by scaffolding and cleaning up documentation for Will_Isaac components and removing obsolete directories, which reduced technical debt. His work enhanced research reproducibility and streamlined onboarding, demonstrating a solid grasp of code management, data cleaning, and machine learning best practices.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
2
Lines of code
1,216
Activity Months1
Your Network
25 peopleSame Organization
@trojans.dsu.edu5
Work History
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a Word Embeddings Notebook feature set enabling end-to-end embedding workflows (training Word2Vec, similarity lookups, model comparisons, and cross-source embedding merging); scaffolded and cleaned up documentation for Will_Isaac components to improve maintainability; and removed an obsolete emb_to_words/Will_Isaac directory to reduce technical debt and confusion. The changes improve research reproducibility, onboarding, and project hygiene, with clear value for NLP experimentation pipelines and team workflows. Technologies demonstrated include Python notebooks, Word2Vec workflows, and documentation practices.
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a Word Embeddings Notebook feature set enabling end-to-end embedding workflows (training Word2Vec, similarity lookups, model comparisons, and cross-source embedding merging); scaffolded and cleaned up documentation for Will_Isaac components to improve maintainability; and removed an obsolete emb_to_words/Will_Isaac directory to reduce technical debt and confusion. The changes improve research reproducibility, onboarding, and project hygiene, with clear value for NLP experimentation pipelines and team workflows. Technologies demonstrated include Python notebooks, Word2Vec workflows, and documentation practices.
September 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Code ManagementData CleaningData ScienceGensimMachine LearningNLTKNatural Language ProcessingPyTorchPythonWord Embeddings
Repositories Contributed To
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