dsu-cs/csc702_fall2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonTOMLTextJupyter Notebook
Technical Skills
BPE TokenizationCode CleanupData AnalysisData AugmentationData EngineeringData Preprocessing
Adam Bradley
PROFILE
Adam Bradley
Adam Bradley developed a data-driven NLP research pipeline for author attribution and sentiment analysis in the dsu-cs/csc702_fall2025 repository, focusing on reproducibility and extensibility. He implemented baseline tokenization using Byte Pair Encoding, data augmentation with nlpaug, and vocabulary analysis for author-based experiments. In October, Adam designed and trained Transformer models for both MNIST image classification and movie review sentiment analysis, integrating PyTorch and spaCy for efficient data preprocessing and model training. His work addressed device alignment issues in validation, improved memory efficiency, and clarified project structure, demonstrating depth in deep learning, data engineering, and natural language processing within a short timeframe.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
25Total
Bugs
2
Commits
25
Features
10
Lines of code
624,379
Activity Months2
Your Network
20 people
Work History
October 2025
7 Commits • 4 Features
Oct 1, 2025October 2025 performance summary for dsu-cs/csc702_fall2025: Implemented end-to-end Transformer-based capabilities for both image classification and sentiment analysis, improved evaluation reliability, and enhanced memory efficiency. Delivered a MNIST-based Transformer groundwork including core components (MultiHeadAttention, PositionWiseFeedForward, PositionalEncoding, EncoderLayer, DecoderLayer), the main Transformer class, and initial training/validation loops, extended to MNIST image classification. Introduced a Transformer-based sentiment analysis model for movie reviews with data loading, preprocessing, model training, and evaluation. Refactored sentiment analysis to spaCy for preprocessing and PyTorch for modeling, addressed memory issues, and tuned the Transformer output layer for binary sentiment classification. Fixed a validation device alignment issue to prevent runtime errors and ensured DataLoader batches keep tensors on the correct device. Improved project clarity and reproducibility with notebook renaming reflecting image classification focus.
7 Commits • 4 Features
Oct 1, 2025October 2025 performance summary for dsu-cs/csc702_fall2025: Implemented end-to-end Transformer-based capabilities for both image classification and sentiment analysis, improved evaluation reliability, and enhanced memory efficiency. Delivered a MNIST-based Transformer groundwork including core components (MultiHeadAttention, PositionWiseFeedForward, PositionalEncoding, EncoderLayer, DecoderLayer), the main Transformer class, and initial training/validation loops, extended to MNIST image classification. Introduced a Transformer-based sentiment analysis model for movie reviews with data loading, preprocessing, model training, and evaluation. Refactored sentiment analysis to spaCy for preprocessing and PyTorch for modeling, addressed memory issues, and tuned the Transformer output layer for binary sentiment classification. Fixed a validation device alignment issue to prevent runtime errors and ensured DataLoader batches keep tensors on the correct device. Improved project clarity and reproducibility with notebook renaming reflecting image classification focus.
October 2025
September 2025
18 Commits • 6 Features
Sep 1, 2025Sep 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a data-driven NLP research pipeline for author attribution, established baseline tokenization and data augmentation, and prepared sentiment analysis scaffolding, embeddings visualization, and reproducible project scaffolding. These efforts accelerate experimentation, improve data quality, and drive measurable business value for author attribution studies.
September 2025
18 Commits • 6 Features
Sep 1, 2025Sep 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a data-driven NLP research pipeline for author attribution, established baseline tokenization and data augmentation, and prepared sentiment analysis scaffolding, embeddings visualization, and reproducible project scaffolding. These efforts accelerate experimentation, improve data quality, and drive measurable business value for author attribution studies.
Activity
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Quality Metrics
Correctness80.6%
Maintainability82.4%
Architecture79.2%
Performance74.4%
AI Usage21.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonTOMLText
Technical Skills
BPE TokenizationCode CleanupData AnalysisData AugmentationData EngineeringData LoadingData PreprocessingData ScienceData ValidationData VisualizationDataset ExpansionDeep LearningDependency ManagementDocumentationFile Handling
Repositories Contributed To
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