DrAlzahraniProjects/csusb_fall2024_cse6550_team4
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVPython
Technical Skills
Data CleaningData LoadingData ManagementData PreprocessingDataset ManagementDataset Organization
Zabheen
PROFILE
Zabheen
Zabheen worked on foundational data tooling for the DrAlzahraniProjects/csusb_fall2024_cse6550_team4 repository, focusing on text classification and chatbot experiments. He developed a Python-based pipeline to load CSV data, organize it into structured text and label fields, and generate NeMo-compatible JSON datasets, ensuring reproducible preprocessing for downstream machine learning tasks. Zabheen also established the groundwork for chatbot data handling by preprocessing conversational data and generating embeddings using the NeMo BERT model. Additionally, he improved codebase maintainability by removing obsolete scripts and sample data, demonstrating a methodical approach to data management, preprocessing, and embedding generation within a collaborative research environment.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
210
Activity Months1
Your Network
7 people
Work History
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance: Delivered foundational data tooling and cleanup for text classification and chatbot experiments in repo DrAlzahraniProjects/csusb_fall2024_cse6550_team4. Key contributions include the dataset labeling and organization pipeline for text classification using NeMo, groundwork for chatbot data preprocessing and embeddings with a NeMo BERT model, and a cleanup pass removing obsolete Nemo dataset scripts and sample data to reduce clutter and prepare the space for new tooling. These efforts established structured JSON-based datasets, reproducible preprocessing/embedding pipelines, and a leaner codebase, accelerating model development and ensuring data quality for downstream experiments.
4 Commits • 2 Features
Nov 1, 2024November 2024 performance: Delivered foundational data tooling and cleanup for text classification and chatbot experiments in repo DrAlzahraniProjects/csusb_fall2024_cse6550_team4. Key contributions include the dataset labeling and organization pipeline for text classification using NeMo, groundwork for chatbot data preprocessing and embeddings with a NeMo BERT model, and a cleanup pass removing obsolete Nemo dataset scripts and sample data to reduce clutter and prepare the space for new tooling. These efforts established structured JSON-based datasets, reproducible preprocessing/embedding pipelines, and a leaner codebase, accelerating model development and ensuring data quality for downstream experiments.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Data CleaningData LoadingData ManagementData PreprocessingDataset ManagementDataset OrganizationEmbedding GenerationMachine LearningNatural Language ProcessingNeMo LibraryText Classification
Repositories Contributed To
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