📅 2025-02-18 — Session: Refactored Python Code for Metadata Extraction and Classification
🕒 20:40–23:50
🏷️ Labels: Python, Text Classification, AST, CFG, Metadata Extraction
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance Python scripts for metadata extraction and text classification, focusing on improving code efficiency, modularity, and functionality.
Key Activities
- Corrected file extension checks in Python code to ensure proper handling of file types.
- Explored fast text classification techniques, including traditional machine learning models and transformer-based models.
- Developed a systematic approach to text classification, including dataset selection and model training.
- Compiled a list of recommended datasets for text categorization and web data classification.
- Investigated perception layers in deep learning models for feature extraction.
- Discussed AI models for code analysis and explored Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs).
- Implemented Python scripts for generating ASTs and CFGs and addressed related ImportErrors.
- Enhanced function metadata extraction and optimized AST for LLM processing.
- Analyzed and suggested improvements for Python output structure and refactoring plans.
Achievements
- Improved Python code for file handling and metadata extraction.
- Established a framework for fast text classification using Scikit-Learn.
- Enhanced understanding of dataset selection and model architecture for text classification.
- Developed scripts for AST and CFG generation and resolved related errors.
- Improved metadata extraction functions and optimized code structure.