📅 2025-02-17 — Session: Developed NLP text categorization pipeline with embeddings
🕒 17:55–18:40
🏷️ Labels: NLP, Text Categorization, Embeddings, Clustering, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to explore and plan the development of a structured approach for text categorization using text embeddings and clustering techniques.
Key Activities:
- Reviewed a structured approach for text labeling and categorization using text embeddings and clustering, including generating embeddings, clustering strategies, and a proposed pipeline for implementation.
- Explored systematic approaches for aggregating micro-knowledge through extraction, grouping, and storage using NLP and clustering techniques.
- Detailed the use of the ‘all-MiniLM-L6-v2’ model from Sentence Transformers for text processing pipelines, including loading, preprocessing, embedding generation, and clustering.
- Addressed dependency conflict resolution in Python by discussing compatible package versions and virtual environments.
- Discussed performance optimization strategies for AI model processing, including model loading, GPU usage, and batching techniques.
- Described a modular architecture for text processing, including embedding generation, graph storage, and search and clustering techniques.
- Explored the definition and structuring of nodes in graph-based knowledge systems, discussing text chunks and abstract entities, and implementing multi-layer graphs.
Achievements:
- Developed a comprehensive plan for a text categorization pipeline using NLP techniques.
- Clarified methods for dependency management and performance optimization in Python.
Pending Tasks:
- Implement the proposed text categorization pipeline.
- Further explore the multi-layer graph implementation for knowledge systems.