📅 2025-02-18 — Session: Explored Advanced Text Classification and Summarization Techniques
🕒 17:50–19:50
🏷️ Labels: Text Classification, Summarization, Hugging Face, NLP, Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore advanced techniques in text classification and summarization using Hugging Face models and other NLP tools.
Key Activities
- Reviewed recommended Hugging Face models for text classification, including zero-shot and supervised models.
- Evaluated best models for text classification and topic modeling, considering pretrained classifiers and custom training options.
- Investigated issues and improvements for Pegasus summarization, focusing on parameter tuning and methodology.
- Examined fast large-scale summarization methods using DistilBART and BERT.
- Set up RunPod for GPU-accelerated summarization, detailing dependency installation and script execution.
- Analyzed performance slowdowns in summarization, identifying model downloading as a bottleneck and suggesting optimizations.
- Analyzed BART model performance issues, offering strategies for runtime optimization.
- Reflected on the computational costs of text summarization versus classification, providing insights into optimization strategies.
- Provided guidelines for ideal length ratios in summarization.
Achievements
- Gained insights into effective text classification and summarization models and techniques.
- Identified performance bottlenecks and potential optimizations in summarization processes.
Pending Tasks
- Implement identified optimizations for model downloading and runtime efficiency.
- Further explore custom training options for text classification models.