📅 2025-02-18 — Session: Explored and Implemented Summarization and Retrieval Techniques
🕒 14:20–16:10
🏷️ Labels: Summarization, RAG, FAISS, Hugging Face, Retrieval, Embeddings
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to explore various summarization techniques and implement retrieval-augmented generation models for document retrieval and processing large text collections.
Key Activities
- Explored extractive vs. abstractive summarization methods and generative summarization techniques.
- Detailed the use of the RAG model for document retrieval, including dataset preparation and querying.
- Built a quote finder using the RAG model’s retrieval component.
- Discussed handling large text collections with FAISS and DPR, focusing on scalability and memory requirements.
- Created a Hugging Face Dataset with FAISS indexing, including embedding computation and dataset saving.
- Corrected FAISS index handling and resolved related errors in Hugging Face datasets.
- Developed a modular script structure for data processing and retrieval.
- Improved retrieval accuracy in FAISS by refining embedding models and using semantic similarity.
Achievements
- Successfully implemented and corrected FAISS index handling in datasets.
- Developed a robust modular script for data processing and retrieval.
Pending Tasks
- Further explore and refine semantic ranking strategies for improved retrieval accuracy.
- Continue to optimize the performance of summarization and retrieval models.