📅 2025-02-18 — Session: Developed Modular Data Retrieval Scripts with FAISS
🕒 14:20–16:10
🏷️ Labels: FAISS, Hugging Face, Rag Model, Summarization, Retrieval
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement advanced summarization and retrieval techniques using state-of-the-art models and frameworks.
Key Activities
- Discussed and compared extractive vs. abstractive summarization methods, focusing on their application in projects.
- Reviewed generative summarization techniques, including model architectures and fine-tuning methods.
- Explored the RAG model for document retrieval, detailing its retriever component and fine-tuning options.
- Built a quote finder using the RAG model, covering dataset preparation and retrieval querying.
- Addressed handling large text collections with FAISS and DPR, emphasizing scalability and memory requirements.
- Created a Hugging Face Dataset with FAISS indexing, including embedding computation and dataset saving.
- Corrected FAISS index argument usage and resolved saving errors in Hugging Face datasets.
- Developed a modular script structure for data processing and retrieval, focusing on preprocessing, embedding, loading, and querying.
- Enhanced retrieval accuracy in FAISS by refining embedding models and normalizing data.
Achievements
- Successfully implemented a modular approach for data processing scripts using Hugging Face and FAISS.
- Corrected and optimized FAISS index handling and dataset saving processes.
Pending Tasks
- Further exploration of abstractive summarization techniques for specific project needs.
- Continuous improvement of retrieval accuracy with FAISS by experimenting with different embedding models and similarity measures.