SPLADE - The First Search Model to Beat BM25

This course teaches about sparse and dense vector search, comparing their differences and advantages, and how the SPLADE model can enhance traditional sparse embedding methods like TF-IDF and BM25. It covers the vocabulary mismatch problem, SPLADE's functioning using transformers, masked language modeling, implementation in Python with PyTorch and Hugging Face, and its usage alongside dense embedding models. The intended audience for this course includes individuals interested in AI-powered search systems and those looking to optimize search and recommendation algorithms.