Ivan Palomaras Carrascosa tries a few things:
In this article, you will learn how Bag-of-Words, TF-IDF, and LLM-generated embeddings compare when used as text features for classification and clustering in scikit-learn.
Topics we will cover include:
- How to generate Bag-of-Words, TF-IDF, and LLM embeddings for the same dataset.
- How these representations compare on text classification performance and training speed.
- How they behave differently for unsupervised document clustering.
Click through for results. Granted, the specific embedding model can alter the quality of results, but even so, I do enjoy the comparison of techniques and the reminder that neural networks aren’t the ultimate solution to everything.