4 entries
I explored how embeddings cluster by visualizing LLM-generated words across different categories. The visualizations helped build intuition about how these embeddings relate to each other in vector space. Most of the code was generated using Sonnet.
I've been doing some experimentation with smaller models and embeddings, including distilbert/distilbert-base-uncased-finetuned-sst-2-english and cardiffnlp/twitter-roberta-base-sentiment-latest as binary sentiment classifiers and google/vit-base-patch16-224 as an image classifier.
I spent yesterday and today working through the excellent guide by Alex on using sqlite-vss to do vector similarity search in a SQLite database. I'm particularly interested in the benefits one can get from having these tools available locally for getting better insights into non-big datasets with a...
The Alexandria Index is building embeddings for large, public data sets, to make them more searchable and accessible.