THE SUPER EFFECTIVENESS OF POKÉMON EMBEDDINGS USING ONLY RAW JSON AND IMAGES

Summary from the TLDR Web Dev newsletter

Embeddings are numerical representations of objects often used in text analysis and search applications. LLMs like BERT and newer models with larger context windows can generate embeddings for text data, taking into account word position and punctuation for better contextual understanding. These embeddings can be used to identify similar items. This author explores embeddings by encoding Pokemon data, including both text and image data, into embeddings and analyzing their similarities. By combining text and image embeddings and using dimensionality reduction techniques like UMAP, the author was able to visualize and analyze relationships between Pokemon.