#Feature Space Explorer

An embedding projector using transformers and matplotlib. Allows you to browse embeddings in a 3D scatterplot. You can zoom in and out, dump values to a pandas-compatible format, &c.

#Usage

1. Check through python src/main.py --help to learn about available options.
2. Pick 2+ ascii documents to compare. Run python src/main.py $text1 $text2 $text3… to start.
3. Explore!

#Notes

• the program will cache embeddings in a JSON file.
• each embedding has four keys, one for the sentence and three for the x, y, z coordinates for the embedding.
• note the program will repack the latter three columns when displaying vectors.

#Example

The above image provides a sense of what one should expect to see with this tool. In it, two books have had their sentences plotted out with GPT-2: one work of fiction and one work of non-fiction. While most of the sentences converge on each other, a clear wedge of sentence extends southwards. Examining this group reveals sentences like chapter labels and page numbers, structural strings whose semantic quality is different than the average sentence in a book. Perhaps not surprising, but certainly nice to see visually.