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README.md

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-This directory specializes in the Google T5 xxl LLM.
+# tokenspace directory
 
-Specifically, it focuses on 
-    https://huggingface.co/mcmonkey/google_t5-v1_1-xxl_encoderonly/
-because that is the one most used by AI generative models at the moment
+This directory contains utilities for the purpose of browsing the
+"token space" of CLIP ViT-L/14
 
-* dictionary.T5.fullword
-This file is filtered from the full list of tokens in this model
-(generated by dumptokens.py), 
-and has only the tokens that are full standalone words
-(and are also plain English ascii. Sorry, fancy languages)
+Primary tools are:
 
-* dictionary.both
-Words that are common across CLIP-L and T5. Generated by
-    sort dictionary.T5.fullword ../dictionary.fullword |uniq -c |awk '$1 =="2"{print $2}' >dict.both
+*  "calculate-distances.py": allows command-line browsing of words and their neighbours
+*  "graph-embeddings.py": plots graph of full values of two embeddings
+
+
+## (clipmodel,cliptextmodel)-calculate-distances.py
+
+Loads the generated embeddings, reads in a word, calculates "distance" to every
+embedding, and then shows the closest "neighbours".
+
+To run this requires the files "embeddings.safetensors" and "dictionary",
+in matching format
+
+You will need to rename or copy appropriate files for this as mentioned below.
+
+Note that SD models use cliptextmodel, NOT clipmodel
+
+## graph-textmodels.py
+
+Shows the difference between the same word, embedded by CLIPTextModel
+vs CLIPModel
+
+## graph-embeddings.py
+
+Run the script. It will ask you for two text strings. 
+Once you enter both, it will plot the graph and display it for you
+
+Note that this tool does not require any of the other files; just that you 
+have the requisite python modules installed. (pip install -r requirements.txt)
+
+### embeddings.safetensors
+
+You can either copy one of the provided files, or generate your own.
+See generate-embeddings.py for that. 
+
+Note that you muist always use the "dictionary" file that matchnes your embeddings file
+
+### embeddings.allids.safetensors
+
+DO NOT USE THIS ONE for programs that expect a matching dictionary.
+This one is purely numeric based. 
+Its intention is more for research datamining, but it does have a matching
+graph front end, graph-byid.py
+
+
+### dictionary
+
+Make sure to always use the dictionary file that matches your embeddings file.
+
+The "dictionary.fullword" file is pulled from fullword.json, which is distilled from "full words"
+present in the ViT-L/14 CLIP model's provided token dictionary, called "vocab.json".
+Thus there are only around 30,000 words in it
+
+If you want to use the provided "embeddings.safetensors.huge" file, you will want to use the matching
+"dictionary.huge" file, which has over 300,000 words
+
+This huge file comes from the linux "wamerican-huge" package, which delivers it under
+/usr/share/dict/american-english-huge
+
+There also exists a "american-insane" package
+
+
+## generate-embeddings.py
+
+Generates the "embeddings.safetensor" file, based on the "dictionary" file present.
+Takes a few minutes to run, depending on size of the dictionary
+
+The shape of the embeddings tensor, is
+ [number-of-words][768]
+
+Note that yes, it is possible to directly pull a tensor from the CLIP model,
+using keyname of  text_model.embeddings.token_embedding.weight
+
+This will NOT GIVE YOU THE RIGHT DISTANCES!
+Hence why we are calculating and then storing the embedding weights actually
+generated by the CLIP process
+
+
+## fullword.json
+
+This file contains a collection of "one word, one CLIP token id" pairings.
+The file was taken from vocab.json, which is part of multiple SD models in huggingface.co
+
+The file was optimized for what people are actually going to type as words.
+First all the non-(/w) entries were stripped out.
+Then all the garbage punctuation and foreign characters were stripped out.
+Finally, the actual (/w) was stripped out, for ease of use.
 
-* showtokens.py
-Given a word, or string of words, shows how T5 tokenizes it