Datasets:

ppbrown
/

tokenspace

@@ -1,94 +1,18 @@
-# tokenspace directory
-This directory contains utilities for the purpose of browsing the
-"token space" of CLIP ViT-L/14
-Primary tools are:
-*  "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.

+This directory specializes in the Google T5 xxl LLM.
+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
+* 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)
+* 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
+* showtokens.py
+Given a word, or string of words, shows how T5 tokenizes it