convert to handle dictionary format

.gitattributes

@@ -54,3 +54,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 embeddings.safetensors.fullword filter=lfs diff=lfs merge=lfs -text
+embeddings.safetensors.huge filter=lfs diff=lfs merge=lfs -text

README.md

@@ -12,17 +12,40 @@ which allows command-line browsing of words and their neighbours
 Loads the generated embeddings, calculates a full matrix
 of distances between all tokens, and then reads in a word, to show neighbours for.
 
-To run this requires the files "embeddings.safetensors" and "fullword.json"
+To run this requires the files "embeddings.safetensors" and "dictionary"
 
+You will need to rename or copy appropriate files for this as mentioned below
+
+### 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
+
+### 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. Takes a few minutes to run.
+Generates the "embeddings.safetensor" file, based on the "dictionary" file present.
+Takes a few minutes to run, depending on size of the dictionary
 
-Basically goes through the fullword.json file, and 
-generates a standalone embedding for each word.
-Shape of the embeddings tensor, is
+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,
@@ -32,11 +55,6 @@ 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
 
-## embeddings.safetensors
-
-Data file generated by generate-embeddings.py
-
-
 
 ## fullword.json
 

embeddings.safetensors.huge

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a848df65f451f2d1ae45484f3ad3751e18e8b5b160b107964bdf71a11f96c934
+size 1070450784

generate-distances.py

@@ -14,46 +14,88 @@ import json
 import torch
 from safetensors import safe_open
 
+from transformers import CLIPProcessor,CLIPModel
+
+clipsrc="openai/clip-vit-large-patch14"
+processor=None
+model=None
+
+device=torch.device("cuda")
+
+
+def init():
+    global processor
+    global model
+    # Load the processor and model
+    print("loading processor from "+clipsrc,file=sys.stderr)
+    processor = CLIPProcessor.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+    print("loading model from "+clipsrc,file=sys.stderr)
+    model = CLIPModel.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+
+    model = model.to(device)
+
+
+
 embed_file="embeddings.safetensors"
 
 device=torch.device("cuda")
 
-print("read in words from json now",file=sys.stderr)
-with open("fullword.json","r") as f:
-    tokendict = json.load(f)
-wordlist = list(tokendict.keys())
+print("read in words from dictionary now",file=sys.stderr)
+with open("dictionary","r") as f:
+    tokendict = f.readlines()
+    wordlist = [token.strip() for token in tokendict]  # Remove trailing newlines
+print(len(wordlist),"lines read")
 
 print("read in embeddings now",file=sys.stderr)
-
 model = safe_open(embed_file,framework="pt",device="cuda")
 embs=model.get_tensor("embeddings")
 embs.to(device)
 print("Shape of loaded embeds =",embs.shape)
 
-# ("calculate distances now")
-distances = torch.cdist(embs, embs, p=2)
-print("distances shape is",distances.shape)
+def standard_embed_calc(text):
+    if processor == None:
+        init()
+
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+    with torch.no_grad():
+        text_features = model.get_text_features(**inputs)
+    embedding = text_features[0]
+    return embedding
+
+
+def print_distances(targetemb):
+    targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2)
+
+    print("shape of distances...",targetdistances.shape)
+
+    smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False)
+
+    smallest_distances=smallest_distances.tolist()
+    smallest_indices=smallest_indices.tolist()
+    for d,i in zip(smallest_distances,smallest_indices):
+        print(wordlist[i],"(",d,")")
+
+
 
 # Find 10 closest tokens to targetword.
 # Will include the word itself
 def find_closest(targetword):
     try:
         targetindex=wordlist.index(targetword)
-    except ValueError:
-        print(targetword,"not found")
+        targetemb=embs[targetindex]
+        print_distances(targetemb)
         return
+    except ValueError:
+        print(targetword,"not found in cache")
 
-    #print("index of",targetword,"is",targetindex)
-    targetdistances=distances[targetindex]
 
-    smallest_distances, smallest_indices = torch.topk(targetdistances, 10, largest=False)
+    print("Now doing with full calc embed")
+    targetemb=standard_embed_calc(targetword)
+    print_distances(targetemb)
 
-    smallest_distances=smallest_distances.tolist()
-    smallest_indices=smallest_indices.tolist()
-    for d,i in zip(smallest_distances,smallest_indices):
-        print(wordlist[i],"(",d,")")
-    #print("The smallest distance values are",smallest_distances)
-    #print("The smallest index values are",smallest_indices)
 
 
 print("Input a word now:")

generate-embeddings.py

@@ -47,14 +47,14 @@ def standard_embed_calc(text):
 
 init()
 
-print("read in words from json now",file=sys.stderr)
-with open("fullword.json","r") as f:
-    tokendict = json.load(f)
+with open("dictionary","r") as f:
+    tokendict = f.readlines()
+    tokendict = [token.strip() for token in tokendict]  # Remove trailing newlines
 
 print("generate embeddings for each now",file=sys.stderr)
 count=1
 all_embeddings = []
-for word in tokendict.keys():
+for word in tokendict:
     emb = standard_embed_calc(word)
     emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
     all_embeddings.append(emb)

requirements.txt

@@ -0,0 +1,3 @@
+torch
+safetensors
+transformers