Update README.md

README.md

@@ -63,21 +63,23 @@ English.
 
 ### Data Fields
 
-candidates (string). 
-cue (string). 
-associations (string). 
-score_fool_the_ai (int64). 
-num_associations (int64). 
-annotation_index (int64). 
-num_candidates (int64). 
-solvers_jaccard_mean (float64). 
-solvers_jaccard_std (float64). 
-ID (int64). 
+candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates.  
+cue (string): pogonophile - the generated cue.  
+associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user.  
+score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model. 
+num_associations (int64): 3 - The number of images selected as associative with the cue.  
+num_candidates (int64): 6 - the number of total candidates.  
+solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance.  
+solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance
+ID (int64): 367 - association ID. 
 
 ### Data Splits
-
-  -- 5 & 6 candidates. With 5 candidates, random chance for success is 38%. With 6 candidates, random chance for success is 34%.   
-  -- 10 & 12 candidates. With 10 candidates, random chance for success is 24%. With 12 candidates, random chance for success is 19%.     
+There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set.
+There are different number of candidates, which creates different difficulty levels: 
+  -- With 5 candidates, random chance for success is 38%.  
+  -- With 6 candidates, random chance for success is 34%.     
+  -- With 10 candidates, random chance for success is 24%. 
+  -- With 12 candidates, random chance for success is 19%.     
 
 ## Dataset Creation