Update README.md

README.md

@@ -25,6 +25,23 @@ While there are countermeasures to avoid bad actors, due to the open nature of t
 However due to the vast amount of other valid ratings, they overarching trend should be towards accuracy. 
 Nevertheless, if you notice any ratings which are obviously malicious, or users which are consistently fake-rating, please let us know and we'll clear them from this dataset.
 
+# Structure
+
+The columns in the dataset are as follows
+
+* ratings_count: How many times this image has been rated throughout this dataset
+* rating: The aesthetic (1-10) rating.
+* kudos: The amount of kudos (i.e. priority) the user had at the moment of rating this image. Higher values represent users who have positively contributed to the AI Horde. This can be used to discover bad actors.
+* account_age: How old the user account is. This can be used to discover bad actors.
+* usage_requests: How many images this user has generated at the moment of rating this image. This can be used to discover bad actors.
+* created_at: When this rating was added
+* client_agent: The client which was used to provide this rating. Unknown clients are more suspicious. This can be used to discover bad actors.
+* artifacts: The artifacts (0-5) rating.
+* user_id: The hashed user id who provided this rating
+* trusted: If true, this user has been trusted by the horde by generating images or text for others for a long amount of time.
+* validated: If true, this user's ratings have been manually validated by one of the AI Horde moderators.
+* captchas_failed: How many captchas this user has failed. This can be used to discover bad actors.
+* country: From which country did the rating originate. This can be used to create location-based rating models.
 
 # Use cases