Update README.md

README.md

@@ -28,13 +28,22 @@ size_categories:
 
 ### Dataset Summary
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This diagnostic dataset is specifically designed to evaluate the visual logical learning capabilities of machine learning models.
+It offers a seamless integration of visual and logical challenges, providing 2D images of complex visual trains,
+where the classification is derived from underlying logical rules.
+Moreover, we provide a flexible dataset generator that empowers researchers to easily exchange or modify the logical rules,
+thereby enabling the creation of new datasets and novel logical learning challenges.
+By combining visual input with logical reasoning,this dataset serves as a comprehensive benchmark for assessing the ability
+of machine learning models to learn and apply logical reasoning within a visual context.
 
 ### Supported Tasks and Leaderboards
 
 (i)   Theory X: The train has either a short, closed car or a car with a barrel load is somewhere behind a car with a golden vase load. This rule was originally introduced as "Theory X" in the new East-West Challenge.
+
 (ii)  Numerical rule: The train has a car where its car position equals its number of payloads which equals its number of wheel axles.
+
 (iii) Complex rule: Either, there is a car with a car number which is smaller than its number of wheel axles count and smaller than the number of loads, or there is a short and a long car with the same colour where the position number of the short car is smaller than the number of wheel axles of the long car, or the train has three differently coloured cars. We refer to Tab. 3 in the supp. for more insights on required reasoning properties for each rule.
+
 ### Languages
 
 English
@@ -43,7 +52,8 @@ English
 
 ### Data Instances
 
-Please refer to our paper.
+We provide different dataset challenging AI for different kind of abilities. These challenges ranging from different logical rules, to different visualizations,
+as well as differing attribute distributions. 
 
 ### Data Fields