Update README.md

This is a diagnostic dataset for visual logical learning. It consists of 2D images of trains, where each train is either heading east- or westbound. The trains carry multiple wagons, which are composed of multiple properties. The task is to predict the direction of the train. The dataset is designed to test the ability of machine learning models to learn logical rules from complex visual input.

README.md

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 ---
 license: mit
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- vlol
+- v-lol
+- ILP
+- visual logical learning
+- visual reasoning
+- logic
+- Inductive logic programming
+- Symbolic AI
+pretty_name: V-LoL
+size_categories:
+- 10K<n<100K
 ---
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** https://sites.google.com/view/v-lol/home
+- **Repository:** https://github.com/ml-research/vlol-dataset-gen
+- **Paper:** https://arxiv.org/abs/2306.07743
+- **Point of Contact:** lukashenrik.helff@stud.tu-darmstadt.de, wolfgang.stammer@cs.tu-darmstadt.de
+
+### 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).
+
+### 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
+
+## Dataset Structure
+
+### Data Instances
+
+Please refer to our paper.
+
+### Data Fields
+
+Please refer to our paper.
+
+### Data Splits
+
+train(80%)/test(20%) split
+
+## Dataset Creation
+
+### Curation Rationale
+
+Please refer to our paper.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Please refer to our paper.
+
+#### Who are the source language producers?
+
+Please refer to our paper.
+
+### Annotations
+
+#### Annotation process
+
+Please refer to our paper.
+
+#### Who are the annotators?
+
+Please refer to our paper.
+
+### Personal and Sensitive Information
+
+Please refer to our paper.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Please refer to our paper.
+
+### Discussion of Biases
+
+Please refer to our paper.
+
+### Other Known Limitations
+
+Please refer to our paper.
+
+## Additional Information
+
+### Dataset Curators
+
+Please refer to our paper.
+
+### Licensing Information
+
+Please refer to our paper.
+
+### Citation Information
+
+Please refer to our paper.
+
+### Contributions
+
+Please refer to our paper.