Improve dataset card: Add description, task category, and relevant tags

This PR enhances the dataset card by:
- Adding the `image-text-to-text` task category to the metadata.
- Including relevant tags such as `geometry`, `mathematical-reasoning`, and `multimodal`.
- Adding a detailed description of the dataset, explaining its purpose, structure, and usage.
- Linking to the survey paper on Hugging Face and the GitHub repository for more context.

README.md

@@ -1,32 +1,57 @@
----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: class
-    dtype: string
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: option
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: task_class
-    dtype: string
-  - name: Attributes
-    dtype: string
-  - name: image
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 82349062.411
-    num_examples: 1913
-  download_size: 230897223
-  dataset_size: 82349062.411
----
+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: class
+    dtype: string
+  - name: id
+    dtype: string
+  - name: question
+    dtype: string
+  - name: option
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: task_class
+    dtype: string
+  - name: Attributes
+    dtype: string
+  - name: image
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 82349062.411
+    num_examples: 1913
+  download_size: 230897223
+  dataset_size: 82349062.411
+task_categories:
+- image-text-to-text
+tags:
+- geometry
+- mathematical-reasoning
+- multimodal
+---
+
+This dataset is designed for research in **Deep Learning for Geometry Problem Solving (DL4GPS)** and accompanies the survey paper [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936). It aims to provide a structured resource for evaluating and training AI models, particularly multimodal large language models (MLLMs), on mathematical reasoning tasks involving geometric contexts.
+
+The dataset provides a collection of geometry problems, each consisting of a textual question and a corresponding image.
+
+For a continuously updated reading list of papers on Deep Learning for Geometry Problem Solving, refer to the [official GitHub repository](https://github.com/majianz/gps-survey).
+
+## Data Structure
+
+Each problem instance in the dataset includes the following fields:
+
+-   `class`: The category of the geometry problem.
+-   `id`: A unique identifier for each problem.
+-   `question`: The textual description of the geometry problem.
+-   `option`: Multiple-choice options for the answer, if applicable.
+-   `answer`: The correct answer to the geometry problem.
+-   `task_class`: A classification of the task involved.
+-   `Attributes`: Additional attributes or metadata about the problem.
+-   `image`: The image of the geometric diagram associated with the problem.