Update README.md

README.md

@@ -129,32 +129,6 @@ dataset_info:
     num_examples: 300
   download_size: 8209981
   dataset_size: 12245318.0
-- config_name: real
-  features:
-  - name: image
-    dtype: image
-  - name: url
-    dtype: string
-  - name: instance_name
-    dtype: string
-  - name: date_scrapped
-    dtype: string
-  - name: uuid
-    dtype: string
-  - name: category
-    dtype: string
-  - name: additional_info
-    dtype: string
-  - name: assets
-    sequence: string
-  - name: difficulty
-    dtype: string
-  splits:
-  - name: validation
-    num_bytes: 496779.0
-    num_examples: 50
-  download_size: 496792
-  dataset_size: 496779.0
 - config_name: table
   features:
   - name: structure
@@ -230,18 +204,10 @@ configs:
   data_files:
   - split: validation
     path: equation/validation-*
-- config_name: figure
-  data_files:
-  - split: validation
-    path: figure/validation-*
 - config_name: plot
   data_files:
   - split: validation
     path: plot/validation-*
-- config_name: real
-  data_files:
-  - split: validation
-    path: real/validation-*
 - config_name: table
   data_files:
   - split: validation
@@ -261,22 +227,19 @@ tags:
 - statistics
 ---
 
-# Image2Structure - Latex
+# Image2Struct - Latex
+[Paper](TODO) | [Website](https://crfm.stanford.edu/helm/image2structure/latest/) | Datasets ([Webpages](https://huggingface.co/datasets/stanford-crfm/i2s-webpage), [Latex](https://huggingface.co/datasets/stanford-crfm/i2s-latex), [Music sheets](https://huggingface.co/datasets/stanford-crfm/i2s-musicsheet)) | [Leaderboard](https://crfm.stanford.edu/helm/image2structure/latest/#/leaderboard) | [HELM repo](https://github.com/stanford-crfm/helm) | [Image2Struct repo](https://github.com/stanford-crfm/image2structure)
 
 ## Dataset description
-We introduce Image2Structure, a dataset to evaluate the capabilities of multimodel models to learn the structure of a document. This subdataset focuses on Latex code. The model is given an image of the expected output with the prompt:
+Image2struct is a benchmark for evaluating vision-language models in practical tasks of extracting structured information from images.
+This subdataset focuses on LaTeX code. The model is given an image of the expected output with the prompt:
 ```Prease provide the LaTex code used to generate this image. Only generate the code relevant to what you see. Your code will be surrounded by all the imports necessary as well as the begin and end document delimiters.```
-An additional message is added if assets are available:
-```
-The following assets can be used:
- - assets/cs/1_log.png
- - ...
- ```
- The assets are provided under the assets folder for each subject. The subjects were collected on ArXiv and are: eess, cs, stat, math, physics, econ, q-bio, q-fin.
+
+ The subjects were collected on ArXiv and are: eess, cs, stat, math, physics, econ, q-bio, q-fin.
  
- The dataset is divided in 5 categories:
+ The dataset is divided into 5 categories. There are 4 categories that are collected automatically using the [Image2Struct repo](https://github.com/stanford-crfm/image2structure):
 * equations
-* figures
 * tables
 * algorithms
-* code
+* code
+And the last category: wild, was collecting by taking screenshots of equations in the Wikipedia page of "equation" and it's related pages.