Update README.md

README.md

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 ---
-license: cc-by-4.0
+annotations_creators:
+- crowdsourced
+language:
+- en
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+paperswithcode_id: vasr
+pretty_name: VASR
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- commonsense-reasoning
+- visual-reasoning
+task_ids: []
+extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files."
 ---
+# Dataset Card for VASR
+- [Dataset Description](#dataset-description)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-vasr-evaluation-with-clip)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+## Dataset Description
+VASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy).
+- **Homepage:** 
+https://vasr-dataset.github.io/
+- **Colab**
+https://colab.research.google.com/drive/1HUg0aHonFDK3hVFrIRYdSEfpUJeY-4dI
+- **Repository:**
+https://github.com/vasr-dataset/vasr/tree/main/experiments
+- **Paper:**
+NA
+- **Leaderboard:**
+https://vasr-dataset.github.io/
+- **Point of Contact:**
+yonatanbitton1@gmail.com 
+### Supported Tasks and Leaderboards
+https://vasr.github.io/leaderboard. 
+https://paperswithcode.com/dataset/vasr. 
+## Colab notebook code for Winogavil evaluation with CLIP
+https://colab.research.google.com/drive/1HUg0aHonFDK3hVFrIRYdSEfpUJeY-4dI
+### Languages
+English. 
+## Dataset Structure
+### Data Fields
+A: datasets.Image() - the first input image, **A**:A'  
+A': datasets.Image() - the second input image, different from A' in a single key, A:**A'**  
+B: datasets.Image() - the third input image, has the same different item as A, **B**:B'  
+B': datasets.Image() - the forth image, which is the analogy solution. Different from B' in a single key (the same different one as in A:A'),  B:**B'**  
+candidates_images: [datasets.Image()] - a list of candidate images solutions to the analogy  
+label: datasets.Value("int64") - the index of the ground-truth solution  
+candidates: [datasets.Value("string")] - a list of candidate string solutions to the analogy  
+A_verb: datasets.Value("string") - the verb of the first input image A  
+A'_verb: datasets.Value("string") - the verb of the second input image A'  
+B_verb: datasets.Value("string") - the verb of the third input image B  
+B'_verb: datasets.Value("string") - the verb of the forth image, which is the analogy solution  
+diff_item_A: datasets.Value("string") - FrameNet key of the item that is different between **A**:A', in image A (which is the same as image B)  
+diff_item_A_str_first: datasets.Value("string") - String representation of the FrameNet key of the item that is different between **A**:A', in image A  
+diff_item_A': datasets.Value("string") - FrameNet key of the item that is different between A:**A'**, in image A' (which is the same as image B')  
+diff_item_A'_str_first: datasets.Value("string") - String representation of the FrameNet key of the item that is different between A:**A'**, in image A'  
+
+### Data Splits
+There are three splits, TRAIN, VALIDATION, and TEST.  
+Since there are four candidates and one solution, random chance is 25%. 
+
+## Dataset Creation
+
+We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies.
+There are two types of labels: 
+- Silver labels, obtained from the automatic generation.
+- Gold labels, obtained from human annotations over the silver annotations.
+
+In the huggingface version we provide only the gold labeled dataset. Please refer to the project website download page if you want to download the silver labels version. 
+
+### Annotations
+
+#### Annotation process
+
+We paid Amazon Mechanical Turk Workers to solve analogies, five annotators for each analogy.
+Workers were asked to select the image that best solves the analogy. 
+The resulting dataset is composed of the 3,820 instances agreed upon with a majority vote of at least 3 annotators, which was obtained in 93% of the cases.
+
+## Considerations for Using the Data
+
+All associations were obtained with human annotators. 
+
+### Licensing Information
+
+CC-By 4.0 
+
+### Citation Information
+
+NA
+