Update README.md

README.md

@@ -24,6 +24,7 @@ extra_gated_prompt: "By clicking on “Access repository” below, you also agre
 # Dataset Card for VASR
 - [Dataset Description](#dataset-description)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [How to Submit Predictions?](#how-to-submit-predictions?)
   - [Colab notebook code for VASR evaluation with ViT](#colab-notebook-code-for-vasr-evaluation-with-clip)
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
@@ -46,10 +47,16 @@ NA
 - **Leaderboard:**
 https://vasr-dataset.github.io/
 - **Point of Contact:**
-yonatanbitton1@gmail.com 
-### Supported Tasks and Leaderboards
+yonatan.bitton@mail.huji.ac.il
+## Supported Tasks and Leaderboards
 https://vasr.github.io/leaderboard. 
 https://paperswithcode.com/dataset/vasr. 
+## Supported Tasks and Leaderboards
+To submit predictions, please send a prediction CSV file to vasr.benchmark@gmail.com / yonatan.bitton@mail.huji.ac.il. 
+The prediction file should include a "B'" column with the predicted candidate name that best solves the analogy, and an index from 1 to 4 indicating the location of the predicted candidate in the given candidate list. 
+An example prediction file is available [HERE](https://drive.google.com/file/d/1NvBNdvlWmEOYjIVi2xdmQ_tUm-TXo42u/view?usp=share_link).
+A submission is allowed once a week, and you will receive a response within a week. 
+
 ## Colab notebook code for VASR evaluation with ViT
 https://colab.research.google.com/drive/1HUg0aHonFDK3hVFrIRYdSEfpUJeY-4dI
 ### Languages
@@ -57,20 +64,12 @@ 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'  
+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'**. Hidden in the test set. 
+candidates_images: [datasets.Image()] - a list of candidate images solutions to the analogy.
+label: datasets.Value("int64") - the index of the ground-truth solution. Hidden in the test set.
+candidates: [datasets.Value("string")] - a list of candidate string solutions to the analogy.
 
 ### Data Splits
 There are three splits, TRAIN, VALIDATION, and TEST.