Update README.md

README.md

@@ -56,12 +56,14 @@ marking 'supporting evidence' for the label, following how the task is defined b
 all the words, in the sentence, they think shows
 evidence for their chosen label.
 
-#### Our annotations
+#### >Our annotations:
 negative        1555 | 
 positive        1435 | 
 no sentiment     470   
 Total           3460
 
+Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
+
 
 ### SST2
 
@@ -81,12 +83,14 @@ sentiment that they do not see.
 *The Zuco data contains eye-tracking data for 400 instances from SST. By annotating some of these with rationales,
 we add an extra layer of information for future research. 
 
-#### Our annotations
+#### >Our annotations:
 positive        1027 | 
 negative         900 | 
 no sentiment     163   
 Total           2090
 
+Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
+
 
 ### CoS-E
 
@@ -105,9 +109,11 @@ think that removing it will decrease your
 confidence toward your chosen label,
 please mark it.’
 
-#### Our annotations
+#### >Our annotations:
 Total           3760
 
+Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
+
 
 ### Dataset Sources
 
@@ -116,15 +122,16 @@ Total           3760
 - **Repository:** https://github.com/terne/Being_Right_for_Whose_Right_Reasons
 - **Paper:** [Being Right for Whose Right Reasons?](https://aclanthology.org/2023.acl-long.59/)
 
-## Uses
+<a id="uses">## Uses</a> 
 
 <!-- Address questions around how the dataset is intended to be used. -->
 In our paper, we present a collection of three
 existing datasets (SST2, DynaSent and Cos-E) with demographics-augmented annotations to enable profiling of models, i.e., quantifying their alignment (or agreement) with rationales provided
 by different socio-demographic groups. Such profiling enables us to ask whose right reasons models are being right for and fosters future research on performance equality/robustness.
 
-For each dataset, we provide the data under a unique **'test'** split, as its original itended used was to test quality & alignment of post-hoc explainability methods. 
-If you use it following a different split, please clarify it to ease reproducibility of your work.
+For each dataset, we provide the data under a unique **'train'** split due to the current limitation of not being possible to upload a dataset with a single *'test'* split.
+Note, however, that the original itended used of these collection of datasets was to **test** the quality & alignment of post-hoc explainability methods. 
+If you use it following different splits, please clarify it to ease reproducibility of your work.
 
 
 ## Dataset Structure