Update README.md

README.md

@@ -27,27 +27,20 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
+This model is designed for research purposes. See the disclaimer for more details.
 
 ## Training procedure
 
-the overall idea of our approach is to disentangle each dream report from its annotation as a whole and to create an augmented set of (dream report; single 
+The overall idea of our approach is to disentangle each dream report from its annotation as a whole and to create an augmented set of (dream report; single 
 feature annotation). To make sure that, given the same report, the model would produce a specific HVDC feature, we simply append at 
-the beginning of each report a string of the form ``HVDC-Feature :'', in a manner that closely mimics T5 task-specific prefix fine-tuning. 
+the beginning of each report a string of the form ``HVDC-Feature:'', in a manner that closely mimics T5 task-specific prefix fine-tuning. 
 
-After this procedure to the original dataset (\~1.8K) we obtain approximately 6.6K items. %For this work, we focused on six HVDC features, namely Characters, 
-Activities, Emotion, Friendliness, Misfortune, and Good Fortune. We did so to exclude features that amounted to less than 10\% of the total instance. Indeed, 
-this would have excluded Good Fortune (see Figure \ref{fig:train_set_prefix_dist}). We include either way the feature to control for 
-memorisation and counterbalance the Misfortune feature. 
-In the present study, we focused on a subset of six HVDC features: Characters, Activities, Emotion, Friendliness, Misfortune, and Good Fortune. 
+After this procedure to the original dataset (\~1.8K) we obtain approximately 6.6K items. In the present study, we focused on a subset of six HVDC features: 
+Characters, Activities, Emotion, Friendliness, Misfortune, and Good Fortune. 
 This selection was made to exclude features that represented less than 10\% of the total instances. Notably, Good Fortune would have been excluded under this 
-criterion (refer to Figure \ref{fig:train_set_prefix_dist}), but we intentionally retained this feature to control against potential 
+criterion, but we intentionally retained this feature to control against potential 
 memorisation effects and to provide a counterbalance to the Misfortune feature. After filtering out instances whose annotation 
-feature is not one of the six selected features, we are left with \~5.3K %5389 
+feature is not one of the six selected features, we are left with \~5.3K 
 dream reports. We then generate a random split of 80\%-20\% for the training (i.e., 4,311 reports) and testing (i.e. 1,078 reports) sets.
 
 ### Training