jrc-ai
/

PreDA-base

@@ -9,6 +9,9 @@ metrics:
 model-index:
 - name: PreDA_t5-base
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -27,27 +30,20 @@ More information needed
 ## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
 ## 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
 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.
-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.
 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
 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
 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
@@ -117,4 +113,4 @@ in annotating dream reports for research purposes, as well as detailing their st
 - Transformers 4.44.2
 - Pytorch 2.1.0+cu118
 - Datasets 3.0.1
-- Tokenizers 0.19.1

 model-index:
 - name: PreDA_t5-base
   results: []
+language:
+- en
+pipeline_tag: text2text-generation
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 ## Intended uses & limitations
+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
 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.
+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, 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
 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
 - Transformers 4.44.2
 - Pytorch 2.1.0+cu118
 - Datasets 3.0.1
+- Tokenizers 0.19.1