Update README.md

README.md

@@ -27,10 +27,14 @@ This classifier is intended to predict the existence of social values from text
 10. tradition
     
 ## Datasets
-This model is finetuned on two datasets: ValueNet and Touche23-ValueEval
+This model is finetuned on two datasets: ValueNet (A New Dataset for Human Value Driven Dialogue System, Qiu et al. 2021) and Touche23-ValueEval (The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments, Mirzakhmedova et al., 2023).
+
 We follow the original paper to convert both datasets into a binary classification task for each dimension.
-- ValueNet: a sentence has a positive label if the original label contains 1 (positive) or -1 (negative), and 0 if the original label is 0.
-- ValueEval: a sentence is assigned a positive label if the original label vector is marked 1 for that dimension. Since the original paper follows a 20-dimension refined categorization, we map them back to 10 dimensions. Therefore, the same sentence appears ten times, once for each dimension.
+- ValueNet
+  - A sentence has a positive label if the original label contains 1 (positive) or -1 (negative), and 0 if the original label is 0.
+- ValueEval
+  - A sentence is assigned a positive label if the original label vector is marked 1 for that dimension.
+  - Since the original paper follows a 20-dimension refined categorization, we map them back to 10 dimensions. Therefore, the same sentence appears ten times, once for each dimension.
 
 ## How to use
 Start your sentence with a label that indicates which dimension to measure. An example would be:
@@ -40,10 +44,15 @@ Start your sentence with a label that indicates which dimension to measure. An e
 Please make sure to follow the exact format "<value\_name>" at the beginning of the sentence as this is a special token in the tokenizer: any spaces or different formats will not be encoded correctly.
 
 ## Performances
-- F1 score (macro)
-  - ValueNet only: 0.648
-  - ValueEval only: 0.744
+- macro F1 score
+  - on ValueNet: 0.648
+  - on ValueEval: 0.744
   - Combined: 0.759
+  
+- ROC-AUC
+  - on ValueNet: 0.736
+  - on ValueEval:0.847
+  - Combined: 0.855
 
 ## Training details
 - Base model: bert-base-uncased
@@ -53,4 +62,4 @@ Please make sure to follow the exact format "<value\_name>" at the beginning of
 - Upsampled training set to maintain 1:1 balance for pos:neg labels
 
 ## References
-Do Differences in Values Influence Disagreements in Online Discussions? (EMNLP'23) [link](https://aclanthology.org/2023.emnlp-main.992/)
+- Do Differences in Values Influence Disagreements in Online Discussions? (EMNLP'23) [link](https://aclanthology.org/2023.emnlp-main.992/)