Update README.md

README.md

@@ -1,3 +1,15 @@
+---
+language: en
+datasets:
+- ValueNet
+tags:
+- regression
+- classification
+- stance detection
+- DeBERTa
+license: mit
+---
+
 ## Study Overview
 
 In this study, we employ the Microsoft DeBERTa v3 model, which introduces an additional embedding for positional indexing, enhancing the model used in Qiu et al. (2022). To date, no model has empirically validated the impact of the positional index on regression or classification tasks. For training and fine-tuning, we developed a custom trainer to evaluate Mean-Squared Error (MSE) as outlined in Qiu et al. (2022). However, the implementation details of the sigmoid activation function with a threshold of \([-1, 1]\)—where \(-1\) indicates a personal stance not aligned with the value in question, \(1\) indicates alignment, and \(0\) denotes neutrality (irrelevance)—were not clearly specified.
@@ -10,6 +22,7 @@ Utilizing this approach, we demonstrated improvements in regression tasks for ev
 
 We would like to acknowledge the authors of the ValueNet dataset for their valuable contribution to this work.
 
+```bibtex
 @article{Qiu_Zhao_Li_Lu_Peng_Gao_Zhu_2022, 
     title={ValueNet: A New Dataset for Human Value Driven Dialogue System}, 
     volume={36}, 
@@ -21,5 +34,5 @@ We would like to acknowledge the authors of the ValueNet dataset for their valua
     author={Qiu, Liang and Zhao, Yizhou and Li, Jinchao and Lu, Pan and Peng, Baolin and Gao, Jianfeng and Zhu, Song-Chun}, 
     year={2022}, 
     month={Jun.}, 
-    pages={11183-11191} 
+    pages={11183-11191}
 }