Update README.md

README.md

@@ -18,6 +18,8 @@ To model this threshold effectively, we opted for the tanh activation function,
 
 Utilizing this approach, we demonstrated improvements in regression tasks for evaluating stances on each test scenario. While the overall MSE did not show significant improvement, we observed higher accuracy, recall, and precision for the regression tasks. It is important to note that the classification task specified in Qiu et al. (2022) solely determines the presence or absence of the value in question, without considering the specific stance presented in the text. Therefore, our regression task, which assesses the particular stance, should not be directly compared with the classification task from Qiu et al. (2022).
 
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 ## Usage
 
 ```python
@@ -120,6 +122,8 @@ values stance.
 
 We would like to acknowledge the authors of the ValueNet dataset for their valuable contribution to this work.
 
+Please give them credit if you use this model, because this model would not be possible without their work.
+
 ```bibtex
 @article{Qiu_Zhao_Li_Lu_Peng_Gao_Zhu_2022, 
     title={ValueNet: A New Dataset for Human Value Driven Dialogue System}, 
@@ -136,6 +140,17 @@ We would like to acknowledge the authors of the ValueNet dataset for their valua
 }
 
 
+If you like my model; please give me credit:
+
+@misc{DeBERTa_Values_Model,
+  author = {Nicholas Harrell},
+  title = {DeBERTa Schwartz's Values Classification and Regression},
+  year = {2024},
+  howpublished = {\url{https://huggingface.co/nharrel/Valuesnet_DeBERTa_v3}},
+  note = {Accessed: YYYY-MM-DD}
+}
+
+