Update README.md

README.md

@@ -1,5 +1,7 @@
 ---
-license: mit
+license: openrail++
+language:
+- en
 ---
 This is a fine-tuned Deberta model to detect human values in arguments. 
 The model is part of the ensemble that was the best-performing system in the SemEval2023 task: [Detecting Human Values in arguments](https://touche.webis.de/semeval23/touche23-web/index.html)
@@ -51,3 +53,29 @@ for label, prediction in zip(LABEL_COLUMNS, test_prediction):
         continue
     print(f"{label}: {prediction}")
 ```
+
+## Citation
+
+```
+@inproceedings{schroter-etal-2023-adam,
+    title = "{A}dam-Smith at {S}em{E}val-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models",
+    author = "Schroter, Daniel  and
+      Dementieva, Daryna  and
+      Groh, Georg",
+    editor = {Ojha, Atul Kr.  and
+      Do{\u{g}}ru{\"o}z, A. Seza  and
+      Da San Martino, Giovanni  and
+      Tayyar Madabushi, Harish  and
+      Kumar, Ritesh  and
+      Sartori, Elisa},
+    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.semeval-1.74",
+    doi = "10.18653/v1/2023.semeval-1.74",
+    pages = "532--541",
+    abstract = "This paper presents the best-performing approach alias {``}Adam Smith{''} for the SemEval-2023 Task 4: {``}Identification of Human Values behind Arguments{''}. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness ({``}Nahj al-Balagha{''}). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.",
+}
+```