Update swiss judgment prediction (#5042)

* updated swiss_judgment_prediction dataset with new data

* fixed some problems

* Update datasets/swiss_judgment_prediction/README.md

Co-authored-by: Albert Villanova del Moral <[email protected]>

* simplified code

* ran make style

* simplified code

* updated dummy data and dataset card and simplified code

* added dummy_data and updated dataset_infos.json

* removed unnecessary variable

* added new citation

* Update README.md

Co-authored-by: Albert Villanova del Moral <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>

Commit from https://github.com/huggingface/datasets/commit/7ee558fd5eb2520185891fd0b6683de60a3c381e

README.md

@@ -245,6 +245,19 @@ Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffent
 }
 ```
 
+and the new citation
+
+```
+@misc{niklaus2022empirical,
+    title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
+    author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
+    year={2022},
+    eprint={2209.12325},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
 ### Contributions
 
 Thanks to [@joelniklaus](https://github.com/joelniklaus) for adding this dataset.

swiss_judgment_prediction.py

@@ -30,7 +30,16 @@ _CITATION = """\
   booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},
   year = {2021},
   location = {Punta Cana, Dominican Republic},
-}"""
+}
+@misc{niklaus2022empirical,
+    title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
+    author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
+    year={2022},
+    eprint={2209.12325},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
 
 _DESCRIPTION = """
 Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.