Update README.md

README.md

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 ---
 license: gpl-3.0
 ---
+## Dataset Description
+
+- **Homepage:** https://www.darrow.ai/
+- **Repository:** https://github.com/darrow-labs/ClassActionPrediction
+- **Paper:** https://arxiv.org/abs/2110.
+- **Leaderboard:** N/A
+- **Point of Contact:** [Gila Hayat](mailto:[email protected])
+
+### Dataset Summary
+
+USClassActions is an English dataset of 200 complaints from the US Federal Court with the respective binarized judgment outcome (Win/Lose). The dataset poses a challenging text classification task. We are happy to share this dataset in order to promote robustness and fairness studies on the critical area of legal NLP.
+
+### Data Instances
+```python
+from datasets import load_dataset
+dataset = load_dataset('darrow-ai/USClassActionOutcomes_ExpertsAnnotations')
+```
+### Data Fields
+`id`: (**int**) a unique identifier of the document \
+`origin_label `: (**str**) the outcome of the case \
+`target_text`: (**str**) the facts of the case \
+`annotator_prediction `: (**str**) annotators predictions of the case outcome based on the target_text \
+`annotator_confidence `: (**str**) the annotator's level of confidence in his outcome prediction \
+
+### Curation Rationale
+
+The dataset was curated by Darrow.ai (2022).
+
+### Citation Information
+
+*Gil Semo, Dor Bernsohn, Ben Hagag, Gila Hayat, and Joel Niklaus*
+*ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US*
+*Proceedings of the 2022 Natural Legal Language Processing Workshop. Abu Dhabi. 2022*
+```
+@InProceedings{darrow-niklaus-2022-uscp,
+  author = {Semo, Gil
+                and Bernsohn, Dor
+                and Stürmer, Matthias
+                and Hagag, Ben
+                and Hayat, Gila
+                and Niklaus, Joel},
+  title = {ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US},
+  booktitle = {Proceedings of the 2022 Natural Legal Language Processing Workshop},
+  year = {2022},
+  location = {Abu Dhabi},
+}
+```