license: gpl-3.0
task_categories:
- text-classification
- zero-shot-classification
language:
- en
tags:
- legal
- legalnlp
- class action
- darrow
Dataset Description
- Homepage: https://www.darrow.ai/
- Repository: https://github.com/darrow-labs/ClassActionPrediction
- Paper: https://arxiv.org/abs/2211.00582
- Leaderboard: N/A
- Point of Contact: Gila Hayat,Gil Semo
More Details & Collaborations
Feel free to contact us in order to get a larger dataset. We would be happy to collaborate on future works.
Dataset Summary
USClassActions is an English dataset of 3K 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. The data was annotated using Darrow.ai proprietary tool.
Data Instances
from datasets import load_dataset
dataset = load_dataset('darrow-ai/USClassActions')
Data Fields
id: (int) a unique identifier of the document target_text: (str) the complaint text verdict: (str) the outcome of the case \
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,
author = {Semo, Gil
and Bernsohn, Dor
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, EMNLP2022},
}