README.md

---
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](mailto:gila@darrow.ai),[Gil Semo](mailto:gil.semo@darrow.ai)

#### 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
```python
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},
}
```