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---
license: mit
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 8972956600
num_examples: 503698
- name: validation
num_bytes: 1259708059
num_examples: 71638
download_size: 4925396868
dataset_size: 10232664659
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Lawma fine-tuning dataset
This fine-tuning dataset contains 260 legal classification tasks derived from the [Supreme Court](http://scdb.wustl.edu/data.php) and [Songer Court of Appeals](www.songerproject.org/us-courts-of-appeals-databases.html) databases, totalling over 500k training examples and 2B tokens. This dataset was used to train [Lawma 8B](https://huggingface.co/ricdomolm/lawma-8b) and [Lawma 70B](https://huggingface.co/ricdomolm/lawma-70b). The Lawma models outperform GPT-4 on 95\% of these legal tasks, on average by over 17 accuracy points. See our [arXiv preprint](https://arxiv.org/abs/2407.16615) and [GitHub repository](https://github.com/socialfoundations/lawma) for more details.
Our reasons to study these legal classification tasks are both technical and substantive. From a technical machine learning perspective, these tasks provide highly non-trivial classification problems where
even the best models leave much room for improvement. From a substantive legal perspective, efficient
solutions to such classification problems have rich and important applications in legal research.
This dataset was created for the project
*Lawma: The Power of Specizalization for Legal Tasks. Ricardo Dominguez-Olmedo and Vedant Nanda and Rediet Abebe and Stefan Bechtold and Christoph Engel and Jens Frankenreiter and Krishna Gummadi and Moritz Hardt and Michael Livermore. 2024*
Please cite as:
```
@misc{dominguezolmedo2024lawmapowerspecializationlegal,
title={Lawma: The Power of Specialization for Legal Tasks},
author={Ricardo Dominguez-Olmedo and Vedant Nanda and Rediet Abebe and Stefan Bechtold and Christoph Engel and Jens Frankenreiter and Krishna Gummadi and Moritz Hardt and Michael Livermore},
year={2024},
eprint={2407.16615},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.16615},
}
``` |