---
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}, 
}
```