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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: instruction |
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dtype: string |
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- name: output |
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dtype: string |
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- name: task |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 8972956600 |
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num_examples: 503698 |
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- name: validation |
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num_bytes: 1259708059 |
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num_examples: 71638 |
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download_size: 4925396868 |
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dataset_size: 10232664659 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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# Lawma fine-tuning dataset |
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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. |
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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 |
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even the best models leave much room for improvement. From a substantive legal perspective, efficient |
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solutions to such classification problems have rich and important applications in legal research. |
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This dataset was created for the project |
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*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* |
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Please cite as: |
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``` |
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@misc{dominguezolmedo2024lawmapowerspecializationlegal, |
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title={Lawma: The Power of Specialization for Legal Tasks}, |
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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}, |
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year={2024}, |
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eprint={2407.16615}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2407.16615}, |
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} |
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``` |
