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---
license: cc-by-4.0
task_categories:
- text-retrieval
- text-ranking
language:
- en
tags:
- legal
- law
- judicial
size_categories:
- n<1K
source_datasets:
- nguha/legalbench
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: test
num_examples: 120
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_examples: 523
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_examples: 120
configs:
- config_name: default
data_files:
- split: test
path: default.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
pretty_name: SCALR (MLEB version)
---
# SCALR (MLEB version)
This is the version of the [SCALR](https://github.com/lexeme-dev/scalr) evaluation dataset used in the [Massive Legal Embeddings Benchmark (MLEB)](https://isaacus.com/mleb) by [Isaacus](https://isaacus.com/).
This dataset tests the ability of information retrieval models to retrieve relevant legal holdings to complex, reasoning-intensive legal questions.
## Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises three splits, `default`, `corpus` and `queries`.
The `default` split pairs questions (`query-id`) with correct holdings (`corpus-id`), each pair having a `score` of 1.
The `corpus` split contains holdings, with the text of a holding being stored in the `text` key and its id being stored in the `_id` key. There is also a `title` column, which is deliberately set to an empty string in all cases for compatibility with the [`mteb`](https://github.com/embeddings-benchmark/mteb) library.
The `queries` split contains questions, with the text of a question being stored in the `text` key and its id being stored in the `_id` key.
## Methodology 🧪
To understand how SCALR itself was created, refer to its [documentation](https://github.com/lexeme-dev/scalr).
This dataset was formatted by taking the test split of SCALR, splitting it in half (after random shuffling) (to allow for the remainder of the dataset to be used for validation), and treating questions as anchors and correct holdings as positive passages, and adding incorrect holdings to the global passage corpus.
## License 📜
This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
## Citation 🔖
If you use this dataset, please cite the [Massive Legal Embeddings Benchmark (MLEB)](https://arxiv.org/abs/2510.19365) as well.
```bibtex
@misc{guha2023legalbench,
title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models},
author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li},
year={2023},
eprint={2308.11462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{butler2025massivelegalembeddingbenchmark,
title={The Massive Legal Embedding Benchmark (MLEB)},
author={Umar Butler and Abdur-Rahman Butler and Adrian Lucas Malec},
year={2025},
eprint={2510.19365},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.19365},
}
``` |