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---
license: cc-by-sa-4.0
task_categories:
- text-retrieval
- question-answering
language:
- en
tags:
- legal
- law
size_categories:
- n<1K
source_datasets:
- reglab/barexam_qa
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: 117
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_examples: 116
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_examples: 117
configs:
- config_name: default
data_files:
- split: test
path: data/default.jsonl
- config_name: corpus
data_files:
- split: corpus
path: data/corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: data/queries.jsonl
pretty_name: Bar Exam QA MTEB Benchmark
---
# Bar Exam QA MTEB Benchmark 🏋
This is the test split of the [Bar Exam QA](https://huggingface.co/datasets/reglab/barexam_qa) dataset formatted in the [Massive Text Embedding Benchmark (MTEB)](https://github.com/embeddings-benchmark/mteb) information retrieval dataset format.
This dataset is intended to facilitate the consistent and reproducible evaluation of information retrieval models on Bar Exam QA with the [`mteb`](https://github.com/embeddings-benchmark/mteb) embedding model evaluation framework.
More specifically, this dataset tests the ability of information retrieval models to identify legal provisions relevant to US bar exam questions.
This dataset has been processed into the MTEB format by [Isaacus](https://isaacus.com/), a legal AI research company.
## Methodology 🧪
To understand how Bar Exam QA was created, refer to its [documentation](https://huggingface.co/datasets/reglab/barexam_qa).
This dataset was formatted by concatenating the `prompt` and `question` columns of the source data delimited by a single space (or, where there was no `prompt`, reverting to the `question` only) into queries (or anchors), and treating the `gold_passage` column as relevant (or positive) passages.
## Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises three splits, `default`, `corpus` and `queries`.
The `default` split pairs queries (`query-id`) with relevant passages (`corpus-id`), each pair having a `score` of 1.
The `corpus` split contains relevant passages from Bar Exam QA, with the text of a passage being stored in the `text` key and its id being stored in the `_id` key.
The `queries` split contains queries, with the text of a query being stored in the `text` key and its id being stored in the `_id` key.
## License 📜
To the extent that any intellectual property rights reside in the contributions made by Isaacus in formatting and processing this dataset, Isaacus licenses those contributions under the same license terms as the source dataset. You are free to use this dataset without citing Isaacus.
The source dataset is licensed under [CC BY SA 4.0](https://choosealicense.com/licenses/cc-by-sa-4.0/).
## Citation 🔖
```bibtex
@inproceedings{Zheng_2025, series={CSLAW ’25},
title={A Reasoning-Focused Legal Retrieval Benchmark},
url={http://dx.doi.org/10.1145/3709025.3712219},
DOI={10.1145/3709025.3712219},
booktitle={Proceedings of the Symposium on Computer Science and Law on ZZZ},
publisher={ACM},
author={Zheng, Lucia and Guha, Neel and Arifov, Javokhir and Zhang, Sarah and Skreta, Michal and Manning, Christopher D. and Henderson, Peter and Ho, Daniel E.},
year={2025},
month=mar, pages={169–193},
collection={CSLAW ’25},
eprint={2505.03970}
}
``` |