---
pretty_name: GDPR Holdings Retrieval
task_categories:
- text-retrieval
- text-ranking
tags:
- legal
- law
- judicial
- regulatory
- eu
- european
- gdpr
source_datasets:
- GDPRHub
language:
- en
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license: cc-by-nc-sa-4.0
size_categories:
- n<1K
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: 500
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: corpus
    num_examples: 500
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: queries
    num_examples: 500
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
---
# GDPR Holdings Retrieval 🔐
**GDPR Holdings Retrieval** by [Isaacus](https://isaacus.com/) is a novel and challenging legal information retrieval evaluation dataset consisting of 500 [fact patterns](https://en.wikipedia.org/wiki/Fact_pattern) paired with [holdings](https://en.wikipedia.org/wiki/Holding_(law)) in European regulatory and court decisions.

This dataset is intended to stress test the ability of an information retrieval model to retrieve relevant judicial and regulatory decisions given arbitrary fact patterns.

This dataset forms part of the [Massive Legal Embeddings Benchmark (MLEB)](https://isaacus.com/mleb), the largest, most diverse, and most comprehensive benchmark for legal text embedding models. 

## Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises three splits, `default`, `corpus` and `queries`.

The `default` split pairs fact patterns (`query-id`) with relevant holding summaries (`corpus-id`), each pair having a `score` of 1.

The `corpus` split contains holding summaries, with the text of such summaries being stored in the `text` key and their ids 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 fact patterns, with the text of such fact patterns being stored in the `text` key and their ids being stored in the `_id` key.

## Methodology 🧪
This dataset was constructed by collecting all [GDPRHub](https://gdprhub.eu/index.php?title=Welcome_to_GDPRhub) articles, using regex to separate their facts and holdings sections and then converting those sections into plain text with [Inscriptis](https://github.com/weblyzard/inscriptis), sampling 500 pairs for inclusion in this dataset.

## License 📜
This dataset is licensed under the same license as [GDPRHub](https://gdprhub.eu/index.php?title=Welcome_to_GDPRhub), [CC BY NC SA 4.0](https://choosealicense.com/licenses/cc-by-nc-sa-4.0/), which permits non-commercial use only of this dataset as long as appropriate attribution is made and derivative works are issued under the same license.

## Citation 🔖
If you use this dataset, please cite the [Massive Legal Embeddings Benchmark (MLEB)](https://arxiv.org/abs/2510.19365):
```bibtex
@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}, 
}
```