isaacus/echr-retrieval

ECHR Retrieval 🏛️

ECHR Retrieval by Isaacus is a challenging legal information retrieval evaluation dataset consisting of 200 short summaries of findings of European Court of Human Rights decisions paired with the text of those decisions sourced from the HUDOC database.

This dataset is intended to stress test the ability of an information retrieval model to retrieve relevant court decisions given arbitrary legal holdings.

This dataset forms part of the Massive Legal Embeddings Benchmark (MLEB), the largest, most diverse, and most comprehensive benchmark for legal text embedding models. ECHR Retrieval was added to MLEB on 20 February 2026.

Structure 🗂️

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

The default split pairs summaries (query-id) with decisions (corpus-id), each pair having a score of 1.

The corpus split contains European Court of Human Rights decisions, with the text of decisions 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 library.

The queries split contains summaries of the findings of decisions, with the text of summaries being stored in the text key and their ids being stored in the _id key.

Methodology 🧪

This dataset was constructed by collecting all publicly available European Court of Human Rights decisions, cleaning them, and then sampling 200 summary-decision pairs for inclusion in this dataset.

License 📜

This dataset is licensed under CC BY 4.0 which allows for both non-commercial and commercial use of this dataset as long as appropriate attribution is made to it.

Citation 🔖

If you use this dataset, please cite the Massive Legal Embeddings Benchmark (MLEB):

@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}, 
}