GerDaLIR

An MTEB dataset

Massive Text Embedding Benchmark

GerDaLIR is a legal information retrieval dataset created from the Open Legal Data platform.

Task category	t2t
Domains	Legal
Reference	https://github.com/lavis-nlp/GerDaLIR

Source datasets:

• jinaai/ger_da_lir

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("GerDaLIR")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.

@inproceedings{wrzalik-krechel-2021-gerdalir,
  abstract = {We present GerDaLIR, a German Dataset for Legal Information Retrieval based on case documents from the open legal information platform Open Legal Data. The dataset consists of 123K queries, each labelled with at least one relevant document in a collection of 131K case documents. We conduct several baseline experiments including BM25 and a state-of-the-art neural re-ranker. With our dataset, we aim to provide a standardized benchmark for German LIR and promote open research in this area. Beyond that, our dataset comprises sufficient training data to be used as a downstream task for German or multilingual language models.},
  address = {Punta Cana, Dominican Republic},
  author = {Wrzalik, Marco  and
Krechel, Dirk},
  booktitle = {Proceedings of the Natural Legal Language Processing Workshop 2021},
  month = nov,
  pages = {123--128},
  publisher = {Association for Computational Linguistics},
  title = {{G}er{D}a{LIR}: A {G}erman Dataset for Legal Information Retrieval},
  url = {https://aclanthology.org/2021.nllp-1.13},
  year = {2021},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("GerDaLIR")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 22203,
        "number_of_characters": 209071412,
        "documents_text_statistics": {
            "total_text_length": 196457325,
            "min_text_length": 150,
            "average_text_length": 19706.823653325308,
            "max_text_length": 427234,
            "unique_texts": 9969
        },
        "documents_image_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 12614087,
            "min_text_length": 150,
            "average_text_length": 1031.0680889324833,
            "max_text_length": 23560,
            "unique_texts": 12234
        },
        "queries_image_statistics": null,
        "relevant_docs_statistics": {
            "num_relevant_docs": 14320,
            "min_relevant_docs_per_query": 1,
            "average_relevant_docs_per_query": 1.1705084191597188,
            "max_relevant_docs_per_query": 9,
            "unique_relevant_docs": 9969
        },
        "top_ranked_statistics": null
    }
}

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This dataset card was automatically generated using MTEB