metadata
dataset_info:
features:
- name: anchor
dtype: string
- name: positives
sequence: string
splits:
- name: test
num_bytes: 328652680
num_examples: 12298
- name: train
num_bytes: 2640473967
num_examples: 98380
- name: validation
num_bytes: 328604134
num_examples: 12297
download_size: 1182778947
dataset_size: 3297730781
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
language:
- de
This is mirror of the GerDaLIR dataset formatted as pairs of (anchor, positive).
The German Dataset for Legal Information Retrieval (GerDaLIR) is a legal information retrieval dataset comprising a large collection of documents, passages and relevance labels. The large amount of training data we provide enables GerDaLIR to be used as a downstream task for German or multilingual language models. The task provided is a precedent retrieval task based on case documents from the open legal information platform Open Legal Data. Relevance labels are derived from references: If a passage contains a reference to one or more available documents, the passage is used as a query while the referenced cases are labelled as relevant.
Citation
@inproceedings{wrzalik-krechel-2021-gerdalir,
title = "{G}er{D}a{LIR}: A {G}erman Dataset for Legal Information Retrieval",
author = "Wrzalik, Marco and
Krechel, Dirk",
editor = "Aletras, Nikolaos and
Androutsopoulos, Ion and
Barrett, Leslie and
Goanta, Catalina and
Preotiuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.13/",
doi = "10.18653/v1/2021.nllp-1.13",
pages = "123--128",
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."
}