Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
German
Size:
10K - 100K
Tags:
text-retrieval
File size: 1,884 Bytes
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---
language:
- de
multilinguality:
- monolingual
task_categories:
- text-retrieval
source_datasets:
- https://github.com/lavis-nlp/GerDaLIR
task_ids:
- document-retrieval
config_names:
- corpus
tags:
- text-retrieval
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: 14320
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_examples: 9969
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_examples: 12234
configs:
- config_name: default
data_files:
- split: test
path: qrels/test.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
**GerDaLIRSmall**
- Original link: https://github.com/lavis-nlp/GerDaLIR
- The dataset consists of documents, passages and relevance labels in German.
- The corpus set consists of a collection of legal documents. In contrast to the original dataset, only documents that have corresponding queries in the query set are chosen to create a smaller corpus for evaluation purposes.
- The query set comprises passages that refer to one or more documents within the corpus set.
**Usage**
```
import datasets
# Download the dataset
queries = datasets.load_dataset("mteb/GerDaLIRSmall", "queries")
documents = datasets.load_dataset("mteb/GerDaLIRSmall", "corpus")
pair_labels = datasets.load_dataset("mteb/GerDaLIRSmall", "default")
``` |