metadata
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6809516
num_examples: 2919
download_size: 2948742
dataset_size: 6809516
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
splits:
- name: train
num_bytes: 1054
num_examples: 56
download_size: 2081
dataset_size: 1054
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8645
num_examples: 50
download_size: 6492
dataset_size: 8645
configs:
- config_name: corpus
data_files:
- split: train
path: corpus/train-*
- config_name: qrels
data_files:
- split: train
path: qrels/train-*
- config_name: queries
data_files:
- split: train
path: queries/train-*
language:
- ar
tags:
- information-retrieval
- nanobeir
- benchmark
NanoSciFact - Arabic
This dataset is the Arabic version of the NanoSciFact benchmark from the NanoBEIR multilingual collection.
Dataset Origin
This dataset is derived from lightonai/nanobeir-multilingual.
NanoBEIR is a smaller version of the BEIR benchmark designed for efficient evaluation of information retrieval models.
Dataset Structure
The dataset contains three configurations:
- corpus: The document collection to search through
- queries: The search queries
- qrels: Relevance judgments (query-document pairs with relevance scores)
Usage
from datasets import load_dataset
# Load the different configurations
corpus = load_dataset("wissamantoun/NanoSciFact_Arabic", "corpus")
queries = load_dataset("wissamantoun/NanoSciFact_Arabic", "queries")
qrels = load_dataset("wissamantoun/NanoSciFact_Arabic", "qrels")
Citation
If you use this dataset, please cite the original BEIR and NanoBEIR work.