Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- fas
- rus
- zho
license: odc-by
multilinguality: multilingual
source_datasets:
- mteb/neuclir-2022
- mteb/neuclir-2022-hard-negatives
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: fas-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 45461798
num_examples: 8882
download_size: 20898283
dataset_size: 45461798
- config_name: fas-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1819700
num_examples: 33602
download_size: 1287816
dataset_size: 1819700
- config_name: fas-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 7230
num_examples: 45
download_size: 5358
dataset_size: 7230
- config_name: rus-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 39011856
num_examples: 8724
download_size: 19444719
dataset_size: 39011856
- config_name: rus-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1741164
num_examples: 32191
download_size: 1235301
dataset_size: 1741164
- config_name: rus-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 7372
num_examples: 44
download_size: 5705
dataset_size: 7372
- config_name: zho-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 30798747
num_examples: 10325
download_size: 21047555
dataset_size: 30798747
- config_name: zho-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1863152
num_examples: 34394
download_size: 1318698
dataset_size: 1863152
- config_name: zho-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3730
num_examples: 47
download_size: 4166
dataset_size: 3730
configs:
- config_name: fas-corpus
data_files:
- split: test
path: fas-corpus/test-*
- config_name: fas-qrels
data_files:
- split: test
path: fas-qrels/test-*
- config_name: fas-queries
data_files:
- split: test
path: fas-queries/test-*
- config_name: rus-corpus
data_files:
- split: test
path: rus-corpus/test-*
- config_name: rus-qrels
data_files:
- split: test
path: rus-qrels/test-*
- config_name: rus-queries
data_files:
- split: test
path: rus-queries/test-*
- config_name: zho-corpus
data_files:
- split: test
path: zho-corpus/test-*
- config_name: zho-qrels
data_files:
- split: test
path: zho-qrels/test-*
- config_name: zho-queries
data_files:
- split: test
path: zho-queries/test-*
tags:
- mteb
- text
The task involves identifying and retrieving the documents that are relevant to the queries. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct.
| Task category | t2t |
| Domains | News, Written |
| Reference | https://neuclir.github.io/ |
Source datasets:
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("NeuCLIR2022RetrievalHardNegatives")
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.
@article{lawrie2023overview,
author = {Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas W and Soldaini, Luca and Yang, Eugene},
journal = {arXiv preprint arXiv:2304.12367},
title = {Overview of the TREC 2022 NeuCLIR track},
year = {2023},
}
@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("NeuCLIR2022RetrievalHardNegatives")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB