Datasets:

mteb
/

NeuCLIR2022Retrieval

annotations_creators:
  - expert-annotated
language:
  - fas
  - rus
  - zho
license: odc-by
multilinguality: multilingual
source_datasets:
  - mteb/neuclir-2022
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: 8275313314
        num_examples: 2232016
    download_size: 3816764641
    dataset_size: 8275313314
  - config_name: fas-qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: test
        num_bytes: 1850588
        num_examples: 34174
    download_size: 1309881
    dataset_size: 1850588
  - config_name: fas-queries
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 18728
        num_examples: 114
    download_size: 10483
    dataset_size: 18728
  - config_name: rus-corpus
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: test
        num_bytes: 14911638472
        num_examples: 4627543
    download_size: 7487737882
    dataset_size: 14911638472
  - config_name: rus-qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: test
        num_bytes: 1785174
        num_examples: 33006
    download_size: 1267429
    dataset_size: 1785174
  - config_name: rus-queries
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 19123
        num_examples: 114
    download_size: 11434
    dataset_size: 19123
  - config_name: zho-corpus
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: test
        num_bytes: 6534387728
        num_examples: 3179209
    download_size: 4519787754
    dataset_size: 6534387728
  - config_name: zho-qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: test
        num_bytes: 1979946
        num_examples: 36575
    download_size: 1402756
    dataset_size: 1979946
  - config_name: zho-queries
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 9128
        num_examples: 114
    download_size: 7663
    dataset_size: 9128
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

NeuCLIR2022Retrieval

An MTEB dataset

Massive Text Embedding Benchmark

The task involves identifying and retrieving the documents that are relevant to the queries.


Task category	t2t
Domains	News, Written
Reference	https://neuclir.github.io/

Source datasets:

mteb/neuclir-2022

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("NeuCLIR2022Retrieval")
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

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

import mteb

task = mteb.get_task("NeuCLIR2022Retrieval")

desc_stats = task.metadata.descriptive_stats

{}

This dataset card was automatically generated using MTEB