metadata

annotations_creators:
  - expert-annotated
language:
  - eng
license: cc-by-4.0
multilinguality: monolingual
source_datasets:
  - mteb/dbpedia
task_categories:
  - text-retrieval
task_ids:
  - topic-classification
dataset_info:
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: train
        num_bytes: 2304628
        num_examples: 6045
    download_size: 1475758
    dataset_size: 2304628
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: train
        num_bytes: 69904
        num_examples: 1158
    download_size: 23311
    dataset_size: 69904
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_bytes: 2723
        num_examples: 50
    download_size: 3303
    dataset_size: 2723
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-*
tags:
  - mteb
  - text

NanoDBPediaRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

NanoDBPediaRetrieval is a small version of the standard test collection for entity search over the DBpedia knowledge base.


Task category	t2t
Domains	Encyclopaedic
Reference	https://huggingface.co/datasets/zeta-alpha-ai/NanoDBPedia

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["NanoDBPediaRetrieval"])
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 repitory.

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{lehmann2015dbpedia,
  author = {Lehmann, Jens and et al.},
  journal = {Semantic Web},
  title = {DBpedia: A large-scale, multilingual knowledge base extracted from Wikipedia},
  year = {2015},
}


@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, Lo{\"\i}c 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("NanoDBPediaRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "train": {
        "num_samples": 6095,
        "number_of_characters": 2034629,
        "num_documents": 6045,
        "min_document_length": 1,
        "average_document_length": 336.30669975186106,
        "max_document_length": 1390,
        "unique_documents": 6045,
        "num_queries": 50,
        "min_query_length": 8,
        "average_query_length": 33.1,
        "max_query_length": 63,
        "unique_queries": 50,
        "none_queries": 0,
        "num_relevant_docs": 1158,
        "min_relevant_docs_per_query": 1,
        "average_relevant_docs_per_query": 23.16,
        "max_relevant_docs_per_query": 81,
        "unique_relevant_docs": 1146,
        "num_instructions": null,
        "min_instruction_length": null,
        "average_instruction_length": null,
        "max_instruction_length": null,
        "unique_instructions": null,
        "num_top_ranked": null,
        "min_top_ranked_per_query": null,
        "average_top_ranked_per_query": null,
        "max_top_ranked_per_query": null
    }
}

This dataset card was automatically generated using MTEB