metadata

annotations_creators:
  - expert-annotated
language:
  - eng
license: cc-by-4.0
multilinguality: monolingual
source_datasets:
  - mteb/scidocs
task_categories:
  - text-retrieval
task_ids: []
dataset_info:
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: train
        num_bytes: 2158511
        num_examples: 2210
    download_size: 1267281
    dataset_size: 2158511
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: train
        num_bytes: 23424
        num_examples: 244
    download_size: 14147
    dataset_size: 23424
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_bytes: 6041
        num_examples: 50
    download_size: 6573
    dataset_size: 6041
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

NanoSCIDOCSRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

NanoFiQA2018 is a smaller subset of SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation.


Task category	t2t
Domains	Academic, Written, Non-fiction
Reference	https://allenai.org/data/scidocs

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(["NanoSCIDOCSRetrieval"])
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.


@inproceedings{specter2020cohan,
  author = {Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
  booktitle = {ACL},
  title = {SPECTER: Document-level Representation Learning using Citation-informed Transformers},
  year = {2020},
}


@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("NanoSCIDOCSRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "train": {
        "num_samples": 2260,
        "number_of_characters": 2044730,
        "num_documents": 2210,
        "min_document_length": 0,
        "average_document_length": 923.5705882352942,
        "max_document_length": 10000,
        "unique_documents": 2210,
        "num_queries": 50,
        "min_query_length": 38,
        "average_query_length": 72.78,
        "max_query_length": 143,
        "unique_queries": 50,
        "none_queries": 0,
        "num_relevant_docs": 244,
        "min_relevant_docs_per_query": 3,
        "average_relevant_docs_per_query": 4.88,
        "max_relevant_docs_per_query": 5,
        "unique_relevant_docs": 236,
        "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