---
title: "Datasets: A Community Library for Natural — Lhoest 2021"
description: "NLP research now relies on many datasets of very different sizes for pretraining, fine-tuning and evaluation."
authors: ["Quentin Lhoest", "Albert Villanova del Moral", "Yacine Jernite", "Abhishek Thakur", "Patrick von Platen", "Suraj Patil", "Julien Chaumond", "Mariama Drame", "Julien Plu", "Lewis Tunstall", "Joe Davison", "Mario Šaško", "Gunjan Chhablani", "Bhavitvya Malik", "Simon Brandeis", "Teven Le Scao", "Victor Sanh", "Canwen Xu", "Nicolas Patry", "Angelina McMillan-Major", "Philipp Schmid", "Sylvain Gugger", "Clément Delangue", "Théo Matussière", "Lysandre Debut", "Stas Bekman", "Pierric Cistac", "Thibault Goehringer", "Victor Mustar", "François Lagunas", "Alexander M. Rush", "Thomas Wolf"]
year: 2021
venue: "arXiv:2109.02846"
source: "https://arxiv.org/abs/2109.02846"
doi: "10.48550/arxiv.2109.02846"
arxiv: "2109.02846"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/"
---
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# Datasets: A Community Library for Natural Language Processing

**Authors:** Quentin Lhoest, [Albert Villanova del Moral](https://md.research.elamur.ai/free/en/authors/albert-villanova-del-moral/), [Yacine Jernite](https://md.research.elamur.ai/free/en/authors/yacine-jernite/), Abhishek Thakur, [Patrick von Platen](https://md.research.elamur.ai/free/en/authors/patrick-von-platen/), Suraj Patil, [Julien Chaumond](https://md.research.elamur.ai/free/en/authors/julien-chaumond/), Mariama Drame, Julien Plu, [Lewis Tunstall](https://md.research.elamur.ai/free/en/authors/lewis-tunstall/), [Joe Davison](https://md.research.elamur.ai/free/en/authors/joe-davison/), Mario Šaško et al. (32 authors) · **Year:** 2021 · **Venue:** arXiv:2109.02846 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2109.02846) · [PDF](https://arxiv.org/pdf/2109.02846) · [DOI 10.48550/arxiv.2109.02846](https://doi.org/10.48550/arxiv.2109.02846) · [arXiv 2109.02846](https://arxiv.org/abs/2109.02846) · [OpenAlex](https://openalex.org/W3213208261)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · [356](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
NLP research now relies on many datasets of very different sizes for pretraining, fine-tuning and evaluation. Each dataset often has its own format, unclear versions and little documentation. Hugging Face's Datasets library addresses this with a single, framework-independent way to load and process any corpus, from a few thousand examples up to internet scale. Volunteer contributors write a builder script for each dataset that fetches the raw files from the original source and converts them into a typed table. The tables are stored with Apache Arrow as memory-mapped on-disk caches, so very large corpora work on modest hardware and pass to NumPy, Pandas, Torch or TensorFlow without copying. A Dataset Hub adds structured tags and data cards that describe how each dataset was built, its licensing and its known biases. After about a year the library had more than 650 datasets and over 250 contributors. It also supported cross-dataset research and shared tasks, showing that a shared, community-documented infrastructure can lower the cost of working with many resources.

## Key points
- Over 650 datasets and more than 250 contributors after roughly one year. The library is Apache 2.0 licensed and started as a fork of TensorFlow-Datasets before diverging substantially.
- Raw data is generally not hosted centrally. It is downloaded from the original authors, with exceptions such as a preprocessed Wikipedia.
- The type system covers class labels, sequences, translation pairs and multidimensional arrays. Batched, multi-process `map` runs cache their results between sessions, and very large datasets can be streamed.
- On the Hub, contributors fill in tags and data cards by hand. For example, filtering for Spanish and Question Answering returned 7 datasets at the time of writing.
- Extra features include:
  - search indexes via FAISS or ElasticSearch, used for the open-source Retrieval-Augmented Generation;
  - versioned metrics such as BLEU and SQuAD;
  - a web viewer for browsing examples from almost all datasets.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

356 papers of our library cite it (of the 140673 whose bibliographies we read); 22 of them have a page here, 334 not yet.

- [UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu](https://md.research.elamur.ai/free/en/papers/adeeba-2026-urblimp-benchmark-evaluating-linguistic-competence/)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/adelani-2022-masakhaner-2-0-africa-centric/)
- [Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets](https://md.research.elamur.ai/free/en/papers/altaher-2022-masader-plus-new-interface-exploring/)
- [Acquiring Linguistic Knowledge from Multimodal Input](https://md.research.elamur.ai/free/en/papers/amariucai-2023-acquiring-linguistic-knowledge-multimodal-input/)
- [PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts](https://md.research.elamur.ai/free/en/papers/bach-2022-promptsource-integrated-development-environment-repository/)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
- [Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models-their/)
- [Editing Models with Task Arithmetic](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
- [The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://md.research.elamur.ai/free/en/papers/lauren-2023-ccon-bigscience-roots-corpus-1-6tb/)
- [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
- [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
- [MTEB: Massive Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
- [SCROLLS: Standardized CompaRison Over Long Language Sequences](https://md.research.elamur.ai/free/en/papers/shaham-2022-scrolls-standardized-comparison-over-long/)
- [Entropy-aware Masking for Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/srinivasagan-2026-entropy-aware-masking-masked-language/)
- [Quality and Appropriateness of Large Text Datasets for Irish NLP](https://md.research.elamur.ai/free/en/papers/walsh-2026-quality-appropriateness-large-text-datasets/)
- [Larger language models do in-context learning differently](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
- [CREPE: Open-Domain Question Answering with False Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lhoest2021datasets,
  title = {{Datasets: A Community Library for Natural Language Processing}},
  author = {Lhoest, Quentin and Moral, Albert Villanova del and Jernite, Yacine and Thakur, Abhishek and Platen, Patrick von and Patil, Suraj and Chaumond, Julien and Drame, Mariama and Plu, Julien and Tunstall, Lewis and Davison, Joe and Šaško, Mario and Chhablani, Gunjan and Malik, Bhavitvya and Brandeis, Simon and Scao, Teven Le and Sanh, Victor and Xu, Canwen and Patry, Nicolas and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Delangue, Clément and Matussière, Théo and Debut, Lysandre and Bekman, Stas and Cistac, Pierric and Goehringer, Thibault and Mustar, Victor and Lagunas, François and Rush, Alexander M. and Wolf, Thomas},
  year = {2021},
  doi = {10.48550/arxiv.2109.02846},
  eprint = {2109.02846},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2109.02846}
}
```

- [← previous CVAE-based Re-anchoring for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/dou-2021-cvae-based-re-anchoring-implicit/)
- [next → DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/2/)

## References

37 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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10. Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, and Gennady Pekhimenko. 2021. Distributed deep learn… [find](https://scholar.google.com/scholar?q=Michael+Diskin%2C+Alexey+Bukhtiyarov%2C+Max+Ryabinin%2C+Lucile+Saulnier%2C+Quentin+Lhoest%2C+Anton+Sinitsin%2C+Dmitry+Popov%2C+Dmitry+Pyrkin%2C+Maxim+Kashirin%2C+Alexander+Borzunov%2C+Albert+Villanova+del+Moral%2C+Denis+Ma)
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