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Dataset Card for "oscar"

Dataset Summary

OSCAR or Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the ungoliant architecture. Data is distributed by language in both original and deduplicated form.


from datasets import load_dataset

dataset = load_dataset("oscar-corpus/OSCAR-2201",
                        use_auth_token=True, # required
                        streaming=True, # optional
                        split="train") # optional, but the dataset only has a train split

for d in dataset:
    print(d) # prints documents

Supported Tasks and Leaderboards

OSCAR is mainly intended to pretrain language models and word representations.


All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 different languages are available. The table in subsection Data Splits Sample Size provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.


OSCAR 22.01 may have quality issues on low size subcorpora, as it has been the case before.

Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus. As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic.

If you encounter something that is unexpected, please file an issue here:

Language code Language Issues

Dataset Structure

We show detailed information for all the configurations of the dataset.

Data Instances


Data Fields

  • id: a int64 feature.

  • content: string Newline-separated content

  • warc_headers: WARC Headers

  • warc_headers.content-length: int64 Content length (in bytes) before cleaning

  • warc_headers.content-type: string MIME type

  • warc_headers.warc-block-digest:string Algorithm name and calculated value of a digest applied to the full block of the record

  • warc_headers.warc-date: string Crawl date (YYYY-MM-DDThh:mm:ssZ)

  • warc_headers.warc-identified-content-language: string Comma-separated list of language identifications done by CommonCrawl (uses CLD3)

  • warc_headers.warc-record-id: string Record ID

  • warc_headers.warc-refers-to: string Record-ID of a single record for which the present record holds additional content

  • warc_headers.warc-target-uri: string URI from where the content has been fetched

  • warc_headers.warc-type: string Type of the WARC Record

  • metadata: Metadata

  • metadata.identification.label: string Language identification of the document

  • metadata.identification.prob: float Confidence of the identification

  • metadata.annotation: [string] Annnotations of the document. null if none present. (Is None if using datasets)

  • metadata.sentence_identifications: [string] List of line identifications. null/None can be present for lines that failed the identification step.

  • meta.offset: int64 line offset where the related text begins. Should be used with meta.nb_sentences when reading the source files rather than using iterators to get related data.

  • text: string content

See the WARC Format standard for more details on the warc_headers fields, and our website for more details about the format in general.

Data Splits

Click to expand the number of samples per configuration


lang size docs words
Multilingual 12.1 GB 1,210,685 936,187,711
Afrikaans 47.0 MB 12,393 6,227,310
Albanian 3.0 GB 437,287 326,325,149
Alemannic / Swiss German 363.6 kB 139 37,381
Amharic 461.0 MB 37,513 30,481,153
Arabic 84.2 GB 8,718,929 6,103,711,887
Aragonese 10.6 kB 12 51
Armenian 4.7 GB 379,267 268,031,270
Assamese 221.2 MB 17,084 11,109,557
Asturian 73.6 kB 77 3,919
Avaric 18.6 kB 14 582
Azerbaijani 3.5 GB 491,847 291,927,692
Bangla 15.1 GB 1,171,501 751,877,226
Bashkir 95.5 MB 11,198 5,418,474
Basque 1.1 GB 233,658 97,092,942
Belarusian 1.8 GB 180,046 107,227,860
Bihari languages 24.2 kB 27 569
Bishnupriya 2.0 MB 271 98,419
Bosnian 10.3 kB 10 422
Breton 33.7 MB 16,119 3,111,619
Bulgarian 35.1 GB 2,887,115 2,405,981,285
Burmese 1.9 GB 158,733 44,835,970
Catalan 13.9 GB 2,627,307 1,508,919,864
Cebuano 44.6 MB 5,742 5,253,785
Central Kurdish 716.4 MB 84,950 43,913,025
Chechen 14.0 MB 4,086 798,766
Chinese 900.9 GB 56,524,518 23,149,203,886
Chuvash 41.8 MB 4,750 2,465,782
Cornish 1.4 kB 2 55
Croatian 11.2 MB 11,462 505,369
Czech 58.6 GB 10,381,916 5,452,724,456
Danish 12.6 GB 2,265,479 1,454,439,292
Dimli (individual language) 706 Bytes 1 19
Divehi 217.2 MB 24,067 10,112,205
Dutch 114.0 GB 20,206,532 12,329,127,151
Eastern Mari 11.3 MB 1,612 641,525
Egyptian Arabic 2.8 MB 1,256 176,096
English 3.2 TB 431,992,659 377,376,402,775
Esperanto 558.3 MB 111,932 58,416,628
Estonian 9.2 GB 1,362,524 820,975,443
Filipino 646.5 MB 70,394 81,881,278
Finnish 37.8 GB 4,948,961 2,900,615,928
French 382.2 GB 52,037,098 41,713,990,658
Galician 255.2 MB 88,803 27,051,212
Georgian 7.1 GB 488,588 281,430,479
German 496.7 GB 70,075,424 46,826,676,844
Goan Konkani 787.2 kB 46 38,831
Greek 78.3 GB 6,738,546 5,031,242,803
Guarani 9.0 kB 10 374
Gujarati 4.8 GB 136,467 301,170,777
Hebrew 30.3 GB 3,132,396 2,249,377,984
Hindi 23.3 GB 1,529,907 1,534,799,198
Hungarian 53.9 GB 6,866,062 4,598,787,907
Icelandic 2.0 GB 396,183 210,365,124
Ido 77.3 kB 105 2,690
Iloko 97.9 kB 75 8,592
Indonesian 17.4 GB 2,244,622 1,984,195,207
Interlingua 40.2 kB 6 10,125
Irish 45.6 MB 12,233 4,877,850
Italian 229.3 GB 28,502,092 24,294,684,830
Japanese 258.7 GB 36,328,931 5,592,948,356
Javanese 152.7 kB 70 10,441
Kalmyk 9.3 kB 9 250
Kannada 2.6 GB 150,850 108,450,571
Karachay-Balkar 119.6 kB 91 4,089
Kazakh 2.9 GB 261,085 157,267,307
Khmer 1.9 GB 121,910 30,564,131
Komi 119.9 kB 127 3,335
Korean 51.8 GB 5,881,481 3,854,968,649
Kurdish 150.3 MB 29,906 17,390,759
Kyrgyz 518.6 MB 62,244 28,028,986
Lao 337.1 MB 28,914 6,682,982
Latin 4.1 MB 4,397 187,446
Latvian 8.2 GB 1,032,987 707,361,898
Lezghian 375.5 kB 124 19,250
Limburgish 1.4 kB 2 41
Lithuanian 20.0 GB 2,303,070 1,712,802,056
Lojban 1.9 MB 570 260,542
Lombard 2.6 kB 2 225
Low German 9.0 MB 1,938 1,012,561
Lower Sorbian 707 Bytes 1 17
Luxembourgish 15.8 MB 5,108 1,545,946
Macedonian 3.6 GB 341,775 244,058,579
Maithili 21.6 kB 23 483
Malagasy 57.3 MB 3,028 7,279,056
Malay 5.3 MB 5,228 217,818
Malayalam 4.1 GB 250,972 137,831,247
Maltese 2.5 MB 2,208 118,190
Marathi 3.3 GB 250,376 160,179,233
Mazanderani 128.2 kB 76 7,337
Minangkabau 6.0 MB 585 614,613
Mingrelian 7.6 MB 2,550 253,333
Mongolian 2.8 GB 237,719 176,405,432
Nahuatl languages 8.7 kB 12 179
Nepali 3.7 GB 391,947 177,885,116
Newari 5.7 MB 1,134 273,837
Norwegian 2.8 GB 973,188 279,182,902
Norwegian Nynorsk 6.8 MB 5,835 459,183
Occitan 2.1 MB 373 31,061
Odia 487.9 MB 52,942 23,755,902
Ossetic 13.9 MB 3,560 800,430
Pashto 490.3 MB 50,312 46,293,249
Persian 77.4 GB 7,665,871 6,430,164,396
Piedmontese 1.7 MB 698 188,270
Polish 139.0 GB 19,301,137 12,584,498,906
Portuguese 170.3 GB 23,735,707 18,441,864,893
Punjabi 1.1 GB 68,094 70,068,604
Quechua 744 Bytes 1 14
Romanian 49.2 GB 4,624,764 5,261,803,995
Russia Buriat 32.9 kB 39 785
Russian 1.1 TB 76,060,844 62,811,122,663
Sakha 65.6 MB 6,284 3,473,813
Sanskrit 136.0 MB 4,472 5,671,369
Scottish Gaelic 137.7 kB 136 7,769
Serbian 6.9 GB 577,472 482,932,670
Serbian (Latin) 931.8 kB 738 92,875
Sicilian 1.5 kB 2 50
Sindhi 117.1 MB 15,516 10,685,611
Sinhala 2.0 GB 108,593 113,179,741
Slovak 16.5 GB 2,409,555 1,619,121,944
Slovenian 1.2 GB 351,894 118,400,246
Somali 2.1 kB 3 109
South Azerbaijani 14.1 MB 5,381 693,746
Spanish 381.9 GB 51,386,247 42,829,835,316
Sundanese 5.0 MB 263 547,145
Swahili 1.3 MB 462 123,050
Swedish 48.0 GB 7,541,278 5,078,331,128
Tajik 870.9 MB 46,366 56,627,727
Tamil 11.4 GB 556,772 452,343,748
Tatar 915.3 MB 76,398 51,875,265
Telugu 3.4 GB 249,756 137,752,065
Thai 66.1 GB 5,030,254 1,626,779,846
Tibetan 234.5 MB 18,683 2,286,269
Turkish 75.1 GB 10,826,031 6,421,221,358
Turkmen 4.4 MB 2,485 276,632
Ukrainian 48.8 GB 4,558,214 2,879,585,992
Emiliano-Romagnolo[eml] 901 Bytes 1 53
Upper Sorbian 132.8 kB 110 8,825
Urdu 3.4 GB 336,994 332,816,354
Uyghur 201.9 MB 18,556 11,240,889
Uzbek 19.9 MB 9,526 1,370,842
Vietnamese 98.9 GB 9,587,233 12,283,185,482
Volapük 825.9 kB 661 57,039
Walloon 105.7 kB 138 4,386
Waray 7.6 MB 933 830,872
Welsh 409.3 MB 90,378 49,488,495
Western Frisian 75.3 MB 21,946 6,357,929
Western Mari 743.5 kB 155 43,916
Western Panjabi 46.7 MB 6,790 4,060,419
Wu Chinese 137.2 kB 88 3,056
Yiddish 232.5 MB 23,418 15,809,780
Yoruba 24.7 kB 26 1,042

Dataset Creation

Curation Rationale

OSCAR was constructed using Ungoliant, a new pipeline derived from goclassy, itself being derived from fastText's one.

The pipeline works on documents rather than lines. Ungoliant is implemented in the Rust programming language, and uses rayon as its data parallelism strategy. Threading is done at shard, record and sentence level, making the whole generation process much more efficient.

Filtering will be explained in a future blog post at our website

Source Data

Initial Data Collection and Normalization

Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and robots.txt policies.

Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.

To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR 22.01, the November/December 2021 snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.

Who are the source language producers?

The data comes from multiple web pages in a large variety of languages.


The dataset does not contain any additional annotations.

Annotation process


Who are the annotators?


Personal and Sensitive Information

Being constructed from Common Crawl, Personal and sensitive information might be present. This must be considered before training deep learning models with OSCAR, specially in the case of text-generation models.

Considerations for Using the Data

Social Impact of Dataset

OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.

Discussion of Biases

OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.

Other Known Limitations

The fastText linear classifier is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by third parties.

Additional Information

Dataset Curators

The corpus was put together by Julien Abadji, Pedro Ortiz Suarez, Benoît Sagot, and Laurent Romary, during work done at Inria, particularly at the ALMAnaCH team.

Licensing Information

These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved")
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.

Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.

We will comply to legitimate requests by removing the affected sources from the next release of the corpus.

Citation Information

       author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
        title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language},
         year = 2022,
        month = jan,
          eid = {arXiv:2201.06642},
        pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
       eprint = {2201.06642},
 primaryClass = {cs.CL},
       adsurl = {},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  author    = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
  title     = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
  series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
  editor    = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
  publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
  address   = {Mannheim},
  doi       = {10.14618/ids-pub-10468},
  url       = {},
  pages     = {1 -- 9},
  year      = {2021},
  abstract  = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
  language  = {en}

       author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa},
        title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
         year = 2021,
        month = mar,
          eid = {arXiv:2103.12028},
        pages = {arXiv:2103.12028},
archivePrefix = {arXiv},
       eprint = {2103.12028},
 primaryClass = {cs.CL},
       adsurl = {},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}

    title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
    author = "Ortiz Su{'a}rez, Pedro Javier  and
      Romary, Laurent  and
      Sagot, Benoit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "1703--1714",
    abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",

  author    = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
  title     = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
  series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
  editor    = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
  publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
  address   = {Mannheim},
  doi       = {10.14618/ids-pub-9021},
  url       = {},
  pages     = {9 -- 16},
  year      = {2019},
  abstract  = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
  language  = {en}


Thanks to @pjox, @Uinelj and @lhoestq for adding this dataset.

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