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community-oscar

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license: cc0-1.0
size_categories:
  - n>1T
multilinguality:
  - multilingual
source_datasets:
  - original
task_categories:
  - fill-mask
  - text-generation
task_ids:
  - language-modeling
paperswithcode_id: oscar
pretty_name: Community-OSCAR
extra_gated_prompt: >-
  By filling the form below I understand that Community-OSCAR is just a partial
  annotation of the WET files of 44 Common Crawl snapshots, the original data is
  included here **only for convenience**, and specially for researchers looking
  for data in lower resource languages. **Only the annotations are distributed
  under a cc0-1.0 license**, for the rest of the content I have read the [Common
  Crawl Terms of use](https://commoncrawl.org/terms-of-use/) and I will abide by
  them. I understand that all uses of the textual content in Community-OSCAR are
  subject to the [Common Crawl Terms of
  use](https://commoncrawl.org/terms-of-use/). I understand that reusing the
  textual content in Community-OSCAR might not be legal in all countries/regions
  and for all use cases. I understand that Community-OSCAR is mainly targeted
  towards researchers and meant to be used in research. The OSCAR Project
  reserves the right to revoke my access to this data. The OSCAR Project
  reserves the right to modify this data at any time in accordance to take down
  requests.
extra_gated_fields:
  Name: text
  Email: text
  Affiliation: text
  Country: text
  Usecase: text
  I have explicitly checked that downloading Community-OSCAR is legal in my jurisdiction, in the country/region where I am located right now, and for the use case that I have described above, I have also read and accepted the Common Crawl Terms of use: checkbox

Community OSCAR

image/png

The OSCAR project (Open Super-large Crawled Aggregated coRpus) is an Open Source project aiming to provide web-based multilingual resources and datasets for Machine Learning (ML) and Artificial Intelligence (AI) applications. The project focuses specifically in providing large quantities of unannotated raw data that is commonly used in the pre-training of large deep learning models. The OSCAR project has developed high-performance data pipelines specifically conceived to classify and filter large amounts of web data. The project has also put special attention in improving the data quality of web-based corpora as well as providing data for low-resource languages, so that these new ML/AI technologies are accessible to as many communities as possible.

Update 2027/11/14

Added the latest 2024-38 dump bringing the total to 45 dumps.

Update 2024/09/22

Added 3 new dumps:

  • 2024-33
  • 2024-30
  • 2024-05

bringing the total to 45 and finishing the coverage of all CommonCrawl releases from 2020-2024

Community-OSCAR is an unofficial version of the OSCAR Corpus created by community members. The annotation schema follows the OSCAR 23.01 release but is based on 45 monthly dumps of Common Crawl ranging from 2024-22 to 2014-42. With these forty dumps, Community-OSCAR is the largest release of the OSCAR Corpus so far.

The annotations contain features, including KenLM-based adult content detection, precomputed Locality-Sensitive Hashes for near deduplication, and blocklist-based categories. Community-OSCAR is distributed as JSONL-files with Zstandard compression. You might already have zstd installed on your system, but if not, please check the Zstandard website for installation instructions.

Ongoing Community Effort

Community-OSCAR is a release created by members of the OSCAR community and part of an ongoing effort in close collaboration with the Occiglot research collective. We are working on extending this release to all publicly available Common Crawl dumps, producing a filtered version (see Occiglot-FineWeb), and have plenty of ideas on further improvements. If you want to support our activities and collaborate with us, please join the Discord server from the OSCAR project or Occiglot research collective.

Downloading the Data

You can stream the data directly into datasets using a script like this 

from datasets import load_dataset
# Load afrkaans subset from 2024-22 snapshot
ds = load_dataset('oscar-corpus/community-oscar', data_files='data/2024-22/af_meta/*.jsonl.zst', split='train',
                  streaming=True)

Alternatively you can download the data using huggingface_hub python library.

If you want to download a considerable amount of data we recomend you use hf_transfer python package and set the environment variable HF_HUB_ENABLE_HF_TRANSFER=1.

Supported Tasks and Leaderboards

OSCAR is mainly intended to pre-train language models and word representations.

NOTE: Community-OSCAR contains the raw unfiltered Common Crawl text data but with quality annotations. For language model training, we highly recommend filtering the data first with these annotations. A prefiltered version of the dataset will be released in the near future (following the approach from Occiglot-FineWeb).

Data Annotations

Each sample comes with a series of annotations that allow the removal of low quality data.

  • identification: Language identification based on fastText.
  • harmful_pp: This perplexity comes from a KenLM model trained on harmful content, previously gathered by using the adult annotation in OSCAR 22.01. In other terms, the lower it is, the more likely a given document contains harmful/adult content.
  • tlsh: We use TLSH to compute a hash for each document. Locality sensitive hashing is a hashing method that computes similar hashes for similar documents.
  • quality_warnings: Computed through heuristics (see below).
  • categories: Content categories arom a URL-based blocklist

The annotation schema is the same as in the OSCAR 23.01 release.

Quality Warnings

  • tiny: The document has a low (<5) number of lines.
  • short_sentences: The document has a high number (>50%) of short lines (<400 bytes)
  • header: The document has a high number of short lines at its head, suggesting the presence of low quality content.
  • footer: The document has a high number of short lines at its tail, suggesting the presence of low quality content.
  • noisy: The document has a high percentage of punctuation (>50%)
  • adult: The document contains adult content. This annotation uses a blocklist and labels a tiny part of the corpus: It does not catch most of the adult content.

More information about the thresholds and annotators are present in the OSCAR paper.

Data Format

The data is stored as ZSTD-compressed JSON line files. Each individual data sample has the following JSON schema:

{
   "content":"English sentence\nphrase en français\n????????????", // (1)
   "warc_headers":{ // (2)
      "warc-identified-content-language":"fra,eng",
      "warc-target-uri":"https://fr.wikipedia.org/wiki/...",
      "warc-record-id":"<urn:uuid:29eaa920-d299-4b1d-b687-c72bd8d68116>",
      "warc-type":"conversion",
      "content-length":"35298", // (3)
      "warc-refers-to":"<urn:uuid:39e42055-0d94-4e45-9c6c-9e7056635d64>",
      "warc-block-digest":"sha1:WFH2A5WHCS2H365GIAFYQPI7UOAMFGHB", // (3)
      "warc-date":"2022-11-26T09:45:47Z",
      "content-type":"text/plain"
   },
   "metadata":{
      "identification":{ // (4)
         "label":"fr",
         "prob":0.8938327
      },
      "harmful_pp":4063.1814, // (5)
      "tlsh":"tlsh:T125315FF2B6088901EEA097015DB39B4600B...", // (6)
      "quality_warnings":[ // (7)
         "short_sentences",
         "header",
         "footer"
      ],
      "categories":[ // (8)
         "examen_pix",
         "liste_bu"
      ],
      "sentence_identifications":[ // (9)
         {
            "label":"fr",
            "prob":0.99837273
         },
         {
            "label":"en",
            "prob":0.9992377
         },
         null
      ]
   }
}

Language Statistics

All the data is distributed by language and release name. Up to 151 different languages are available. The table below provides the language code as well as the number of documents, compressed data sizes, number of words (by whitespaces) and characters. The statistics are computed based on uncompressed data and on estimates calculated on a subset of 12 releases and extrapolated to all 41 releases (snapshots). Roughly half of the data is English, leaving 19T non-english words.

lang Language Data (avg./release) #Docs (avg./release) #Words (avg./release) #Characters (avg./release) Data (Total) Lines (Total) Words (Total) Characters (Total)
Total 6.78TiB 1.13B 935.22B 6.12T 278.01TiB 46.46B 38.34T 251.03T
en English 2.56TiB 483.20M 465.20B 2.80T 105.16TiB 19.81B 19.07T 114.75T
Total (w/o en) 4.22TiBB 650.05M 470.02B 3.32T 172.85TiB 26.65B 19.27T 136.27T
ru Russian 982.11GiB 94.21M 80.68B 593.06B 39.32TiB 3.86B 3.31T 24.32T
zh Chinese 590.68GiB 67.47M 15.71B 242.67B 23.65TiB 2.77B 644.28B 9.95T
de German 405.00GiB 79.64M 58.54B 427.68B 16.22TiB 3.27B 2.40T 17.53T
es Spanish 349.22GiB 58.54M 59.59B 367.67B 13.98TiB 2.40B 2.44T 15.07T
fr French 306.61GiB 57.83M 50.73B 317.41B 12.28TiB 2.37B 2.08T 13.01T
it Italian 184.18GiB 31.41M 29.58B 195.26B 7.37TiB 1.29B 1.21T 8.01T
ja Japanese 165.97GiB 42.67M 5.72B 72.52B 6.65TiB 1.75B 234.47B 2.97T
pt Portuguese 151.63GiB 26.79M 25.09B 157.95B 6.07TiB 1.10B 1.03T 6.48T
pl Polish 123.65GiB 22.79M 17.70B 126.32B 4.95TiB 934.43M 725.54B 5.18T
nl Dutch 91.28GiB 24.44M 15.17B 97.54B 3.65TiB 1.00B 621.77B 4.00T
vi Vietnamese 83.07GiB 11.49M 14.80B 69.26B 3.33TiB 471.02M 606.86B 2.84T
th Thai 69.61GiB 5.39M 2.50B 29.73B 2.79TiB 220.82M 102.43B 1.22T
el Greek 68.44GiB 7.33M 6.24B 42.67B 2.74TiB 300.68M 255.91B 1.75T
tr Turkish 68.31GiB 13.11M 8.95B 67.37B 2.74TiB 537.64M 366.80B 2.76T
fa Persian 66.77GiB 8.60M 7.76B 40.94B 2.67TiB 352.65M 318.20B 1.68T
ar Arabic 61.53GiB 8.57M 6.28B 37.83B 2.46TiB 351.41M 257.37B 1.55T
cs Czech 54.99GiB 12.97M 8.07B 53.42B 2.20TiB 531.97M 330.77B 2.19T
hu Hungarian 44.35GiB 7.43M 5.80B 43.57B 1.78TiB 304.66M 237.74B 1.79T
uk Ukrainian 44.22GiB 5.21M 3.65B 26.80B 1.77TiB 213.63M 149.61B 1.10T
sv Swedish 42.76GiB 9.03M 6.95B 43.99B 1.71TiB 370.36M 284.99B 1.80T
bg Bulgarian 33.90GiB 3.49M 3.21B 20.78B 1.36TiB 142.90M 131.57B 852.02B
ro Romanian 33.50GiB 4.65M 5.29B 34.25B 1.34TiB 190.83M 216.89B 1.40T
ko Korean 32.35GiB 6.64M 3.31B 15.73B 1.30TiB 272.25M 135.84B 644.73B
fi Finnish 29.79GiB 5.48M 3.63B 30.82B 1.19TiB 224.50M 148.72B 1.26T
he Hebrew 28.17GiB 3.74M 2.98B 17.41B 1.13TiB 153.25M 122.19B 713.96B
hi Hindi 21.48GiB 1.77M 1.86B 10.08B 880.81GiB 72.38M 76.09B 413.17B
id Indonesian 14.67GiB 2.76M 2.25B 15.71B 601.30GiB 113.09M 92.12B 644.31B
lt Lithuanian 12.72GiB 2.36M 1.68B 12.83B 521.35GiB 96.72M 69.01B 526.16B
bn Bangla 12.68GiB 1.26M 834.94M 5.49B 520.02GiB 51.81M 34.23B 225.10B
sk Slovak 12.19GiB 2.85M 1.77B 12.03B 499.80GiB 116.77M 72.49B 493.20B
da Danish 11.38GiB 2.87M 1.93B 11.90B 466.69GiB 117.50M 79.22B 488.04B
ca Catalan 11.16GiB 2.81M 1.86B 11.63B 457.38GiB 115.40M 76.42B 476.63B
ta Tamil 10.33GiB 574763 559.30M 4.59B 423.46GiB 23.57M 22.93B 188.20B
multi - 8.85GiB 1.22M 1.05B 7.14B 362.94GiB 49.85M 43.22B 292.56B
ka Georgian 7.31GiB 592883 387.90M 3.14B 299.82GiB 24.31M 15.90B 128.83B
et Estonian 7.24GiB 1.59M 976.06M 7.53B 296.66GiB 65.37M 40.02B 308.78B
sr Serbian 7.19GiB 704978 675.30M 4.44B 294.65GiB 28.90M 27.69B 182.02B
lv Latvian 7.12GiB 1.21M 936.00M 7.03B 291.95GiB 49.69M 38.38B 288.11B
hy Armenian 4.43GiB 424969 348.89M 2.70B 181.46GiB 17.42M 14.30B 110.56B
ml Malayalam 4.32GiB 291610 198.34M 1.85B 177.15GiB 11.96M 8.13B 75.82B
az Azerbaijani 3.36GiB 603832 408.83M 3.12B 137.62GiB 24.76M 16.76B 128.07B
te Telugu 3.34GiB 267406 194.03M 1.49B 136.82GiB 10.96M 7.96B 60.95B
kk Kazakh 3.24GiB 320143 239.00M 1.93B 132.95GiB 13.13M 9.80B 79.00B
ne Nepali 3.17GiB 400031 204.06M 1.33B 130.11GiB 16.40M 8.37B 54.35B
mr Marathi 3.00GiB 251313 194.56M 1.32B 122.82GiB 10.30M 7.98B 54.26B
ur Urdu 2.66GiB 342973 342.83M 1.66B 109.20GiB 14.06M 14.06B 68.00B
mk Macedonian 2.57GiB 379079 243.56M 1.58B 105.49GiB 15.54M 9.99B 64.72B
sq Albanian 2.48GiB 496860 407.12M 2.48B 101.56GiB 20.37M 16.69B 101.67B
my Burmese 2.36GiB 173587 79.12M 968.68M 96.60GiB 7.12M 3.24B 39.72B
gu Gujarati 2.31GiB 128983 194.10M 1.15B 94.72GiB 5.29M 7.96B 47.23B
kn Kannada 2.04GiB 153938 117.35M 951.65M 83.81GiB 6.31M 4.81B 39.02B
be Belarusian 2.00GiB 234797 163.91M 1.21B 82.05GiB 9.63M 6.72B 49.51B
no Norwegian 1.97GiB 1.11M 320.46M 2.07B 80.65GiB 45.70M 13.14B 84.72B
is Icelandic 1.89GiB 479673 285.65M 1.83B 77.44GiB 19.67M 11.71B 75.23B
mn Mongolian 1.87GiB 213630 162.46M 1.15B 76.86GiB 8.76M 6.66B 47.35B
km Khmer 1.79GiB 140645 40.87M 714.22M 73.31GiB 5.77M 1.68B 29.28B
si Sinhala 1.78GiB 112624 134.15M 844.35M 73.06GiB 4.62M 5.50B 34.62B
sl Slovenian 1.01GiB 445779 156.82M 1.06B 41.59GiB 18.28M 6.43B 43.45B
tg Tajik 988.00MiB 73726 83.29M 576.25M 39.56GiB 3.02M 3.41B 23.63B
eu Basque 806.16MiB 262487 104.16M 842.18M 32.28GiB 10.76M 4.27B 34.53B
pa Punjabi 780.76MiB 71240 65.56M 350.37M 31.26GiB 2.92M 2.69B 14.36B
tt Tatar 684.17MiB 77931 56.24M 402.50M 27.39GiB 3.20M 2.31B 16.50B
ckb Central Kurdish 624.12MiB 93207 52.42M 360.95M 24.99GiB 3.82M 2.15B 14.80B
ky Kyrgyz 489.66MiB 72858 36.24M 286.59M 19.61GiB 2.99M 1.49B 11.75B
tl Filipino 460.45MiB 74172 77.36M 480.16M 18.44GiB 3.04M 3.17B 19.69B
am Amharic 436.81MiB 44448 38.29M 205.38M 17.49GiB 1.82M 1.57B 8.42B
eo Esperanto 411.92MiB 107893 64.90M 421.28M 16.49GiB 4.42M 2.66B 17.27B
or Odia 364.42MiB 54702 24.03M 157.21M 14.59GiB 2.24M 985.22M 6.45B
bo Tibetan 335.38MiB 22403 4.47M 126.55M 13.43GiB 918536 183.15M 5.19B
ps Pashto 293.88MiB 45796 37.31M 178.25M 11.77GiB 1.88M 1.53B 7.31B
lo Lao 286.92MiB 35277 7.46M 113.82M 11.49GiB 1.45M 305.88M 4.67B
cy Welsh 268.65MiB 78645 46.25M 278.10M 10.76GiB 3.22M 1.90B 11.40B
ug Uyghur 212.29MiB 22044 15.59M 121.98M 8.50GiB 903834 639.20M 5.00B
dv Divehi 207.92MiB 30485 13.36M 119.18M 8.33GiB 1.25M 547.74M 4.89B
as Assamese 202.40MiB 18951 13.49M 88.34M 8.10GiB 776997 552.99M 3.62B
gl Galician 195.43MiB 96533 31.92M 199.39M 7.82GiB 3.96M 1.31B 8.18B
yi Yiddish 157.11MiB 20405 15.33M 94.83M 6.29GiB 836632 628.66M 3.89B
ba Bashkir 155.53MiB 23672 12.54M 92.19M 6.23GiB 970562 514.29M 3.78B
ku Kurdish 120.61MiB 31901 19.63M 114.28M 4.83GiB 1.31M 805.03M 4.69B
sd Sindhi 110.46MiB 14589 13.74M 67.60M 4.42GiB 598183 563.48M 2.77B
hr Croatian 88.36MiB 14480 12.35M 88.91M 3.54GiB 593680 506.36M 3.65B
sa Sanskrit 87.88MiB 7776 4.39M 34.88M 3.52GiB 318843 180.14M 1.43B
pnb Western Panjabi 53.99MiB 8182 6.47M 32.91M 2.16GiB 335482 265.46M 1.35B
sah Yakut 50.51MiB 7804 3.55M 29.19M 2.02GiB 319984 145.57M 1.20B
fy Western Frisian 50.19MiB 23703 7.91M 50.53M 2.01GiB 971826 324.19M 2.07B
cv Chuvash 41.20MiB 6514 3.40M 24.15M 1.65GiB 267111 139.23M 990.10M
ga Irish 33.83MiB 15186 5.41M 33.13M 1.35GiB 622656 221.81M 1.36B
ceb Cebuano 30.24MiB 4924 4.79M 31.23M 1.21GiB 201918 196.19M 1.28B
af Afrikaans 27.95MiB 12458 5.07M 28.89M 1.12GiB 510784 207.74M 1.18B
br Breton 26.95MiB 22426 4.53M 27.41M 1.08GiB 919479 185.89M 1.12B
os Ossetic 19.94MiB 6503 1.70M 11.90M 817.46MiB 266636 69.59M 488.10M
uz Uzbek 16.15MiB 13478 1.97M 16.45M 662.13MiB 552625 80.91M 674.62M
azb South Azerbaijani 14.83MiB 7798 1.19M 8.69M 607.84MiB 319721 48.88M 356.10M
lb Luxembourgish 13.08MiB 7289 1.96M 13.28M 536.38MiB 298849 80.38M 544.40M
mg Malagasy 12.88MiB 3983 1.85M 13.45M 527.98MiB 163309 76.00M 551.43M
mhr Eastern Mari 10.49MiB 2345 830225 6.13M 430.25MiB 96145 34.04M 251.16M
nds Low German 9.58MiB 2046 1.58M 9.71M 392.94MiB 83916 64.77M 398.20M
ce Chechen 8.88MiB 3313 735487 5.22M 363.94MiB 135870 30.15M 214.08M
xmf Mingrelian 7.23MiB 2959 393400 3.15M 296.34MiB 121322 16.13M 129.24M
new Newari 5.02MiB 916 324361 2.08M 205.73MiB 37569 13.30M 85.38M
sh Serbian 4.22MiB 1019 1.02M 4.28M 173.18MiB 41779 41.69M 175.64M
ms Malay 4.02MiB 4988 382700 3.32M 164.63MiB 204511 15.69M 136.14M
min Minangkabau 3.63MiB 953 324454 2.07M 148.63MiB 39103 13.30M 84.97M
nn Norwegian Nynorsk 3.30MiB 8285 553667 3.37M 135.40MiB 339685 22.70M 138.31M
tk Turkmen 2.41MiB 1662 269719 2.28M 98.64MiB 68162 11.06M 93.31M
gom Goan Konkani 2.14MiB 135 127985 891404 87.80MiB 5541 5.25M 36.55M
arz Egyptian Arabic 2.11MiB 1482 234425 1.26M 86.36MiB 60789 9.61M 51.84M
bpy Bishnupriya 1.95MiB 379 130226 824058 80.09MiB 15566 5.34M 33.79M
la Latin 1.61MiB 4738 276095 1.68M 66.12MiB 194281 11.32M 69.02M
pms Piedmontese 1.49MiB 466 264178 1.47M 61.22MiB 19112 10.83M 60.40M
jbo Lojban 1.30MiB 276 277677 1.35M 53.38MiB 11329 11.38M 55.40M
mt Maltese 1.17MiB 2544 145437 1.17M 48.00MiB 104324 5.96M 48.04M
oc Occitan 1020.71KiB 280 38821 1.03M 40.87MiB 11510 1.59M 42.35M
war Waray 997.57KiB 328 150252 1.02M 39.94MiB 13468 6.16M 41.73M
vo Volapük 935.64KiB 572 140394 888363 37.46MiB 23482 5.76M 36.42M
ast Asturian 599.07KiB 1742 90259 592203 23.99MiB 71459 3.70M 24.28M
lez Lezghian 454.96KiB 145 35011 259634 18.22MiB 5962 1.44M 10.65M
mrj Western Mari 419.70KiB 130 33093 242203 16.80MiB 5340 1.36M 9.93M
su Sundanese 394.30KiB 35 61531 388935 15.79MiB 1438 2.52M 15.95M
sw Swahili 376.35KiB 683 64274 384592 15.07MiB 28033 2.64M 15.77M
gsw Swiss German 367.62KiB 163 54335 352731 14.72MiB 6700 2.23M 14.46M
wuu Wu Chinese 232.88KiB 86 6765 86236 9.32MiB 3532 277388 3.54M
wa Walloon 171.79KiB 52 3278 175434 6.88MiB 2132 134435 7.19M
gd Scottish Gaelic 91.63KiB 253 10844 91821 3.67MiB 10403 444624 3.76M
mzn Mazanderani 88.55KiB 50 9282 50922 3.55MiB 2080 380582 2.09M
hsb Upper Sorbian 85.59KiB 134 11768 80704 3.43MiB 5504 482505 3.31M
ia Interlingua 80.21KiB 37 23253 81382 3.21MiB 1527 953407 3.34M
krc Karachay-Balkar 58.04KiB 83 4072 32574 2.32MiB 3420 166969 1.34M
kv Komi 25.03KiB 67 2182 14450 1.00MiB 2760 89479 592456
av Avaric 23.69KiB 27 1440 13157 971.31KiB 1113 59053 539450
jv Javanese 18.04KiB 46 2534 17534 739.83KiB 1886 103928 718917
ilo Iloko 15.43KiB 39 2554 15737 632.47KiB 1599 104737 645234
li Limburgish 13.46KiB 2 77 13762 552.02KiB 118 3161 564260
mai Maithili 11.20KiB 18 1455 5092 459.00KiB 738 59689 208806
yo Yoruba 10.89KiB 43 1502 7585 446.46KiB 1773 61595 311008
lmo Lombard 9.66KiB 26 1582 9200 396.21KiB 1066 64896 377227
an Aragonese 7.55KiB 11 264 4311 309.74KiB 481 10851 176751
bar Bavarian 6.82KiB 26 1743 3543 279.77KiB 1090 71479 145287
io Ido 6.51KiB 43 1114 6633 266.79KiB 1797 45674 271970
bh Bihari languages 5.90KiB 21 465 2325 241.72KiB 866 19065 95354
bxr Russia Buriat 5.16KiB 23 470 3036 211.74KiB 951 19278 124476
bs Bosnian 3.85KiB 8 474 3802 157.87KiB 328 19464 155912
ie Interlingue 3.06KiB 1 722 2967 125.60KiB 61 29602 121647
so Somali 2.26KiB 18 647 2114 92.64KiB 738 26556 86692
xal Kalmyk 2.02KiB 6 189 1184 82.73KiB 280 7769 48544
nah Nahuatl languages 1.80KiB 13 192 1690 74.00KiB 553 7884 69290
gn Guarani 1.51KiB 5 191 1375 61.90KiB 222 7836 56386
ht Haitian Creole 85.38B 1 118 681 27.35KiB 41 4858 27921
kw Cornish 70.14B 3 104 559 22.47KiB 127 4272 22931
x-eml Unknown language [x-eml] 61.71B 1 85 445 19.77KiB 41 3485 18258
lrc Northern Luri 52.33B 1 41 232 16.76KiB 68 1681 9512
dsb Lower Sorbian 36.08B 1 37 260 11.56KiB 68 1517 10687
rue Rusyn 29.75B 1 8 130 9.53KiB 41 328 5330
scn Sicilian 27.12B 1 37 204 8.69KiB 49 1525 8380
qu Quechua 26.51B 1 24 207 8.49KiB 59 997 8487
vec Venetian 20.00B 1 34 151 6.41KiB 41 1394 6191
diq Dimli (individual language) 18.46B 1 19 132 5.91KiB 41 779 5425
rm Romansh 13.00B 1 10 104 4.16KiB 41 410 4264
- 0.00B 0 0 0 0.00B 0 0 0

Issues

Community-OSCAR may have quality issues on low size subcorpora, as it has been the case before. Please consider taking a look at Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets to get a better understanding of the current limitations of our language classifier.

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: https://github.com/oscar-corpus/corpus/issues.

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

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 organization'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 Community-OSCAR the following forty monthly Common Crawl snapshots were used:

2024-33
2024-30
2024-22
2024-18
2024-10
2023-50
2023-40
2023-23
2023-14
2023-06
2022-49
2022-40
2022-33
2022-27
2022-21
2022-05
2021-49
2021-43
2021-39
2021-31
2021-25
2021-21
2021-17
2021-10
2021-04
2020-50
2020-45
2020-40
2020-34
2020-29
2020-24
2020-16
2020-10
2020-05
2019-22
2018-47
2018-30
2017-43
2017-13
2016-40
2016-22
2015-48
2015-14
2014-42

Who are the source language producers?

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

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 languages, 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. We have added annotations to Common Crawl, so please consider using them to select the data that you would like to use for your particular use case.

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 languages. Some audits have already been done by third parties.

Dataset Curators & Contributors

Community-OSCAR was put together by community members in close collaboration with the Occiglot research collective. The main contributors are Manuel Brack, Pedro Ortiz Suarez, Malte Ostendorff, Patrick Schramowski, Georg Rehm, Kristian Kersting, Jose Javier Saiz, Iñaki Lacunza Castilla, Alexander Shvets, Jorge Palomar-Giner, and Marta Villegas. Moreover, this release is supported by and was enabled by contributions from the OSCAR team at Inria (project-team ALMAnaCH), specially by Julien Abadji, Rua Ismail and Benoit Sagot, the Common Crawl Foundation, the SLT and SAINT teams at DFKI, TU Darmstadt, the LangTech unit at the Barcelona Supercomputing Center, the 42 supercomputer and Hessian AI, the OpenGPT-X project, Fraunhofer, Jülich Supercomputing Centre, TU Dresden, Deutsche Telekom, as well as by members of the OSCAR community, in particular Sotaro Takeshita, Sebastian Nagel.

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, the metadata and the annotations of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, the OSCAR community members have waived all copyright and related or neighboring rights to OSCAR.

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.

Please use the contact information on our website for take down requests.

We strongly advise users to submit take down request to Common Crawl. For more information please read their Terms of Use

Citation Information

If you use our work, please cite the technical report: 

@misc{brack2024communityoscar,
    title        = {Community OSCAR: A Community Effort for Multilingual Web Data},
    author       = {Manuel Brack and Malte Ostendorff and Pedro Ortiz Suarez and José Javier Saiz and Iñaki Lacunza Castilla and Jorge Palomar-Giner and Aleksandr Shvets and Patrick Schramowski and Georg Rehm and Marta Villegas and Kristian Kersting},
    year         = {2024},
    howpublished = {technical report},
    url          = {https://occiglot.eu/papers/Community_Oscar.pdf}
}

Additionally, please consider these citations that Community OSCAR relies on: 

@ARTICLE{2022arXiv221210440J,
       author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
        title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language},
         year = 2022,
        month = dec,
          eid = {arXiv:2212.10440},
        pages = {arXiv:2212.10440},
          doi = {10.48550/arXiv.2212.10440},
archivePrefix = {arXiv},
       eprint = {2212.10440},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{abadji-etal-2022-towards,
    title = "Towards a Cleaner Document-Oriented Multilingual Crawled Corpus",
    author = "Abadji, Julien  and
      Ortiz Suarez, Pedro  and
      Romary, Laurent  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.463",
    pages = "4344--4355",
    abstract = "The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.",
}

      
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
  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       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
  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}
}

@article{kreutzer-etal-2022-quality,
    title = "Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets",
    author = {Kreutzer, Julia  and
      Caswell, Isaac  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
      Suarez, Pedro Ortiz  and
      Orife, Iroro  and
      Ogueji, Kelechi  and
      Rubungo, Andre Niyongabo  and
      Nguyen, Toan Q.  and
      M{\"u}ller, Mathias  and
      M{\"u}ller, Andr{\'e}  and
      Muhammad, Shamsuddeen Hassan  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
      Azime, Israel Abebe  and
      Awokoya, Ayodele  and
      Ataman, Duygu  and
      Ahia, Orevaoghene  and
      Ahia, Oghenefego  and
      Agrawal, Sweta  and
      Adeyemi, Mofetoluwa},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "10",
    year = "2022",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2022.tacl-1.4",
    doi = "10.1162/tacl_a_00447",
    pages = "50--72",
    abstract = "With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50{\%} sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.",
}

@inproceedings{ortiz-suarez-etal-2020-monolingual,
    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 = "https://www.aclweb.org/anthology/2020.acl-main.156",
    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.",
}

@inproceedings{OrtizSuarezSagotRomary2019,
  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       = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
  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}
}