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Datasets:
oscar-corpus
/
oscar-2301-hpc

Tasks:
Fill-Mask
Text Generation
Sub-tasks:
language-modeling
Size:
n>1T
ArXiv:
License:
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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By filling the form below, you understand that only the metadata and the annotations of OSCAR 23.01 have a cc0-1.0 license, and that the rest of the content is crawled data derived from the November/December 2022 snapshot of Common Crawl, for which the authors of OSCAR do not hold any copyright whatsoever.

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Dataset Card for "oscar-2301-hpc"

IMPORTANT NOTE: This dataset is intended to be downloaded as a snapshot and used directly on an HPC where keeping the number of inodes low is important. The files here are specifically designed to be very large, if you're looking for more managable file sizes please use the standard distribution of OSCAR instead: https://huggingface.co/datasets/oscar-corpus/OSCAR-2301

IMPORTANT NOTE: THIS DATASET CARD IS STILL BEING WRITTEN, PLEASE BE PATIENT WHILE WE COMPLETE ALL THE INFORMATION ABOUT THE CORPUS

Dataset Summary

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.

OSCAR 23.01 is the January 2023 version of the OSCAR Corpus based on the November/December 2022 dump of Common Crawl. While being quite similar to OSCAR 22.01, it contains several new features, including KenLM-based adult content detection, precomputed Locality-Sensitive Hashes for near deduplication, and blocklist-based categories. OSCAR 23.01 has also moved from gzip to Zstandard compression. You might already have zstd installed on your system, but if not, please check the Zstandard website for installation instructions.

Supported Tasks and Leaderboards

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

Languages

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.

Issues

OSCAR 23.01 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: https://github.com/oscar-corpus/corpus/issues.

Language code Language Issues

Dataset Structure

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

Data Instances

TODO

Layout 

{
   "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
      ]
   }
}

Data Splits

Click to expand the number of samples per configuration

Table

Code Language # docs # words Content Length :
0 af Afrikaans 23,994 6,217,024 37.2 MB
1 sq Albanian 1,342,790 462,694,599 3.2 GB
2 am Amharic 119,434 40,262,809 512.9 MB
3 ar Arabic 25,012,116 10,081,452,882 110.7 GB
4 an Aragonese 34 264 11.0 kB
5 hy Armenian 1,056,974 336,045,041 4.9 GB
6 as Assamese 89,542 24,395,215 412.1 MB
7 ast Asturian 440 10,917 74.1 kB
8 av Avaric 44 1,073 18.6 kB
9 az Azerbaijani 1,159,994 316,850,330 3.0 GB
10 bn Bangla 3,474,086 1,092,983,765 19.1 GB
11 ba Bashkir 128,248 26,036,637 363.7 MB
12 eu Basque 678,474 136,672,615 1.2 GB
13 be Belarusian 445,612 164,729,607 2.3 GB
14 bh Bihari languages 48 507 6.8 kB
15 bpy Bishnupriya 2,346 346,947 5.4 MB
16 bs Bosnian 20 395 3.0 kB
17 br Breton 36,338 4,759,407 31.4 MB
18 bg Bulgarian 8,933,998 3,635,273,738 44.1 GB
19 my Burmese 430,276 82,433,836 3.0 GB
20 ca Catalan 6,953,898 2,240,460,836 15.3 GB
21 ceb Cebuano 16,174 6,263,404 41.1 MB
22 ckb Central Kurdish 182,508 61,334,746 772.9 MB
23 ce Chechen 11,686 1,051,752 13.9 MB
24 zh Chinese 138,478,270 44,378,380,161 1.4 TB
25 cv Chuvash 16,652 3,039,925 42.3 MB
26 kw Cornish 8 80 432 Bytes
27 hr Croatian 31,808 3,542,961 26.5 MB
28 cs Czech 34,859,632 9,717,378,559 77.0 GB
29 da Danish 7,214,338 2,217,634,340 14.8 GB
30 dv Divehi 77,060 10,655,359 200.1 MB
31 nl Dutch 72,552,688 19,564,553,306 135.0 GB
32 mhr Eastern Mari 9,502 1,615,215 22.9 MB
33 arz Egyptian Arabic 3,958 385,511 3.7 MB
34 en English 1,235,510,986 523,869,288,690 3.4 TB
35 eo Esperanto 226,924 67,774,923 474.8 MB
36 et Estonian 3,601,904 938,296,892 8.0 GB
37 tl Filipino 250,558 110,560,444 719.2 MB
38 fi Finnish 14,471,710 4,198,143,883 41.1 GB
39 fr French 158,334,998 62,127,088,294 430.5 GB
40 gl Galician 248,762 38,345,625 255.7 MB
41 ka Georgian 1,343,036 373,935,158 8.4 GB
42 de German 206,598,430 73,848,586,648 594.7 GB
43 gom Goan Konkani 398 121,035 2.3 MB
44 el Greek 20,282,864 7,691,622,692 95.7 GB
45 gn Guarani 14 260 2.2 kB
46 gu Gujarati 425,552 417,001,705 5.6 GB
47 ht Haitian Creole 2 20,671 93.1 kB
48 he Hebrew 3,997,888 1,697,158,891 18.0 GB
49 hi Hindi 5,514,454 2,475,605,444 32.6 GB
50 hu Hungarian 21,349,372 16,013,364,289 150.1 GB
51 is Icelandic 1,210,232 294,471,539 2.2 GB
52 io Ido 224 2,598 16.1 kB
53 ilo Iloko 144 4,411 28.0 kB
54 id Indonesian 7,109,778 3,228,020,221 23.4 GB
55 ia Interlingua 34 9,384 33.5 kB
56 ie Interlingue 2 0 881 Bytes
57 ga Irish 29,894 9,054,923 63.2 MB
58 it Italian 89,021,606 36,327,274,203 259.4 GB
59 ja Japanese 94,236,404 4,401,059,165 181.2 GB
60 jv Javanese 172 3,286 25.7 kB
61 xal Kalmyk 2 27 315 Bytes
62 kn Kannada 448,500 124,924,350 2.6 GB
63 krc Karachay-Balkar 496 8,385 122.4 kB
64 kk Kazakh 677,622 214,679,857 3.3 GB
65 km Khmer 450,660 59,880,231 3.2 GB
66 kv Komi 460 5,909 70.3 kB
67 ko Korean 15,147,698 3,435,866,935 38.1 GB
68 ku Kurdish 80,338 25,921,607 174.1 MB
69 ky Kyrgyz 144,288 32,062,783 489.3 MB
70 lo Lao 118,374 10,659,203 472.1 MB
71 la Latin 14,384 307,865 2.0 MB
72 lv Latvian 2,435,882 845,459,899 7.4 GB
73 lez Lezghian 676 60,634 856.6 kB
74 li Limburgish 6 169 1.4 kB
75 lt Lithuanian 5,182,028 1,674,362,574 14.5 GB
76 jbo Lojban 572 312,315 1.5 MB
77 lmo Lombard 112 3,269 21.0 kB
78 nds Low German 5,248 1,612,175 10.7 MB
79 dsb Lower Sorbian 8 84 664 Bytes
80 lb Luxembourgish 18,090 2,514,838 18.4 MB
81 mk Macedonian 1,063,298 389,344,425 4.7 GB
82 mai Maithili 46 467 6.8 kB
83 mg Malagasy 10,830 1,416,430 11.2 MB
84 ms Malay 11,500 238,477 2.6 MB
85 ml Malayalam 800,936 236,597,838 5.8 GB
86 mt Maltese 5,180 149,886 1.3 MB
87 mr Marathi 729,578 252,706,331 4.5 GB
88 mzn Mazanderani 384 16,115 169.2 kB
89 min Minangkabau 2,436 305,589 3.8 MB
90 xmf Mingrelian 7,318 283,316 6.1 MB
91 mwl Mirandese 4 54 423 Bytes
92 mn Mongolian 1,061,710 454,350,415 5.8 GB
93 multi Multilingual 2,948,202 1,251,676,406 11.9 GB
94 nah Nahuatl languages 38 279 2.4 kB
95 ne Nepali 1,152,156 278,901,036 4.9 GB
96 new Newari 1,996 229,703 4.0 MB
97 no Norwegian 2,797,378 373,160,033 2.6 GB
98 nn Norwegian Nynorsk 19,470 575,518 3.7 MB
99 oc Occitan 920 34,701 405.0 kB
100 or Odia 158,426 31,963,340 543.1 MB
101 os Ossetic 8,628 3,935,964 50.7 MB
102 ps Pashto 87,408 30,196,179 261.6 MB
103 fa Persian 23,813,882 9,609,206,698 93.2 GB
104 pms Piedmontese 2,524 510,087 3.1 MB
105 pl Polish 57,184,826 18,073,705,588 147.1 GB
106 pt Portuguese 36,062,800 15,172,557,311 105.0 GB
107 pa Punjabi 222,058 104,235,418 1.4 GB
108 qu Quechua 2 13 143 Bytes
109 ro Romanian 11,985,668 6,302,600,833 45.6 GB
110 bxr Russia Buriat 72 698 8.2 kB
111 ru Russian 194,143,422 78,032,029,344 1.1 TB
112 sah Sakha 17,566 4,288,051 68.8 MB
113 sa Sanskrit 16,802 2,479,345 56.3 MB
114 gd Scottish Gaelic 776 18,458 146.1 kB
115 sr Serbian 1,677,896 632,781,822 7.7 GB
116 sh Serbian (Latin) 3,214 166,517 816.4 kB
117 sd Sindhi 48,566 14,667,207 131.6 MB
118 si Sinhala 301,066 172,755,385 2.6 GB
119 sk Slovak 8,931,784 2,704,716,280 21.5 GB
120 sl Slovenian 1,112,560 192,816,743 1.4 GB
121 so Somali 6 51 503 Bytes
122 azb South Azerbaijani 26,364 2,029,729 28.4 MB
123 es Spanish 153,574,556 63,388,237,965 429.9 GB
124 su Sundanese 18 258 2.0 kB
125 sw Swahili 1,664 164,459 1.0 MB
126 sv Swedish 21,891,348 6,993,719,601 50.0 GB
127 gsw Swiss German 342 34,328 232.7 kB
128 tg Tajik 144,932 76,987,285 1.0 GB
129 ta Tamil 1,638,238 738,824,392 15.8 GB
130 tt Tatar 262,654 59,253,765 833.8 MB
131 te Telugu 644,712 201,575,815 3.9 GB
132 th Thai 14,845,900 2,224,483,018 92.0 GB
133 bo Tibetan 62,352 6,062,558 531.6 MB
134 tr Turkish 26,654,330 8,290,890,087 73.7 GB
135 tk Turkmen 4,576 325,786 3.3 MB
136 uk Ukrainian 10,059,992 3,183,842,018 44.7 GB
137 x-eml Emiliano-Romagnol 4 329 1.8 kB
138 hsb Upper Sorbian 402 15,827 123.2 kB
139 ur Urdu 887,004 434,023,273 3.8 GB
140 ug Uyghur 51,304 14,659,554 219.8 MB
141 uz Uzbek 15,806 1,665,960 15.3 MB
142 vi Vietnamese 33,933,994 22,424,984,210 140.8 GB
143 vo Volapük 896 49,968 371.9 kB
144 wa Walloon 390 6,347 34.3 kB
145 war Waray 1,494 19,665 126.8 kB
146 cy Welsh 151,512 52,250,043 333.0 MB
147 fy Western Frisian 45,458 9,885,788 70.4 MB
148 mrj Western Mari 496 60,180 765.8 kB
149 pnb Western Panjabi 12,904 11,844,695 105.8 MB
150 wuu Wu Chinese 136 1,199 26.8 kB
151 yi Yiddish 47,438 14,287,370 171.7 MB
152 yo Yoruba 128 2,396 16.6 kB

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.

Annotations

The dataset does not contain any additional annotations.

Annotation process

N/A

Who are the annotators?

N/A

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

This release of OSCAR was made possible by Julien Abadji, Pedro Ortiz Suarez, Rua Ismail, Sotaro Takeshita, Sebastian Nagel and Benoit Sagot.

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 project, Inria, the Univertity of Mannheim and DFKI GmbH have waived all copyright and related or neighboring rights to OSCAR
This work is published from: France and Germany.

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 

@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}
}
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oscar-project.org
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github.com
Papers:
Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data
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