Datasets:
ArXiv:
License:
| license: cc0-1.0 | |
| language: | |
| - war | |
| - ceb | |
| - min | |
| - vie | |
| - ilo | |
| - tgl | |
| - lao | |
| - khm | |
| - mya | |
| - jav | |
| - ind | |
| - tha | |
| - sun | |
| - zlm | |
| pretty_name: Oscar 2201 | |
| task_categories: | |
| - self-supervised-pretraining | |
| tags: | |
| - self-supervised-pretraining | |
| 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. | |
| ## Languages | |
| war, ceb, min, vie, ilo, tgl, lao, khm, mya, jav, ind, tha, sun, zlm | |
| ## Supported Tasks | |
| Self Supervised Pretraining | |
| ## Dataset Usage | |
| ### Using `datasets` library | |
| ``` | |
| from datasets import load_dataset | |
| dset = datasets.load_dataset("SEACrowd/oscar_2201", trust_remote_code=True) | |
| ``` | |
| ### Using `seacrowd` library | |
| ```import seacrowd as sc | |
| # Load the dataset using the default config | |
| dset = sc.load_dataset("oscar_2201", schema="seacrowd") | |
| # Check all available subsets (config names) of the dataset | |
| print(sc.available_config_names("oscar_2201")) | |
| # Load the dataset using a specific config | |
| dset = sc.load_dataset_by_config_name(config_name="<config_name>") | |
| ``` | |
| More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use). | |
| ## Dataset Homepage | |
| [https://huggingface.co/datasets/oscar-corpus/OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) | |
| ## Dataset Version | |
| Source: 2022.1.0. SEACrowd: 2024.06.20. | |
| ## Dataset License | |
| Creative Commons Zero v1.0 Universal (cc0-1.0) | |
| ## Citation | |
| If you are using the **Oscar 2201** dataloader in your work, please cite the following: | |
| ``` | |
| @inproceedings{abadji2022cleaner, | |
| author = {Julien Abadji and | |
| Pedro Javier Ortiz Su{'{a}}rez and | |
| Laurent Romary and | |
| Beno{\^{\i}}t Sagot}, | |
| title = {Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}, | |
| booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference, | |
| {LREC} 2022, Marseille, France, 20-25 June 2022}, | |
| pages = {4344--4355}, | |
| publisher = {European Language Resources Association}, | |
| year = {2022}, | |
| url = {https://aclanthology.org/2022.lrec-1.463}, | |
| } | |
| @inproceedings{abadji2021ungoliant, | |
| 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{kreutzer2022quality, | |
| 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}, | |
| editor = {Roark, Brian and | |
| Nenkova, Ani}, | |
| 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{ortizsuarez2020monolingual, | |
| 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{ortizsuarez2019asynchronous, | |
| 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} | |
| } | |
| @article{lovenia2024seacrowd, | |
| title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, | |
| author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, | |
| year={2024}, | |
| eprint={2406.10118}, | |
| journal={arXiv preprint arXiv: 2406.10118} | |
| } | |
| ``` |