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CulturaX
Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages
Dataset Summary
We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. We employ MinHash at document level to achieve fuzzy deduplication for the datasets in different languages. Our data cleaning framework includes diverse criteria and threshold selections, guided by extensive data samples, ensuring comprehensive noise filtering in various aspects. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs.
Our dataset combines the most recent iteration of mC4 (version 3.1.0) [1] with all accessible OSCAR corpora up to the present year, including 20.19, 21.09, 22.01, and 23.01 [2]. After deep cleaning and deduplication, CulturaX involves 16TB data in the parquet format (expanding to 27TB when unpacked). More than a half of our dataset is dedicated to non-English languages to significantly boost the data size and enhance the feasibility of training models in multilingual scenarios.
To obtain perplexity scores for data cleaning, we train a SentencePiece tokenizer and 5-gram Kneser-Ney language models as provided in the KenLM library [3] using the 20230501 dumps of Wikipedia. Our KenLM models are also released in HuggingFace: https://huggingface.co/uonlp/kenlm.
Details for the dataset can be found in our technical paper: https://arxiv.org/abs/2309.09400
You can download the dataset using Hugging Face datasets:
You may need to follow these instructions to setup authentication before downloading the dataset: https://huggingface.co/docs/huggingface_hub/quick-start#login
from datasets import load_dataset
ds = load_dataset("uonlp/CulturaX",
"en",
use_auth_token=True)
Languages
The supported languages and statistics for our dataset can be found below:
(Note that the language code als
and eml
refer to gsw
and x-eml
in the OSCAR-2301 dataset.)
Code | Language | # Documents | # Tokens | # Tokens (%) | |
---|---|---|---|---|---|
0 | en | English | 3,241,065,682 | 2,846,970,578,793 | 45.13 |
1 | ru | Russian | 799,310,908 | 737,201,800,363 | 11.69 |
2 | es | Spanish | 450,937,645 | 373,845,662,394 | 5.93 |
3 | de | German | 420,017,484 | 357,030,348,021 | 5.66 |
4 | fr | French | 363,754,348 | 319,332,674,695 | 5.06 |
5 | zh | Chinese | 218,624,604 | 227,055,380,882 | 3.60 |
6 | it | Italian | 211,309,922 | 165,446,410,843 | 2.62 |
7 | pt | Portuguese | 190,289,658 | 136,941,763,923 | 2.17 |
8 | pl | Polish | 142,167,217 | 117,269,087,143 | 1.86 |
9 | ja | Japanese | 111,188,475 | 107,873,841,351 | 1.71 |
10 | nl | Dutch | 117,392,666 | 80,032,209,900 | 1.27 |
11 | ar | Arabic | 74,027,952 | 69,354,335,076 | 1.10 |
12 | tr | Turkish | 94,207,460 | 64,292,787,164 | 1.02 |
13 | cs | Czech | 65,350,564 | 56,910,486,745 | 0.90 |
14 | vi | Vietnamese | 57,606,341 | 55,380,123,774 | 0.88 |
15 | fa | Persian | 59,531,144 | 45,947,657,495 | 0.73 |
16 | hu | Hungarian | 44,132,152 | 43,417,981,714 | 0.69 |
17 | el | Greek | 51,430,226 | 43,147,590,757 | 0.68 |
18 | ro | Romanian | 40,325,424 | 39,647,954,768 | 0.63 |
19 | sv | Swedish | 49,709,189 | 38,486,181,494 | 0.61 |
20 | uk | Ukrainian | 44,740,545 | 38,226,128,686 | 0.61 |
21 | fi | Finnish | 30,467,667 | 28,925,009,180 | 0.46 |
22 | ko | Korean | 20,557,310 | 24,765,448,392 | 0.39 |
23 | da | Danish | 25,429,808 | 22,921,651,314 | 0.36 |
24 | bg | Bulgarian | 24,131,819 | 22,917,954,776 | 0.36 |
25 | no | Norwegian | 18,907,310 | 18,426,628,868 | 0.29 |
26 | hi | Hindi | 19,665,355 | 16,791,362,871 | 0.27 |
27 | sk | Slovak | 18,582,517 | 16,442,669,076 | 0.26 |
28 | th | Thai | 20,960,550 | 15,717,374,014 | 0.25 |
29 | lt | Lithuanian | 13,339,785 | 14,247,110,836 | 0.23 |
30 | ca | Catalan | 15,531,777 | 12,530,288,006 | 0.20 |
31 | id | Indonesian | 23,251,368 | 12,062,966,061 | 0.19 |
32 | bn | Bangla | 12,436,596 | 9,572,929,804 | 0.15 |
33 | et | Estonian | 8,004,753 | 8,805,656,165 | 0.14 |
34 | sl | Slovenian | 7,335,378 | 8,007,587,522 | 0.13 |
35 | lv | Latvian | 7,136,587 | 7,845,180,319 | 0.12 |
36 | he | Hebrew | 4,653,979 | 4,937,152,096 | 0.08 |
37 | sr | Serbian | 4,053,166 | 4,619,482,725 | 0.07 |
38 | ta | Tamil | 4,728,460 | 4,378,078,610 | 0.07 |
39 | sq | Albanian | 5,205,579 | 3,648,893,215 | 0.06 |
40 | az | Azerbaijani | 5,084,505 | 3,513,351,967 | 0.06 |
41 | kk | Kazakh | 2,733,982 | 2,802,485,195 | 0.04 |
42 | ur | Urdu | 2,757,279 | 2,703,052,627 | 0.04 |
43 | ka | Georgian | 3,120,321 | 2,617,625,564 | 0.04 |
44 | hy | Armenian | 2,964,488 | 2,395,179,284 | 0.04 |
45 | is | Icelandic | 2,373,560 | 2,350,592,857 | 0.04 |
46 | ml | Malayalam | 2,693,052 | 2,100,556,809 | 0.03 |
47 | ne | Nepali | 3,124,040 | 2,061,601,961 | 0.03 |
48 | mk | Macedonian | 2,762,807 | 2,003,302,006 | 0.03 |
49 | mr | Marathi | 2,266,588 | 1,955,227,796 | 0.03 |
50 | mn | Mongolian | 1,928,828 | 1,850,667,656 | 0.03 |
51 | be | Belarusian | 1,643,486 | 1,791,473,041 | 0.03 |
52 | te | Telugu | 1,822,865 | 1,566,972,146 | 0.02 |
53 | gl | Galician | 1,785,963 | 1,382,539,693 | 0.02 |
54 | eu | Basque | 1,598,822 | 1,262,066,759 | 0.02 |
55 | kn | Kannada | 1,352,142 | 1,242,285,201 | 0.02 |
56 | gu | Gujarati | 1,162,878 | 1,131,730,537 | 0.02 |
57 | af | Afrikaans | 826,519 | 1,119,009,767 | 0.02 |
58 | my | Burmese | 865,575 | 882,606,546 | 0.01 |
59 | si | Sinhala | 753,655 | 880,289,097 | 0.01 |
60 | eo | Esperanto | 460,088 | 803,948,528 | 0.01 |
61 | km | Khmer | 1,013,181 | 746,664,132 | 0.01 |
62 | pa | Punjabi | 646,987 | 727,546,145 | 0.01 |
63 | cy | Welsh | 549,955 | 576,743,162 | 0.01 |
64 | ky | Kyrgyz | 570,922 | 501,442,620 | 0.01 |
65 | ga | Irish | 304,251 | 376,947,935 | 0.01 |
66 | ps | Pashto | 376,914 | 363,007,770 | 0.01 |
67 | am | Amharic | 243,349 | 358,206,762 | 0.01 |
68 | ku | Kurdish | 295,314 | 302,990,910 | 0.00 |
69 | tl | Filipino | 348,453 | 242,086,456 | 0.00 |
70 | yi | Yiddish | 141,156 | 217,584,643 | 0.00 |
71 | lo | Lao | 217,842 | 168,256,876 | 0.00 |
72 | fy | Western Frisian | 223,268 | 167,193,111 | 0.00 |
73 | sd | Sindhi | 109,162 | 147,487,058 | 0.00 |
74 | mg | Malagasy | 115,910 | 142,685,412 | 0.00 |
75 | or | Odia | 153,461 | 100,323,213 | 0.00 |
76 | as | Assamese | 52,627 | 83,787,896 | 0.00 |
77 | ug | Uyghur | 47,035 | 77,677,306 | 0.00 |
78 | uz | Uzbek | 87,219 | 75,250,787 | 0.00 |
79 | la | Latin | 48,968 | 44,176,580 | 0.00 |
80 | hr | Croatian | 460,690 | 40,796,811 | 0.00 |
81 | sw | Swahili | 66,506 | 30,708,309 | 0.00 |
82 | ms | Malay | 238,151 | 19,375,976 | 0.00 |
83 | br | Breton | 43,765 | 13,987,037 | 0.00 |
84 | sa | Sanskrit | 16,290 | 13,561,367 | 0.00 |
85 | gd | Scottish Gaelic | 8,408 | 4,796,485 | 0.00 |
86 | su | Sundanese | 1,554 | 1,308,460 | 0.00 |
87 | jv | Javanese | 2,058 | 625,429 | 0.00 |
88 | tg | Tajik | 483,835 | - | - |
89 | ceb | Cebuano | 263,890 | - | - |
90 | tt | Tatar | 218,102 | - | - |
91 | ckb | Central Kurdish | 172,035 | - | - |
92 | lb | Luxembourgish | 165,891 | - | - |
93 | mt | Maltese | 151,320 | - | - |
94 | nn | Norwegian Nynorsk | 126,083 | - | - |
95 | qu | Quechua | 1,202 | 72,101 | 0.00 |
96 | ba | Bashkir | 71,957 | - | - |
97 | arz | Egyptian Arabic | 71,625 | - | - |
98 | dv | Divehi | 66,702 | - | - |
99 | bo | Tibetan | 54,185 | - | - |
100 | sh | Serbian (Latin) | 45,619 | - | - |
101 | yo | Yoruba | 192 | 42,943 | 0.00 |
102 | bs | Bosnian | 1,237 | 39,768 | 0.00 |
103 | azb | South Azerbaijani | 29,833 | - | - |
104 | ht | Haitian Creole | 12 | 26,183 | 0.00 |
105 | war | Waray | 23,687 | - | - |
106 | cv | Chuvash | 22,570 | - | - |
107 | sah | Sakha | 22,141 | - | - |
108 | li | Limburgish | 206 | 18,532 | 0.00 |
109 | ce | Chechen | 17,322 | - | - |
110 | pnb | Western Panjabi | 15,625 | - | - |
111 | nds | Low German | 15,139 | - | - |
112 | tk | Turkmen | 14,393 | - | - |
113 | gn | Guarani | 103 | 12,708 | 0.00 |
114 | oc | Occitan | 10,556 | - | - |
115 | xmf | Mingrelian | 9,706 | - | - |
116 | ast | Asturian | 9,002 | - | - |
117 | os | Ossetic | 8,596 | - | - |
118 | mhr | Eastern Mari | 7,883 | - | - |
119 | pms | Piedmontese | 7,566 | - | - |
120 | als[*] | Swiss German | 6,936 | - | - |
121 | vo | Volapük | 6,621 | - | - |
122 | so | Somali | 39 | 6,053 | 0.00 |
123 | bpy | Bishnupriya | 5,087 | - | - |
124 | new | Newari | 4,344 | - | - |
125 | hsb | Upper Sorbian | 4,244 | - | - |
126 | lmo | Lombard | 3,530 | - | - |
127 | an | Aragonese | 2,746 | - | - |
128 | ilo | Iloko | 2,328 | - | - |
129 | mzn | Mazanderani | 1,914 | - | - |
130 | lez | Lezghian | 1,806 | - | - |
131 | rm | Romansh | 30 | 1,769 | 0.00 |
132 | krc | Karachay-Balkar | 1,745 | - | - |
133 | min | Minangkabau | 1,429 | - | - |
134 | kv | Komi | 1,396 | - | - |
135 | wa | Walloon | 1,383 | - | - |
136 | jbo | Lojban | 1,349 | - | - |
137 | io | Ido | 1,144 | - | - |
138 | mrj | Western Mari | 1,056 | - | - |
139 | gom | Goan Konkani | 721 | - | - |
140 | ia | Interlingua | 613 | - | - |
141 | av | Avaric | 438 | - | - |
142 | bh | Bihari languages | 265 | - | - |
143 | wuu | Wu Chinese | 222 | - | - |
144 | nah | Nahuatl languages | 131 | - | - |
145 | vec | Venetian | 113 | - | - |
146 | bxr | Russia Buriat | 100 | - | - |
147 | kw | Cornish | 94 | - | - |
148 | mai | Maithili | 93 | - | - |
149 | eml[*] | Emiliano-Romagnol | 91 | - | - |
150 | dsb | Lower Sorbian | 59 | - | - |
151 | xal | Kalmyk | 51 | - | - |
152 | lrc | Northern Luri | 43 | - | - |
153 | nap | Neapolitan | 31 | - | - |
154 | tyv | Tuvinian | 23 | - | - |
155 | scn | Sicilian | 21 | - | - |
156 | frr | Northern Frisian | 11 | - | - |
157 | mwl | Mirandese | 9 | - | - |
158 | myv | Erzya | 4 | - | - |
159 | ie | Interlingue | 4 | - | - |
160 | pam | Pampanga | 4 | - | - |
161 | bar | Bavarian | 3 | - | - |
162 | yue | Yue Chinese | 3 | - | - |
163 | cbk | Chavacano | 2 | - | - |
164 | bcl | Central Bikol | 1 | - | - |
165 | vls | West Flemish | 1 | - | - |
166 | rue | Rusyn | 1 | - | - |
Dataset Structure
{
"text": ...,
"timestamp": ...,
"url": ...,
"source": "mc4" | "OSCAR-xxxx",
}
Considerations for Using the Data
As CulturaX is the cleaned version of the mC4 and OSCAR datasets, which were both extracted from CommonCrawl, personal and sensitive information might still contain personal and sensitive information. This must be considered prior to using this dataset for any purpose, such as training deep learning models, etc.
License Information
The licence terms for CulturaX strictly follows those of mC4
and OSCAR
. Please refer to both below licenses when using this dataset.
Acknowledgements
We would like to extend our sincere thanks to Google Cloud for providing the TPU resources that made this project possible. Their support has been invaluable in enabling our team to run evaluations on our dataset efficiently.
Citation
To cite CulturaX, please use:
@inproceedings{nguyen-etal-2024-culturax,
title = "{C}ultura{X}: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages",
author = "Nguyen, Thuat and
Nguyen, Chien Van and
Lai, Viet Dac and
Man, Hieu and
Ngo, Nghia Trung and
Dernoncourt, Franck and
Rossi, Ryan A. and
Nguyen, Thien Huu",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.377",
pages = "4226--4237",
abstract = "Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is released in Hugging Face facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.",
}
Reference
[1] Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In NAACL 2021. https://huggingface.co/datasets/mc4
[2] Pedro Javier Ortiz Suárez, Benoît Sagot, and Laurent Romary. 2019. Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. In Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC- 7) 2019. https://oscar-project.org/
[3] KenLM: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, 2011.
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