metadata

dataset_info:
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  - config_name: 15B
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  - config_name: 1M
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  - config_name: 20B
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  - config_name: 25B
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  - config_name: 30B
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  - config_name: 5B
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configs:
  - config_name: 100M
    data_files:
      - split: train
        path: 100M/train-*
      - split: test
        path: 100M/test-*
  - config_name: 100k
    data_files:
      - split: train
        path: 100k/train-*
      - split: test
        path: 100k/test-*
  - config_name: 10B
    data_files:
      - split: train
        path: 10B/train-*
      - split: test
        path: 10B/test-*
  - config_name: 10M
    data_files:
      - split: train
        path: 10M/train-*
      - split: test
        path: 10M/test-*
  - config_name: 10k
    data_files:
      - split: train
        path: 10k/train-*
      - split: test
        path: 10k/test-*
  - config_name: 15B
    data_files:
      - split: train
        path: 15B/train-*
      - split: test
        path: 15B/test-*
  - config_name: 1B
    data_files:
      - split: train
        path: 1B/train-*
      - split: test
        path: 1B/test-*
  - config_name: 1M
    data_files:
      - split: train
        path: 1M/train-*
      - split: test
        path: 1M/test-*
  - config_name: 20B
    data_files:
      - split: train
        path: 20B/train-*
      - split: test
        path: 20B/test-*
  - config_name: 25B
    data_files:
      - split: train
        path: 25B/train-*
      - split: test
        path: 25B/test-*
  - config_name: 30B
    data_files:
      - split: train
        path: 30B/train-*
      - split: test
        path: 30B/test-*
  - config_name: 5B
    data_files:
      - split: train
        path: 5B/train-*
      - split: test
        path: 5B/test-*
task_categories:
  - text-generation
  - text2text-generation

Filtered CulturaX + Wikipedia for Dutch

This is a combined and filtered version of CulturaX and Wikipedia, only including Dutch. It is intended for the training of LLMs.

Different configs are available based on the number of tokens (see a section below with an overview). This can be useful if you want to know exactly how many tokens you have. Great for using as a streaming dataset, too. Tokenization is done with the big vocabulary of the google/gemma-2b tokenizer so depending on your tokenizer these exact numbers may differ.

Every config also has a test set (for validation) of 1% the total size of the dataset, minimally 1 max. 64k samples (~26M tokens).

Wikipedia and CulturaX were suffled before merging and the teset set creation was also shuffled. Priority is given to Wikipedia to prioritize knowledge-content, so the smaller configs will consist exclusively of Wikipedia and for the larger configs we augment with CulturaX. Every config builds further on the previous, so this means that every config contains the same data as the smaller ones and more HOWEVER their train/test splits are not the same, so test set of one config may overlap with samples for another training set. This is usually not a problem but just be aware that you do not train on one config's training set and test with another config's test set.

Filtering

While CultruaX already has done a lot of filtering, some more filtering can be done to improve the quality of the corpus. These filters are described below.

The baseline ratios (punctuation, uppercase, digits) were calculated on the SONAR-500 corpus (excluding WRPEA WRPED WRUEA WRUED WRUEB).

CulturaX:

removed documents that contain the text "rechten voorbehouden" or "rights reserved"
remove document's whose URL contained "wikipedia.org" (because we include a cleaned version of Wikipedia ourselves)
removed documents that contain a "bad word" (see the section below)
removed documents that contain any non-latin characters. The idea is that "knowledge"-based information (e.g. original writing of a name) are allowed when the data comes from Wikipedia, but not from any other webcrawl, to avoid unsollicited noise.

CulturaX + Wikipedia:

removed documents where ratio of punctuation marks vs. non-whitespace characters is higher than 0.2
removed documents where ratio of uppercase vs. non-whitespace characters is higher than 0.22
removed documents where ratio of digits vs. non-whitespace characters is higher than 0.16
removed documents where the average token length is < 2 or > 20

Bad words

BAD_PHRASES_DOC_LEVEL = {
    # https://en.wikipedia.org/wiki/Dutch_profanity
    "achterlijk",
    "debiel",
    "downie",
    "idioot",
    "kankerlijer",
    "klere",
    "kolere",
    "minkukel",
    "pestkop",
    "pleuris",
    "pleuritis",
    "teringlijer",
    "tyfuslijer",
    "gadver",
    "getver",
    "godver",
    "godskolere",
    "godverork",
    "graftak",
    "kopvod",
    "verdomme",
    "anaalgeneraal",
    "bitch",
    "dikzak",
    "flikker",
    "fok",
    "fuck",
    "hoer",
    "klootzak",
    "klote",
    "kreng",
    "kringspiermusketier",
    "kut",
    "lamzak",
    "lul",
    "manwijf",
    "matennaai",
    "neuken",
    "neuker",
    "ouwehoer",
    "reet",
    "reetkever",
    "reetridder",
    "rotzak",
    "schijt",
    "shit",
    "slet",
    "slijmbal",
    "slons",
    "sodemieter",
    "stoephoer",
    "swaffel",
    "teef",
    "trut",
    "tut",
    "zak",
    "uilskuiken",
    "zeik",
    "bamivreter",
    "bosneger",
    "neger",
    "fransoos",
    "geitenneuker",
    "kaaskop",
    "kakker",
    "koelie",
    "lijp",
    "medelander",
    "mocro",
    "mof",
    "nikker",
    "poepchinees",
    "roetmop",
    "spaghettivreter",
    "loempiavouwer",
    "spanjool",
    "spleetoog",
    "tatta",
    "tokkie",
    "zandneger",
    "zwartzak",
    "halvezool",
    "kenau",
    "klootviool",
    "knuppel",
    "koekert",
    "koekwaus",
    "oelewapper",
    "smeerlap",
    "sukkel",
    "sul",
    "wappie",
    "wijf",
    "zooi",
    # xxx (a.o. https://gitlab.com/yhavinga/c4nlpreproc/-/blob/master/clean/badwords_ennl.py?ref_type=heads)
    "xxx",
    "anal",
    "blowjob",
    "buttplug",
    "cock",
    "cunt",
    "geil",
    "sex",  # Standaardnederlands = seks, maybe we catch some porn or socialmedia sites with this misspelling
    "porn",
    # extra
    "nigger",
    "nigga",
    "hoerig",
    "klojo",
}

Config details

10k

ratio_wikipedia: 100.00%
total_num_tokens: 10,078
train_num_tokens: 9,957
test_num_tokens: 121
total_num_samples: 38
train_num_samples: 37
test_num_samples: 1

100k

ratio_wikipedia: 100.00%
total_num_tokens: 100,099
train_num_tokens: 99,537
test_num_tokens: 562
total_num_samples: 303
train_num_samples: 300
test_num_samples: 3

1M

ratio_wikipedia: 100.00%
total_num_tokens: 1,000,104
train_num_tokens: 987,432
test_num_tokens: 12,672
total_num_samples: 2,722
train_num_samples: 2,695
test_num_samples: 27

10M

ratio_wikipedia: 100.00%
total_num_tokens: 10,000,692
train_num_tokens: 9,905,387
test_num_tokens: 95,305
total_num_samples: 25,641
train_num_samples: 25,385
test_num_samples: 256

100M

ratio_wikipedia: 100.00%
total_num_tokens: 100,000,049
train_num_tokens: 99,022,731
test_num_tokens: 977,318
total_num_samples: 237,578
train_num_samples: 235,203
test_num_samples: 2,375

1B

ratio_wikipedia: 82.38%
total_num_tokens: 1,000,000,003
train_num_tokens: 990,064,856
test_num_tokens: 9,935,147
total_num_samples: 2,869,233
train_num_samples: 2,840,541
test_num_samples: 28,692

5B

ratio_wikipedia: 35.62%
total_num_tokens: 5,000,000,224
train_num_tokens: 4,974,586,006
test_num_tokens: 25,414,218
total_num_samples: 12,603,939
train_num_samples: 12,539,939
test_num_samples: 64,000

10B

ratio_wikipedia: 26.86%
total_num_tokens: 10,000,000,658
train_num_tokens: 9,973,803,589
test_num_tokens: 26,197,069
total_num_samples: 24,628,921
train_num_samples: 24,564,921
test_num_samples: 64,000

15B

ratio_wikipedia: 23.85%
total_num_tokens: 15,000,001,092
train_num_tokens: 14,973,654,717
test_num_tokens: 26,346,375
total_num_samples: 36,653,903
train_num_samples: 36,589,903
test_num_samples: 64,000

20B

ratio_wikipedia: 22.32%
total_num_tokens: 20,000,000,303
train_num_tokens: 19,973,764,973
test_num_tokens: 26,235,330
total_num_samples: 48,678,883
train_num_samples: 48,614,883
test_num_samples: 64,000

25B

ratio_wikipedia: 21.40%
total_num_tokens: 25,000,000,737
train_num_tokens: 24,973,747,815
test_num_tokens: 26,252,922
total_num_samples: 60,703,865
train_num_samples: 60,639,865
test_num_samples: 64,000

30B

ratio_wikipedia: 20.79%
total_num_tokens: 30,000,000,034
train_num_tokens: 29,973,830,841
test_num_tokens: 26,169,193
total_num_samples: 72,728,846
train_num_samples: 72,664,846
test_num_samples: 64,000

35B

ratio_wikipedia: 20.35%
total_num_tokens: 35,000,000,468
train_num_tokens: 34,973,480,399
test_num_tokens: 26,520,069
total_num_samples: 84,753,828
train_num_samples: 84,689,828
test_num_samples: 64,000