--- dataset_info: - config_name: 100M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 368158357.95775706 num_examples: 235203 - name: test num_bytes: 3717538.0422429685 num_examples: 2375 download_size: 224184711 dataset_size: 371875896 - config_name: 100k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 365453.4653465347 num_examples: 300 - name: test num_bytes: 3654.5346534653463 num_examples: 3 download_size: 212072 dataset_size: 369108 - config_name: 10B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 39904320962.76584 num_examples: 24564921 - name: test num_bytes: 103964370.23416495 num_examples: 64000 download_size: 25249998174 dataset_size: 40008285333 - config_name: 10M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 37059125.991965994 num_examples: 25385 - name: test num_bytes: 373730.00803400803 num_examples: 256 download_size: 22486785 dataset_size: 37432856 - config_name: 10k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 37658.21052631579 num_examples: 37 - name: test num_bytes: 472 num_examples: 1 download_size: 30893 dataset_size: 38130.21052631579 - config_name: 15B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 60014708510.13879 num_examples: 36589903 - name: test num_bytes: 104972711.86121707 num_examples: 64000 download_size: 37966833792 dataset_size: 60119681222 - config_name: 1B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 3805376695.1198378 num_examples: 2840541 - name: test num_bytes: 38437701.880162396 num_examples: 28692 download_size: 2346974411 dataset_size: 3843814397 - config_name: 1M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 3695065.7880235123 num_examples: 2695 - name: test num_bytes: 37019.21197648787 num_examples: 27 download_size: 2183019 dataset_size: 3732085 - config_name: 20B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 80125589478.94254 num_examples: 48614883 - name: test num_bytes: 105482877.0574707 num_examples: 64000 download_size: 50682523292 dataset_size: 80231072356 - config_name: 25B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 100236677321.01715 num_examples: 60639865 - name: test num_bytes: 105790923.98284689 num_examples: 64000 download_size: 63397565382 dataset_size: 100342468245 - config_name: 30B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 120347862572.46747 num_examples: 72664846 - name: test num_bytes: 105997103.53253783 num_examples: 64000 download_size: 76111936677 dataset_size: 120453859676 - config_name: 5B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 19795857463.09181 num_examples: 12539939 - name: test num_bytes: 101031980.90819068 num_examples: 64000 download_size: 12526141470 dataset_size: 19896889444 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](https://huggingface.co/datasets/uonlp/CulturaX) and [Wikipedia](https://huggingface.co/datasets/wikimedia/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 ```python 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