Datasets:
name string | deterministic bool | sources dict | total_docs int64 | english_share float64 | doc_counts dict | vocab_ladder list | rungs list | recipe string |
|---|---|---|---|---|---|---|---|---|
cwt-multilingual-pretrain-mix | true | {
"english": {
"repo": "HuggingFaceFW/fineweb",
"config": "sample-10BT",
"revision": "9bb295ddab0e05d785b879661af7260fed5140fc"
},
"non_english": {
"repo": "HuggingFaceFW/fineweb-2",
"languages": [
"arb_Arab",
"ben_Beng",
"ces_Latn",
"cmn_Hani",
"dan_Latn",
... | 200,000 | 0.4 | {
"eng(FineWeb)": 80000,
"arb_Arab": 4285,
"ben_Beng": 4285,
"ces_Latn": 4285,
"cmn_Hani": 4285,
"dan_Latn": 4285,
"deu_Latn": 4285,
"ell_Grek": 4285,
"fas_Arab": 4285,
"fin_Latn": 4285,
"fra_Latn": 4285,
"heb_Hebr": 4285,
"hin_Deva": 4285,
"hun_Latn": 4285,
"ind_Latn": 4285,
"ita_Latn":... | [
32000,
64000,
128000,
256000
] | [
{
"vocab_size": 32000,
"occupancy": 0.997125,
"dead_tokens": 92,
"fertility": 2.536954538289989,
"n_words": 7289673,
"n_subwords": 18493569,
"fertility_per_lang": {
"eng": 1.5121450205415778,
"arb_Arab": 2.1540646123189187,
"ben_Beng": 4.688690933703495,
"ces_Latn... | first-N docs of each pinned-revision stream; no sampling/RNG → byte-identical rebuild |
CWT Multilingual Pretrain Mix (reproducible recipe)
A deterministic multilingual-including-English pretraining corpus for controlled vocabulary-scaling studies.
The raw text is not stored here — it is reproduced byte-identically from manifest.json + the pinned
dataset revisions, with no sampling and no RNG (the first-N documents of each stream).
Recipe
- English:
HuggingFaceFW/finewebconfigsample-10BT, revision9bb295ddab0e05d785b879661af7260fed5140fc - Non-English:
HuggingFaceFW/fineweb-2, revisionaf9c13333eb981300149d5ca60a8e9d659b276b9 - 28 non-English languages: arb_Arab, ben_Beng, ces_Latn, cmn_Hani, dan_Latn, deu_Latn, ell_Grek, fas_Arab, fin_Latn, fra_Latn, heb_Hebr, hin_Deva, hun_Latn, ind_Latn, ita_Latn, jpn_Jpan, kor_Hang, nld_Latn, pol_Latn, por_Latn, ron_Latn, rus_Cyrl, spa_Latn, swe_Latn, tha_Thai, tur_Latn, ukr_Cyrl, vie_Latn
- English share 40%; total 200,000 docs (first-N per language; deterministic).
Reproduce byte-identically
pip install datasets
python rebuild_dataset.py --manifest manifest.json --out corpus.jsonl # [--total_docs N]
Tokenizers trained on this mix: DaveGabe/cwt-multilingual-tokenizers.
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