metadata
language:
- en
dataset_info:
- config_name: 100M_1
features:
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dtype: string
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- name: title
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- config_name: 100M_3
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- config_name: 10M_1
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- name: url
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- name: title
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configs:
- config_name: 100M_1
data_files:
- split: train
path: 100M_1/train-*
- split: validation
path: 100M_1/validation-*
- config_name: 100M_2
data_files:
- split: train
path: 100M_2/train-*
- split: validation
path: 100M_2/validation-*
- config_name: 100M_3
data_files:
- split: train
path: 100M_3/train-*
- split: validation
path: 100M_3/validation-*
- config_name: 10M_1
data_files:
- split: train
path: 10M_1/train-*
- split: validation
path: 10M_1/validation-*
- config_name: 10M_2
data_files:
- split: train
path: 10M_2/train-*
- split: validation
path: 10M_2/validation-*
- config_name: 10M_3
data_files:
- split: train
path: 10M_3/train-*
- split: validation
path: 10M_3/validation-*
- config_name: all
data_files:
- split: train
path: all/train-*
This repository contains random subsets of the English wikipedia obtained from
"wikimedia/wikipedia" ("20231101.en").
It includes two random subsets of the English wikipedia, one containing roughly 10M words total (23k articles), the other containing roughly 100M words total (228K articles).
These data are intended to be used for the BabyLM challenge. For convenience, the repository also includes the full English wikipedia containing roughly 2.8B words total
(6.4M articles).
You can load these datasets as follows:
from datasets import load_dataset
ds_10M = load_dataset("eminorhan/wikipedia", "10M") # 10M word subset
ds_100M = load_dataset("eminorhan/wikipedia", "100M") # 100M word subset
ds_all = load_dataset("eminorhan/wikipedia", "all") # the full data (2.8B words)
Both subsets come with train/validation splits, whereas the full data only has a train split.
We applied lightweight preprocessing to the article texts using this script,
which mainly strips away some sections of the articles like "References", "See also", etc.