---
language:
- en
dataset_info:
- config_name: 100M
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 650667681.8337438
    num_examples: 227211
  - name: validation
    num_bytes: 2863715.5852214186
    num_examples: 1000
  download_size: 778394816
  dataset_size: 653531397.4189652
- config_name: 10M
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 62042398.153822035
    num_examples: 21665
  - name: validation
    num_bytes: 2863715.5852214186
    num_examples: 1000
  download_size: 80814159
  dataset_size: 64906113.73904346
configs:
- config_name: 100M
  data_files:
  - split: train
    path: 100M/train-*
  - split: validation
    path: 100M/validation-*
- config_name: 10M
  data_files:
  - split: train
    path: 10M/train-*
  - split: validation
    path: 10M/validation-*
---
This repository contains random subsets of the English wikipedia obtained from 
[`"wikimedia/wikipedia"`](https://huggingface.co/datasets/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:
```python
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, mainly stripping away some sections of the articles like "References", "See also", *etc.*, using this script.