Datasets:
dataset_info:
- config_name: '1640'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 254777
num_examples: 3509
download_size: 114173
dataset_size: 254777
- config_name: '1650'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 31314
num_examples: 412
download_size: 15122
dataset_size: 31314
- config_name: '1660'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 56559
num_examples: 726
download_size: 25941
dataset_size: 56559
- config_name: '1670'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15093
num_examples: 188
download_size: 8153
dataset_size: 15093
- config_name: '1680'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1290089
num_examples: 17458
download_size: 609438
dataset_size: 1290089
- config_name: '1690'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2977705
num_examples: 42333
download_size: 1355778
dataset_size: 2977705
- config_name: '1700'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3800917
num_examples: 53331
download_size: 1702603
dataset_size: 3800917
- config_name: '1710'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1601983
num_examples: 22763
download_size: 733219
dataset_size: 1601983
- config_name: '1720'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2268261
num_examples: 32813
download_size: 1012144
dataset_size: 2268261
- config_name: '1730'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5498116
num_examples: 79079
download_size: 2515986
dataset_size: 5498116
- config_name: '1740'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 10147602
num_examples: 149317
download_size: 4572359
dataset_size: 10147602
- config_name: '1750'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14183279
num_examples: 212000
download_size: 6235076
dataset_size: 14183279
- config_name: '1760'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 34039377
num_examples: 545759
download_size: 15159865
dataset_size: 34039377
- config_name: '1770'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 89191958
num_examples: 1333609
download_size: 39582304
dataset_size: 89191958
- config_name: '1780'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 136703541
num_examples: 2015223
download_size: 60960878
dataset_size: 136703541
- config_name: '1790'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 163823087
num_examples: 2435714
download_size: 72860792
dataset_size: 163823087
- config_name: '1800'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 220361417
num_examples: 3368887
download_size: 98935407
dataset_size: 220361417
- config_name: '1810'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 263830012
num_examples: 4205776
download_size: 122219730
dataset_size: 263830012
- config_name: '1820'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 395727486
num_examples: 6265710
download_size: 175240370
dataset_size: 395727486
- config_name: '1830'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 580725783
num_examples: 9355635
download_size: 254403662
dataset_size: 580725783
- config_name: '1840'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 898420001
num_examples: 14051720
download_size: 381018147
dataset_size: 898420001
- config_name: '1850'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1354049159
num_examples: 21187511
download_size: 570228565
dataset_size: 1354049159
- config_name: '1860'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2512543535
num_examples: 39321823
download_size: 1046916115
dataset_size: 2512543535
- config_name: '1870'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3383836222
num_examples: 53045312
download_size: 1399880807
dataset_size: 3383836222
- config_name: '1880'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4501878144
num_examples: 72015436
download_size: 1827179641
dataset_size: 4501878144
- config_name: '1890'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3219902112
num_examples: 52337279
download_size: 1315107645
dataset_size: 3219902112
- config_name: '1900'
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 205822484
num_examples: 3284826
download_size: 84811326
dataset_size: 205822484
- config_name: all
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7999426267
num_examples: 124880138
download_size: 7483375536
dataset_size: 7999426267
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- sv
tags:
- newspapers
- historical
size_categories:
- 1B<n<10B
kubhist2
Dataset Description
- Homepage: https://changeiskey.org
- Repository: https://github.com/ChangeIsKey/kubhist2
- Point of Contact: Simon Hengchen / iguanodon.ai
Dataset Summary
This is a version of the Kubhist 2 dataset originally created, curated and made available by Språkbanken Text (SBX) at the University of Gothenburg (Sweden) under the CC BY 4.0 license. This is a a corpus of OCRed newspapers from Sweden spanning the 1640s to the 1900s. The original data is available with many types of annotation in XML at https://spraakbanken.gu.se/en/resources/kubhist2. A good description of the original data is available in this blog entry by Dana Dannélls: https://spraakbanken.gu.se/blogg/index.php/2019/09/15/the-kubhist-corpus-of-swedish-newspapers/.
If you use this dataset for academic research, cite it using the provided citation information at the bottom of this page.
In a nutshell, this hugginface dataset version offers:
- only the OCRed text
- available in decadal subsets
- one line per sentence, sentences shorter than 4 words were discarded
In total this dataset contains 2,819,065,590 tokens. A distribution of tokens per decade is available below.
License is CC BY 4.0 ShareAlike.
(env) simon@terminus:/mnt/user/cik/kubhist2 wc -w text/*/*.txt
39348 text/1640/1640.txt
4700 text/1650/1650.txt
8524 text/1660/1660.txt
2396 text/1670/1670.txt
199670 text/1680/1680.txt
487943 text/1690/1690.txt
619884 text/1700/1700.txt
265930 text/1710/1710.txt
355759 text/1720/1720.txt
856218 text/1730/1730.txt
1589508 text/1740/1740.txt
2211316 text/1750/1750.txt
5496545 text/1760/1760.txt
14434932 text/1770/1770.txt
22366170 text/1780/1780.txt
26768856 text/1790/1790.txt
36225842 text/1800/1800.txt
44510588 text/1810/1810.txt
65571094 text/1820/1820.txt
95359730 text/1830/1830.txt
143992956 text/1840/1840.txt
214538699 text/1850/1850.txt
392672066 text/1860/1860.txt
524802728 text/1870/1870.txt
695859650 text/1880/1880.txt
498244203 text/1890/1890.txt
31580335 text/1900/1900.txt
2819065590 total
Languages
Swedish (nysvenska)
Dataset Structure
One feature: text
.
Load the whole corpus using
dataset = load_dataset("ChangeIsKey/kubhist2")
or a decadal subset using
dataset = load_dataset("ChangeIsKey/kubhist2", "decade")
The decade
must be a string, valid values are within range(1640, 1910, 10)
.
You can combine several decades using concatenate_datasets
like this:
from datasets import load_dataset, concatenate_datasets
ds_1800 = load_dataset("ChangeIsKey/kubhist2", "1800")
ds_1810 = load_dataset("ChangeIsKey/kubhist2", "1810")
ds_1820 = load_dataset("ChangeIsKey/kubhist2", "1820")
ds_1800_1820 = concatenate_datasets([
ds_1800["train"],
ds_1810["train"],
ds_1820["train"]
])
Data Splits
The dataset has only one split, train
.
Dataset Creation
Curation Rationale
The original data is in a highly-annotated XML format not ideally suited for basic NLP tasks such as unsupervised language modeling: information such as page numbers, fonts, etc. is less relevant and has thus been discarded. Keeping only the running text of the newspaper and removing sentences shorter than 4 words further allows a 150x data size reduction (2.4TB --> 16GB).
Source Data
The original data is available with many types of annotation in XML at https://spraakbanken.gu.se/en/resources/kubhist2.
Initial Data Collection and Normalization
See on Språkbanken Text's website.
Who are the source language producers?
Språkbanken Text: https://spraakbanken.gu.se/en/
Personal and Sensitive Information
This is historical newspaper data, with the latest data published in 1909. Everyone mentioned in this dataset was probably already a public figure, and has been dead for a while.
Considerations for Using the Data
Discussion of Biases
This is historical data. As such, outdated views might be present in the data.
Other Known Limitations
The data comes from an OCR process. The text is thus not perfect, especially so in the earlier decades.
Additional Information
Dataset Curators
This huggingface version of the data has been created by Simon Hengchen.
Licensing Information
Creative Commons Attribution Share Alike 4.0: https://creativecommons.org/licenses/by-sa/4.0/
Citation Information
You should always cite the original kubhist2 release, provided below as bibtex. If you want to additionally refer to this specific version, please also add a link to the huggingface page: https://huggingface.co/datasets/ChangeIsKey/kubhist2.
@misc{Kubhist2,
title = {The Kubhist Corpus, v2},
url = {https://spraakbanken.gu.se/korp/?mode=kubhist},
author = {Spr{\aa}kbanken},
year = {Downloaded in 2019},
organization = {Department of Swedish, University of Gothenburg}
}
Acknowledgments
This dataset has been created in the context of the ChangeIsKey! project funded by Riksbankens Jubileumsfond under reference number M21-0021, Change is Key! program. The compute dedicated to the creation of the dataset has been provided by iguanodon.ai.
Many thanks got to Språkbanken Text for creating and curating this resource.