Datasets:
pretty_name: Government (nno)
language:
- nno
license: other
license_name: NLOD 2.0
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
domains:
- Report
Dataset Card for NCC Government (nno)
Govermental reports written on Norwegian Nynorsk
This dataset is derived from the Norwegian Colossal Corpus (NCC) and includes almost all reports, propositions, and notes created by the Norwegian government from 1995 to 2021. The documents are available in a native XHTML format and can be downloaded directly from the government’s official API. The internal format allows the fine-grained control of the output, which enables the filtering of headings, footers, tables, and other HTML elements, resulting in a high-quality corpus with very few errors.
Dataset Description
- Number of samples: 1.09K
- Number of tokens (Llama 3): 42.14M
- Average document length in tokens (min, max): 38.63K (75, 344.29K)
Dataset Structure
An example from the dataset looks as follows.
{
"id": "government-nno-0",
"text": "Regjeringa meiner det er viktig at innbyggjarane medverkar i kommunale avgjerdsprosessar som gjeld u[...]",
"source": "government-nno",
"added": "2026-02-02",
"created": "2021-01-01, 2021-12-31",
"token_count": 31774
}
Data Fields
An entry in the dataset consists of the following fields:
id(str): A unique identifier for each document.text(str): The content of the document.source(str): The source of the document (see Source Data).added(str): The date when the document was added to this collection.created(str): The date range when the document was originally created.token_count(int): The number of tokens in the sample computed using the Llama 3 tokenizer.
Dataset Statistics
Annotation Overview
Each document comes with annotations describing its content, such as content quality, information density, and educational value. Each bar shows the share of documents at each level of one annotation, from worst (light) to best (dark). To learn more about the annotations and how to use them, see the annotations section of the main readme.
Additional Information
License Information
This dataset is licensed under NLOD 2.0. This license is derived from the original publication, which is published by the National Library of Norway.
Filtering
This subset is the result of the following filtering from all available data splits on the NCC. It includes the following documents:
- Documents, which are tagged as a part of the collection (
government_nb) - Document which consist of at least 10 tokens (Llama)
- Documents which are not duplicated
Too see how many documents where filtered at each step see the log
Citation Information
If you use this source please cite the following articles:
@inproceedings{kummervold-etal-2022-norwegian-colossal,
title = {The {N}orwegian colossal corpus: A text corpus for training large {N}orwegian language models},
author = {Kummervold, Per E and
Wetjen, Freddy and
De la Rosa, Javier},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://aclanthology.org/2022.lrec-1.410},
pages = {3852--3860},
abstract = {Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.},
}
@inproceedings{kummervold-etal-2021-operationalizing,
title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
author = {Kummervold, Per E and
De la Rosa, Javier and
Wetjen, Freddy and
Brygfjeld, Svein Arne},
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
year = {2021},
address = {Reykjavik, Iceland (Online)},
publisher = {Linköping University Electronic Press, Sweden},
url = {https://aclanthology.org/2021.nodalida-main.3},
pages = {20--29},
abstract = {In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other
languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.},
}