metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - 'no'
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification

Dataset Card Creation Guide

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: N/A
Repository: GitHub
Paper: N/A
Leaderboard: N/A
Point of Contact: -

Dataset Summary

This is a classification dataset created from a subset of the Talk of Norway. This dataset contains text phrases from the political parties Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker, as well as a timestamp. The classification task is to, simply by looking at the text, being able to predict is the speech was done by a representative from Fremskrittspartiet or from SV.

Supported Tasks and Leaderboards

This dataset is meant for classification. Results can for instance be viewed in this article.

Languages

The text in the dataset is in Norwegian.

Dataset Structure

Data Instances

Example of one instance in the dataset.

{'label': 0, 'text': 'Verre er det med slagsmålene .', 'date': '2016-01-15'}

Data Fields

id: index of the example
text: Text of a speech
date: Date (YYYY-MM-DD) the speech was produced
label: Political party the speaker was associated with at the time
- 0 = Fremskrittspartiet
- 1 = Sosialistisk Venstreparti

Data Splits

The dataset is split into a train, validation, and test split with the following sizes:

	Tain	Valid	Test
Number of examples	3600	1200	1200

The dataset is balanced on political party.

Dataset Creation

This dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.

Additional Information

The [Talk of Norway dataset] is also available here.

Licensing Information

This work is licensed under a Creative Commons Attribution 4.0 International License.

Citation Information

The following article can be quoted when referring to this dataset, since it is the first study that are using the dataset for evaluating a language model:

@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 = {Link{"o}ping 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 Bokm{aa}l 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.",
}

Datasets:

NbAiLab
/

norwegian_parliament